BLUF: Most organizations aren’t losing the data game because they lack data. They’re losing because nobody agrees on what the data means. A semantic data layer is the organizational and technical infrastructure that fixes that. The hard part isn’t building it, but treating data like a product with an owner, a roadmap, and a seat at the leadership table.

If data is the new oil, the oldest legacy enterprises in business should be poised for a whole new wave of investment. But they’re not. And just like oil trapped in the Canadian oil sands, the data is useless until someone figures out how to make it usable.

In most organizations, the pattern is almost predictable. Someone from finance pulls a customer count. Someone from sales pulls a different one. Someone from the data team pulls a third. All three numbers are technically correct. All three are sourced from systems in the same company that someone paid millions to build and maintain. And yet the executive sitting at the head of the table has to choose which one to believe.

That moment of paralysis is not a technology failure, and it isn’t that the people gathering the data don’t know what they’re doing. The problem is systemic. And until organizations treat it as such, no amount of investment in AI, business intelligence, or data modernization will fix it.

Several years ago I was working with a large, established insurance company to develop a strategy for the future. The executive sponsoring the engagement was certain that future profitability would come not from selling insurance and carefully managing a fund, but from their data. I dropped the South Park Underpants Gnome analogy. He didn’t get it.

I tried again: just as stealing underpants isn’t a path to profit, neither is reducing investment in your core products because you’ve decided that data equals revenue. The company was a trusted insurance provider, not a data broker. Their data could make their core business sharper, faster, and more competitive, but only if they could actually use it. That was the part nobody had thought through.

When we dug in, the picture was familiar. They had a lot of data. They also had no coherent way to work with it. It was siloed across systems that didn’t communicate, defined differently by every team that touched it, and governed by nobody in particular. The strategic conversation they thought they were having, about monetization and future revenue, turned out to be a much more foundational one: we shifted to exploring what their data meant, and whether anyone in the organization agreed.

Realize it or not, most organizations today are in the midst of an underpants gnome conundrum of their own.

Over the last several years, across industries and geographies, I’ve seen organizations struggle with what to do with their data and how to make it accessible. The symptom changes. The root cause rarely does.

Somewhere in the organization’s history, someone decided that shared meaning was a problem each team could solve independently. This is a risk I flagged often when organizations pursued aggressive team autonomy models. The larger and more complex an organization’s business, the less likely any team will ever operate with true independence: the system is simply too big. For a while, the methodological purists proved louder than that concern. Organizations pursued the belief that they could succeed as a collection of independent teams, with coordination and planning recast as overhead rather than value. And the consequences of that decision stayed invisible because the cost of misalignment was low enough to absorb. Reports had footnotes. Analysts reconciled manually. Leaders learned which number to cite in which room.

The cost is no longer low enough to absorb. And the reason why is worth understanding before we talk about the solution.

What We Built Before

The concept of a semantic data layer isn’t new. The problem it solves has been with us since organizations started running on more than one system of record. What has changed is how consequential the absence of one has become.

Before semantic layers became a defined category, organizations managed the meaning problem in one of two ways. Both worked well enough for long enough that the problems they created didn’t become visible until the data environment got large enough and interconnected enough to make them unavoidable.

The first approach was ETL-layer logic. Extract, transform, load pipelines became the de facto home for business definitions. If the finance team needed “active customer” to mean something specific, that logic got baked into the pipeline that fed their reporting database. If the sales team needed a slightly different definition for their dashboard, a different pipeline got built with slightly different logic. Over time, organizations accumulated dozens, sometimes hundreds, of pipelines each carrying their own embedded definitions. Nobody documented the differences systematically. Nobody governed them. The pipelines kept running, the definitions kept drifting, and the organization kept producing reports that didn’t agree with each other without anyone being able to explain exactly why.

The second approach was Master Data Management, or MDM. MDM was the enterprise’s formal attempt to solve the meaning problem before semantic layers existed as a concept. It was ambitious: a centralized system of record for the entities that mattered most, customers, products, suppliers, locations, with governance processes to keep definitions clean. In the right conditions, MDM worked. In most enterprise conditions, it didn’t. The implementations were expensive, the governance overhead was heavy, and the business participation required to keep master data current was difficult to sustain. MDM projects became known for taking years and delivering less than promised. Many organizations still carry the scars of a failed MDM implementation, which makes the conversation about semantic layers politically complicated before it even starts.

What both approaches shared was a fundamental design assumption: that the meaning problem was a data problem, solvable by data teams, and that the business’s job was to consume the results rather than participate in defining them. That assumption is where both approaches broke down.

A semantic data layer, properly implemented, rejects that assumption. The technology part is straightforward. The harder part, getting the business to co-own the definitions, is where most organizations struggle and where most of the real work lives.

What the Layer Does

The term sounds like something a data architect invented to win a budget conversation: confuse the accountant with big words.

A semantic data layer is a shared, governed definition of what your data means. Not what it technically contains. What it means. The difference matters more than most technology leaders realize.

Your CRM calls them clients. Your ERP calls them accounts. Your data warehouse references them by a party identifier that means nothing to any human being who hasn’t memorized a data dictionary. When your AI model, your BI dashboard, or your executive report pulls data, it is drawing from at least three different definitions of the same concept. The downstream result is what you saw in that meeting: three numbers, zero trust.

A semantic data layer sits between your source systems and their consumers. It translates technical data structures into business-meaningful definitions, applies consistent business logic, and ensures that when your analytics tool says “active customer,” it means the same thing whether the query hits your CRM, your data warehouse, or your operational database.

The diagram above illustrates the vocabulary problem. More pipelines won’t fix it. Faster pipelines won’t fix it. A shared dictionary that the systems are required to honor fixes it, and that is what a semantic layer does.

Two Problems, Not One

It is worth being precise about what a semantic layer covers, because two related problems often get conflated in the same conversation.

The first is entity definition: what is a “customer,” a “product,” a “location”? These are the foundational objects your business runs on. When systems use different names or slightly different scopes for the same thing, every report built on top of them inherits the disagreement. This is a structural problem, and it sits underneath everything else.

The second is metric governance: what is “revenue,” “active user,” or “churn rate”? These are calculations, and they can produce different numbers even when the underlying entity definitions agree, because the business logic embedded in the calculation differs by team or tool. Finance calculates monthly recurring revenue one way. Sales calculates it another. Both are pulling from the same source system and still arriving at different answers.

A semantic layer addresses both. But they are not the same problem, and they don’t respond to the same fix. Entity definitions are foundational. Metric governance is built on top of them. The sequence matters: get the entities right first, then govern the metrics built from them. Organizations that skip straight to metric governance without resolving the entity layer underneath tend to find that their governed metrics are consistent but still wrong, because the objects they’re measuring were never properly defined to begin with.

The practical implication is worth stating directly. The “start with ten metrics” advice that appears later in this post is a metric governance move. It is the right place to start building organizational credibility and executive buy-in. But it won’t automatically resolve the entity definition problem underneath. Both need attention, and knowing which one you’re working on at any given moment is the difference between a semantic investment that compounds and one that plateaus.

The Urgency

For years, the absence of a semantic layer was a nuisance organizations could manage. Reports required footnotes. Analysts reconciled manually. Leaders learned which number to cite in which room. The tax was real, but it was distributed and largely invisible.

Generative AI has transformed that manageable nuisance into a structural liability, and it is doing so faster than most organizations realize.

The investment thesis behind most enterprise AI initiatives assumes that connecting capable models to rich data produces reliable insight. That assumption depends entirely on the data being consistent and trustworthy. When it isn’t, when “customer” means three different things in three different systems and none of those systems is authoritative, the model does not throw an error. It synthesizes. It finds a plausible answer from the available inputs and delivers it in whatever format you asked for, with no indication that the foundation it reasoned from was broken.

This is not a model problem. The models are performing as designed. The problem is that the data infrastructure most large organizations have built over the last two decades was not designed with shared meaning as a requirement, because the cost of inconsistency was low enough to absorb before AI made that cost visible.

The risk compounds further as organizations move toward agentic AI. A confused reasoning system that produces a wrong answer is a problem you can catch in review. A confused agent that takes a wrong action, updating a customer record, routing a transaction, triggering a downstream workflow, based on a semantically broken input is a different category of problem entirely. The stakes of semantic inconsistency go up significantly when AI moves from answering questions to taking actions.

The flip side is worth stating plainly. Organizations that do establish semantic clarity before connecting AI to their data get something their competitors don’t: a model that reasons from a shared, trustworthy foundation. That means more reliable answers, faster time to insight, and AI outputs that executives are willing to act on rather than second-guess. Semantic clarity doesn’t just reduce AI risk. It is what makes AI genuinely useful at the enterprise level.

I’ve seen this shift accelerate in a specific way. Engagements that a few years ago started with “we need better reporting” now start with “we’re deploying AI and our outputs aren’t reliable.” When the conversation reaches that point, semantic inconsistency is almost always part of the diagnosis. The organizations moving fastest to fix it aren’t doing so because they read about semantic layers. They’re doing so because a failed AI initiative made the cost of inconsistency impossible to ignore.

Organizations that invest in AI capability before establishing semantic clarity are not getting ahead of the problem. They are getting behind it faster.

The Gap Is Costing You

The cost of semantic inconsistency tends to be invisible on any individual line of the budget. Analysts spend time reconciling instead of analyzing, governance programs stall without shared definitions to govern against, and the cost compounds quietly across every team that touches data.

The most visible damage shows up at the top. The three-numbers problem doesn’t just create a moment of awkwardness in an executive meeting, it creates a pattern of hesitation that slows everything down. Leaders who have learned not to trust their data ask more questions before committing. They wait for reconciliation. They hedge. That caution is rational given the environment they’re operating in, and it is expensive.

The Failure Modes

The absence of a semantic layer creates predictable, consistent problems across organizations regardless of industry, size, or technical maturity.

Definitions get negotiated in spreadsheets. In the absence of a governed layer, individual teams create their own definitions and encode them in Excel files, BI reports, and tribal knowledge. Those definitions drift over time, diverge across teams, and become almost impossible to reconcile systematically because they were never managed as organizational assets. They were managed as individual workarounds.

New systems inherit old ambiguity. When organizations implement a new ERP, a new CRM, or a new data platform, they almost always bring the definitional inconsistency forward with them. The migration project focuses on moving data. Nobody has a mandate to resolve what the data means. The new system goes live with the same meaning problem the old system had, plus new terminology to argue about.

The data team becomes the scapegoat. When reports disagree, someone has to take the blame, and it almost always lands on the data engineering team, because they built the pipelines the reports run on. In most cases, the data team is not actually at fault. They implemented what was asked of them, often by multiple business stakeholders with conflicting requirements. The fault is organizational: no one with sufficient authority held the business accountable for providing consistent definitions before the technical work was done. The data team absorbs the blame for a governance failure that sat above them.

BI tools multiply the problem rather than solving it. Organizations that invest in self-service analytics hoping to broaden data access sometimes find that they have instead broadened data inconsistency. When every analyst can build their own reports, and every report can embed its own business logic, the number of competing definitions of any given metric grows with the number of analysts. The semantic layer problem doesn’t get easier as BI tooling gets more accessible. It gets harder.

AI answers the wrong question fluently. A language model connected to semantically inconsistent data doesn’t know it is working from inconsistent inputs. It synthesizes what it finds and presents it with the confidence of a well-trained communicator. The result is answers that sound authoritative and are frequently wrong in ways that are difficult to detect without already knowing the answer. This is the failure mode that is moving the semantic layer conversation from data team priority to C-suite priority faster than most other forces in enterprise technology right now.

The Infrastructure Trap

Most organizations that do invest in a semantic layer make a consistent mistake: they treat it as infrastructure rather than as a product. The distinction is not a small one. It has concrete operational consequences.

Infrastructure gets built once. It gets documented once. It gets maintained reactively, when something breaks. Nobody owns it in a meaningful way. Nobody measures whether it’s working. The business never really asked for it, which means the business has no stake in keeping it current.

A semantic layer treated as infrastructure decays. Business definitions change. New source systems get added. Organizational mergers bring in entirely new vocabularies. Nobody updates the layer because nobody’s job it is to update it. Within eighteen months, the layer that was supposed to create shared meaning has become another source of inconsistency, only now with the additional problem of looking authoritative while being wrong.

The difference between the two approaches is illustrated below.

The organizations getting real value from their semantic investments treat the layer as a product. That means it has owners. It has a roadmap. It has adoption metrics. Business stakeholders are part of the governance process, not passive recipients of definitions handed down from IT. The definitions evolve as the business evolves, through a deliberate change process rather than through drift.

Treating semantic data as a product is a governance decision as much as a technology decision. It requires someone with organizational authority to hold both the technical and business sides of the conversation accountable to the same vocabulary.

Getting It Right

The enterprises I have seen navigate this successfully share a consistent set of moves. None of them are complicated in concept. All of them are hard in practice.

Put a senior executive in charge of data strategy, not just data operations. This is the move that separates organizations that resolve the semantic problem from the ones that keep managing around it. The definitional disagreements that prevent a semantic layer from taking hold, what counts as a closed deal, what makes a customer active, how revenue is calculated across business units, are not questions a data engineering team can answer. They require someone with organizational authority to convene the right business stakeholders, surface the disagreements, and make binding decisions.

In practice, this means a Chief Data Officer, or a senior executive with equivalent authority, who holds both the business and technology sides of the semantic conversation accountable. Not a data steward committee with no enforcement mechanism. Not a center of excellence that produces recommendations business units are free to ignore. An executive with a mandate to establish and maintain shared definitions, the organizational leverage to hold business units accountable to those definitions, and a reporting line that makes data strategy a C-suite concern rather than a back-office function.

The organizations that have gotten this right have typically made this move before the AI conversation started, which is one of the reasons their AI initiatives are performing better than their peers. The semantic foundation was established because someone with authority decided that shared meaning was a strategic requirement, not an IT project. The AI capability was layered on top of a foundation that could support it.

Start with the entities your systems disagree about most, then move to the metrics your leadership team argues about most. Get the foundational objects right first: customer, product, location, account. Then define, formally, in writing, with sign-off, the business concepts that generate the most disagreement when they appear in executive reports. Revenue. Active user. Churn. Margin by product line. Both layers of work matter. Neither substitutes for the other. Getting them sequenced correctly is what makes the investment compound rather than stall.

Treat the semantic layer’s health as a board-level metric. The organizations that sustain this over time measure it the way they measure any product: adoption rates across business units, query volume against governed definitions versus ad hoc queries, time-to-answer for common business questions, and whether executives actually believe the numbers they’re seeing. These aren’t vanity metrics. They are leading indicators of whether the investment is growing stronger or quietly falling apart.

The Takeaway

The technology leaders positioned well on the other side of this problem aren’t the ones who bought the best semantic layer tooling. They’re the ones who recognized early that the meaning problem is an organizational problem, and made the leadership investments required to solve it at that level.

That work is not glamorous. It involves sitting in rooms where executives from different business units disagree about the definition of their most important metrics, and holding the line that the organization needs a single answer rather than a convenient plurality of them. It involves explaining to a board why a data governance initiative is a strategic investment rather than a cost center. It involves building the organizational infrastructure that makes AI initiatives reliable, which is less visible than the AI initiatives themselves but more important to whether they actually succeed.

The leaders who look back on this period with satisfaction won’t be the ones who deployed the most AI pilots or adopted the most capable models. They’ll be the ones who asked the harder question first: does our organization have a shared language for what our data means? And then built the organizational infrastructure required to answer it.

That infrastructure starts with someone in the room who has the authority and the will to say: we are not leaving until we agree on what a customer is.

We have spent the last three years obsessing over how much AI can produce — and it has delivered. Faster code. More content. Product cycles compressed from weeks into hours. We built the tools, staffed the pilots, and celebrated the throughput. Frankly, I’m tired of it. Because now I’m watching some of the most capable enterprises I know quietly fracture under the weight of what they built. Not because the tools failed. Because they worked too well.

This is the problem nobody in the AI conversation wants to say out loud: we have solved for production capacity and ignored absorption capacity entirely. We have handed organizations a firehose and called it progress. The organizations that cannot answer the question of how much change their customers and employees can actually absorb at any given moment are building a structural problem that will cost them more than any transformation they have ever attempted.

The constraint of the agentic AI era is not what you can produce. It is what your market, your customers, and your own organization can receive.

Reruns Abound

There is a category of financial services company that most people recognize, even if I cannot name it. A large institution with a strong brand and deep customer trust built over decades. And somewhere around 2017 or 2018, a mandate from above: we are going to be the everything provider. Insurance, banking, investing, mortgages, auto, home, auto buying, home buying. We are going to be the best at all of it simultaneously.

The teams delivered. They shipped what was asked. New products came to market at a pace the organization had never seen.

The customers were lost.

There were so many products, and the brand was evolving too fast, in too many directions at once. Customers who had trusted this institution for decades, who had chosen it precisely because of its clarity and reliability, started experiencing something they could not quite name. Confusion. Friction. A vague sense that the organization had stopped knowing what it was there for. Core product quality slipped, not dramatically. Compliance slipped quite dramatically. In financial services, product is one thing, but compliance is everything.

The institution had optimized for production and lost the trust of the people that made it great. No mechanism existed to ask how much change customers could metabolize. No framework to sequence change against readiness. No visibility into the cumulative burden landing on the people they were ultimately building for.

This is what happens when production velocity outruns absorption capacity. Agentic AI is about to supercharge that exact dynamic.

The institution had optimized for production. It had not built any instrumentation for absorption. And there was no mechanism to ask the one question that mattered: how much change can our customers actually receive right now?

The Agentic Multiplier

Agentic AI refers to systems that pursue goals autonomously — not tools that respond to prompts, but systems that plan, act, and iterate across multiple steps without a human in the loop at each one. That distinction matters more than most organizations currently appreciate.

When I talk to executives and product people about agentic AI, the conversation almost always centers on capability and throughput. What can the agents do? How much can they automate? What is the ROI model? These are legitimate questions, and the answers can be genuinely impressive.

What almost never makes it into the conversation: agentic systems do not just produce more, they produce more continuously, and they do it across every surface simultaneously.

A single well-configured agentic system can touch customer communications, product configuration, internal documentation, workflow sequencing, and reporting in a single cycle. It can deploy a change to your customer experience, your employee experience, and your operational layer without any human reviewing whether the cumulative effect is something the receiving end can absorb.

This is new. The digital transformation era had a quarterly release cycle — a natural throttle. The Agile era had iteration cadences — natural checkpoints. Agentic AI removes the friction that previously forced organizations to pace themselves. For most enterprises, that friction was not waste. It was load-bearing.

Gartner predicts that by 2028, at least 15% of day-to-day work decisions will be made autonomously through agentic AI, up from virtually zero in 2024, and that 33% of enterprise software applications will embed agentic capability by the same timeframe.[1] The throughput is undeniable. The governance infrastructure to match it is almost universally nonexistent.

The Velocity Gap

Production is climbing. Absorption is not keeping pace. That is the gap, and most organizations are not measuring it because they have never had to. The natural brakes of the previous era gave the system time to absorb what was being deployed. Those brakes are gone. What replaced them is a question most enterprises have not yet asked — until the returns fail to materialize.

A BCG study from 2025 found that only 5% of companies have achieved AI value at scale, while 60% report no material returns despite substantial investment.[2] That is not a technology problem. The tools are working. The gap is the organizational capability to land change well — to put it somewhere it actually takes hold, in the hands of people who are ready to use it, in systems stable enough to carry it.

Change Saturation

Market and customer saturation from change overload follows a recognizable pattern. You can feel when you’re overburdened with change, and you can see it in your customers. It looks like resistance and disengagement.

Most organizations misread those signals when they appear. They diagnose a communication problem, a training gap, a product issue. The response drives more activity, which compounds the original problem. By the time the real cause surfaces, the organization is already in stage three of a pattern that was entirely visible from stage one — to anyone who was looking at the right thing.

The Three-Stage Saturation Pattern

First, engagement metrics soften. Not collapse. Soften. Customers interact a little less. Support tickets start referencing confusion about recent changes. Employees ask a few more questions about why things work differently now, and long-tenured people quietly stop volunteering ideas. These signals get attributed to execution problems, communication gaps, or training deficits. The real cause, cumulative change burden exceeding absorption capacity, never makes it into the diagnosis.

Second, the organization accelerates in response. Because the metrics softened, there is pressure to ship the next thing. Surely the next feature, the next product, the next release will turn the curve. The volume of change goes up in direct response to the symptoms of too much change. This is the pattern that turns a manageable problem into a structural one.

Third, trust erodes in ways that take years to rebuild. The customer who once gave you benefit of the doubt stops extending it. The employee who once championed the new way of working starts keeping her head down. The institutional confidence built over a decade gets spent in eighteen months of overdeployment.

By the time the pattern is visible to leadership, the cost is already sunk. The organizations that hit this wall with AI velocity in the system will hit it faster and harder than anything the digital transformation era produced.

The organizations that will win this era are not the fastest deployers of agentic capability. They are the ones that build, alongside that capability, a rigorous and continuous answer to one question: what is landing on our customers and our people right now, and what is their remaining capacity to receive more?

Absorption Capacity

I have sat across the table from a lot of leaders who hear “absorption capacity” and think it is a case for slowing down. It is not. It is a case for knowing what you are doing to your customers before they tell you.

The organizations I have watched get this right share one thing: they know where their customers are. Not in a CRM sense. In a change sense. Which segments are still orienting to the last release. Which employees are running on fumes from the last reorganization. Which products have been touched so many times in twelve months that the people using them have stopped trusting them.

That knowledge shapes sequencing. What goes next. What waits. What needs a stabilization period before the next build lands on top of it.

MIT Sloan Management Review’s 2025 research on the agentic enterprise, conducted with BCG across 2,100 executives in 116 countries, underlines the point: the organizations that thrive focus less on the technology itself and more on the human systems that surround it.[3] Most enterprises have not operationalized that observation. They are still governing AI adoption the way they governed software releases, which was already not working before the velocity got 10x faster.

You cannot manage change absorption from a program dashboard. You need signals from the delivery layer, the product layer, the customer layer, and the organizational layer, running continuously, visible in relation to one another. You need a framework that surfaces the problem before it becomes a crisis, not after it shows up in quarterly metrics.

The Change Radar

Years ago, working through one of the earlier transformation waves, I came across a concept called the change radar. It came from a persistent frustration: organizations were consistently surprised by change that had been visible for months to anyone with the right vantage point. Not unpredictability. The absence of any shared visualization of what was already in flight.

Hard to prepare for what you cannot see, and most change falls into that category. To stop inundating an organization and its customers with more than they can absorb, we need a mechanism to make the invisible visible.

The change radar organizes all change across two axes. The first is origin: who is driving the change? Some of it we are imposing — new products, new platforms, restructured experiences. Some is being imposed on us — regulatory shifts, competitive moves, market pressure. Both consume organizational and customer capacity, and most change management frameworks only account for the first. The second axis is impact: does this change land on our internal organization, our people and processes, or on our external customers and market?

Mapped across those axes, with a horizon model distinguishing between change that is assessing and visioning, change that is preparing, and change that is actively implementing, you get something most executive teams have never seen clearly: the full picture of cumulative change landing on any given audience at any given moment.

Think of it the way air traffic controllers manage a major hub. At any point, hundreds of aircraft are in motion simultaneously. The controller’s job is not to determine whether flying is a good idea. It is to manage sequencing, spacing, and load so everything can land safely. The radar does not slow the planes down. It makes the density visible so the right decisions can be made about what comes next.

That is what the change radar does for enterprise transformation.

Unlike a change advisory board or a program status dashboard, the radar is cumulative, cross-layer, and continuous. It does not ask whether one initiative is ready to launch. It asks what the entire change environment looks like for a given audience at a given moment — including what is being imposed on them from outside the organization.

The value compounds when layered against portfolio and program prioritization. Most portfolio decisions are made against one question: can we build it? The change radar adds the prior question: can our customers and organization receive it right now? If the answer is no, the sequencing decision is not to delay the work. It is to identify what must land first to create the capacity for what comes next.

As is often said: timing is everything. If we land the right change at the wrong time, we risk losing the strategic opportunity it was designed to create.

In the agentic AI era, this is not a governance artifact for large change programs. It is the fundamental discipline for responsible velocity. The agents will produce, continuously, across every surface. The question is whether leadership has the instrumentation to sequence what gets deployed, when, against a real model of customer and organizational readiness.

What Sustains the Advantage

I wrote recently about why the transformation program model keeps failing. The argument was structural: programs are designed to end, and the capability they build dissipates when the program closes. The organizations that break the cycle are the ones that build continuous modernization into their operating model permanently. Not as an initiative. As how they operate.[4]

The change radar is part of that. Not a one-time assessment. A continuous instrumentation layer that sits alongside your portfolio, your delivery system, and your customer feedback loops. It makes the question of change capacity a standing discipline rather than something that surfaces in a crisis.

The organizations I have watched absorb agentic AI capability well share a common characteristic: someone is tracking the cumulative weight of what is landing on customers and people, in real time, with enough visibility to make sequencing decisions before the damage is done. The ones that struggle are not less ambitious. They are less instrumented.

Most organizations are not stuck because they lack the ambition to change. They are stuck because they have never built the muscle that makes change visible before it becomes damage.

The goal is an organization that does not need to be rescued from its own velocity. One that can see its system clearly enough to deploy at the pace that compounds its advantage rather than erodes its foundation.

A Risk of the Agentic Era

The enterprise technology conversation right now is dominated by one concern: falling behind. The risk of being outpaced by competitors who move faster on AI adoption. That risk is real.

The one I am watching is the other side of it. Organizations spending their most valuable asset — customer trust and employee confidence — faster than any productivity gain can replace it. Moving so fast, with so little visibility into the cumulative change burden they are creating, that the damage is already done before anyone sees it coming.

The financial services company I described earlier recovered. Slowly, carefully, by getting serious about what their customers actually needed and rebuilding the clarity they had traded for ambition. It cost them years. They did it in a relatively slow-moving deployment environment, where the damage accumulated over quarters.

With agentic AI in the system, that same pattern plays out over weeks.

The leaders who navigate this era well are the ones who can hold two things simultaneously: speed is a genuine competitive advantage, and the pace of change must be calibrated against the real capacity of the humans on the receiving end.

That calibration requires visibility. Visibility requires instrumentation. And instrumentation requires the kind of deliberate, continuous operating discipline that transformation programs were never designed to build.

The constraint is not what you can produce.

It is what the world can receive.

The organizations that build the discipline to see that clearly, and continuously, are the ones that will still be compounding their advantage when everyone else is in recovery.

References

[1] Gartner, Predicts 2025: Agentic AI — 33% of enterprise software to embed agentic AI by 2028; 15% of daily work decisions made autonomously by 2028. Reported via RCR Wireless, June 2025.

[2] BCG, The Widening AI Value Gap, 2025. Only 5% of companies have achieved AI value at scale; 60% report no material returns despite substantial investment.

[3] MIT Sloan Management Review and BCG, The Emerging Agentic Enterprise: How Leaders Must Navigate a New Age of AI, November 2025. Global survey of 2,102 senior executives across 21 industries and 116 countries.

[4] BCG, Flipping the Odds of Digital Transformation Success, 2020/updated 2024. Roughly 70% of digital transformation initiatives fail to achieve stated objectives.

Well friends, it has been quite the eventful Q1, and I’m excited to be back to my positive, creative, inspired, and goal-driven self! Truth be told, it had been a while.

After leaving my old company at the start of the year, I planned to build a firm of my own. One focused on an amazing team of A-players, a culture of winning, and best-in-class results-oriented solutions, all with a little swagger.

That changed when my buddy Eric told me the company he works for already had all of those things and was launching a consulting arm. They asked if I wanted to help. Not that I don’t love a challenge, but not having to worry about cash flow or rebuilding a network was too appealing to pass up.

I started at The Select Group in late February, and it has been a very refreshing experience. The people and culture feel like what I experienced at Under Armour from 2010 to 2013: gritty, results-oriented, amazing people who want to work together and WIN.

All of this isn’t to say I don’t have other things cooking (I always do). The Mattis Foundation is already making an impact, Jenelle is sourcing land for our first real estate development project, we’re thinking about funding a roll-up of local septic companies, and we’re looking at a short-term funding solution for small businesses.

Oh, and I’m back to writing and thinking through new ways to solve complex business problems.
Oh, and I launched The Adam Mattis Show because I miss giving people a platform from which they can tell their stories.
Oh, and I bought a Corvette that I’ll start racing in time trials this summer.
Oh, and Ford turned one. He is walking, starting to talk, and at the rate he’s progressing he’ll be our CPA by October.

But hey, you know, things have been pretty chill around here. 😉

Latest Writing

It’s amazing how the ideas flow when you remove stress variables from the system. It has been a long time since I’ve experienced such mental clarity, and damn — it feels good.

Things really began to unlock when I co-hosted the Triangle Digital Products and Innovation Meetup with my friends at Lucid. It didn’t occur to me until I arrived that it had been four months since I had been on a stage or even presented to a small group.

It felt great, and the next day I was back to writing.

Here’s what I’ve published over the last month (as always, your feedback and comments are appreciated!):

• Augmenting Your Dev Org with Agentic Teams (also on DZone)

• Who’s Left to Buy Your Product?

• Your SDLC Doesn’t Know What to Do With AI. It’s Costing You $ & %.

• The Product Model is Alive

• The Case Against Perpetual Transformation

Date Your Wife.

Last week, for my wife’s birthday, I arranged for a photographer to meet us at the restaurant. She had been feeling a little bummed about turning the age she did (I’ve learned my lesson about disclosure and won’t be sharing the number 🫢), so I wanted her to have the chance to see herself the way I see her every day.

For just $150 and 15 minutes, the photographer completely changed the tone of the night and shifted Jenelle’s perception of herself.

I’m sharing this because last night reminded me that the best gifts don’t have to cost a fortune and sometimes they require a little sacrifice (photos are definitely not my favorite way to spend time).

That, and because she’s an absolute smokeshow and I wanted to brag a little. 😎

Ford’s First Lap

I can’t believe our little dude is already one. What an insanely fast year it’s been!

Being Ford’s dad is hands-down the best thing I’ve ever done. He’s incredibly smart, loves people, is endlessly curious about how things work, has a big personality, and loves cars and motocross almost as much as I do.

It’s been so cool watching the things that light him up — and seeing him learn to love our dogs, Bailey and Ellie.

I love being Ford’s dad.

A Business You Should Know: Crawford Hospitality

Guys Mills, Pennsylvania, the small town where I grew up, isn’t exactly known for producing internationally renowned talent. We do have a few standouts, though. For example, Tiffany Beers built the actual self-lacing shoe from Back to the Future for Nike.

So when someone from your hometown makes it big and lives in your current town, I feel it’s my responsibility to help them compound their wins.

Scott Crawford, the nephew of the guy my dad worked for growing up, is a Michelin-recognized chef who operates six amazing restaurants in Raleigh.

I never reached out directly. I’m always paranoid about seeming like I’m using a loose family connection to “get” something. Instead, I supported him from a distance: Jenelle and I have eaten at every one of his restaurants, and I’ve followed him on Instagram.

After I interacted with one of his stories, Scott messaged me: “Are you Jeff’s son?” Shocked he even knew who I was, I replied yes. He remembered meeting me when I was about Ford’s age and said my dad had made a positive impression on him.

We exchanged numbers and now stay in casual contact over our shared love for fast things on two wheels.

While planning Jenelle’s birthday, I asked Scott for advice about his new speakeasy, Sous Terre. He not only helped with the key for a nightcap but also rolled out the red carpet. Even on a busy Wednesday night, he sent his General Manager and hostess to take excellent care of us.

Absolutely not expected. Absolutely appreciated.

If you’re ever in Raleigh, check out one of Scott’s restaurants. Even if you only make it to the airport, you’re in luck. He’s got one there too.

Cheers.

• Crawford and Son

• Jolie

• Bordetto

• Sous Terre

• Crawford’s Genuine

• Crawford Brothers Steakhouse

Thank You.

As always, I appreciate your support of myself, my family, and my people. The fact that so many of you have been following along with my shenanigans for all of these years is humbing.

I know I dropped the ball in terms of value over the last couple of newsletters, but in the immortal words of Frank Costanza: I’M BACK, BABY!

More to follow.

Cheers,
-AM

For my entire professional career, we have been in transformation, and I am calling it done. At some point, you have to exercise the muscles you built and get serious about being great at change itself. That is a different discipline than transforming, and it is exactly what The Select Group (TSG) helps enterprises do.

I have spent more than twenty years helping enterprises transform. I do not say that with exhaustion, but with the kind of perspective that only comes from having lived the full arc several times over, with the scars to tell their own stories. Twenty years is long enough to have ridden a few waves: the web build-out of the early 2000s, the digital transformation decade that followed, the methodological revolution that reshaped how teams work, and now the AI era that is reordering everything again. I have helped guide organizations through all of it as an engineer, architect, executive, and thought leader contributing to one of the most significant operating frameworks of modern history. Reflecting on all of it, I have noticed something that nobody else seems to be ready to discuss.

We have been transforming the same organizations over and over again. We have called it progress. In some cases we have even declared victory, and at the time, rightly so. Then leadership changes, people change, the transformation is declared “done,” and progress goes into regression.

Each wave arrived with genuine urgency and a solid business case. Each was the right thing at the time. Companies that did not build for the web lost. Companies that did not modernize their digital customer experience became irrelevant.

But somewhere between the first wave and this one, the enterprise stopped treating transformation as a response to a specific forcing function and started scoping it as a permanent program. The transformations kept rolling. The capability transfer rarely made it into the next evolution.

Here is what I believe after watching this cycle repeat for two decades: the problem is not the ambition. The problem is that we never built the muscles to stay changed.

The Wave Pattern

The web work came first. We built the digital storefronts, stood up the e-commerce infrastructure, moved the customer interaction layer online. That was genuine, consequential transformation. It was actually the foundation for my first real business, Carzz. The platform, pre-Cars.com, helped powersports and auto dealers easily manage their inventory on their very own .com. What a time that was. The .com era rewired business models and forced organizations to develop capabilities they had never previously needed. The companies that did it well came out structurally different.

Then came the broader digital transformation era. The mandate expanded beyond the customer-facing layer: data strategy, cloud architecture, experience design, platform thinking. Enterprises made enormous investments and brought the customer closer than ever. BCG’s global study of over 850 companies found only about one in three of those initiatives delivered on their stated value targets.

I personally led a digital transformation at an education consortium spanning 86 universities, 22 campuses, and 5 geographic regions. The impact was profound in terms of operational efficiency, student experience, and EBITDA gain, only to be eroded nearly as soon as the work was complete by a series of divestitures.

Then the wave shifted to how organizations work. Faster delivery, modern operating structures, rearchitected team models. The underlying problem was obvious: the delivery vehicle at most large enterprises was a product of the industrial age. It had to be shifted for a world where knowledge and context drove innovation. But the solution got packaged and industrialized in ways that prioritized repeatability over fit. Many organizations emerged with new processes layered on top of the same structural problems that made the work slow in the first place.

Now here comes AI. And I will tell you exactly what I tell every executive I sit across from: if you approach this wave the same way you approached the last three, you will get the same result. A program. An engagement. Measurable gains at close. And eighteen months later, someone in your organization asking what the next thing is. The gains will not stick, and the pain will be much greater this time than any previous wave due to the speed and scope of AI tooling. If you do not approach this differently, you will compound the friction already in the system, and you will fail.

A 2024 Bain analysis found that 88 percent of business transformations fail to achieve their original ambitions. Not a slim majority. Eighty-eight percent. The failed efforts cost organizations an estimated $2.3 trillion annually. We have been spectacularly, consistently bad at the thing we have been selling for two decades.

I do not think the problem is incompetence. I think it is something more structural, and more fixable.

“At some point, you have to stop getting in shape and start being an athlete. Those are not the same thing.”

I run six days a week. Cold plunge to start, sauna to finish. I have done it long enough to understand the difference between transformation and performance. Year one, you are chasing a distance. Year three, you are not transforming anymore. You are performing, and your goal shifts to sustainability and efficency. The gains are harder and slower, but they do not evaporate when you miss a week. You built the version of yourself that just keeps getting better. But you recognize that if you do not care for the system you have built, it will fail.

Enterprises need to make the same shift. The goal is not to complete a transformation. The goal is to build the operating model, the team capability, and the organizational reflexes that make improvement the default, and then seek sustainability in that system. This is a fundamentally different discipline than how most companies have operated throughout history. And most transformation programs were never designed to leave it behind.

Why the Model Keeps Failing

I want to be careful here, because I have been on both sides of this. I have run transformation programs. I have landed the engagements. And I have a lot of respect for the practitioners doing this work. The problem is not the people. The problem is how the work gets scoped.

Transformation programs are built to have an end. Scope, timeline, deliverables, handoff. That structure makes sense when you are solving a discrete, bounded problem. It does not make sense when the underlying issue is an organization’s ongoing capacity to absorb change. The mistake is not in the engagement itself, but in the scoping: optimized for delivery of a defined output rather than transfer of a durable capability. When the program ends, if building that internal muscle was never part of the scope, the decay begins. And even in the cases where internal discipline was established, organizational politics typically prevent the same level of transparent conversation that is free to take place between external advisors. Internal team members tend to call these crucial conversations CLMs: career-limiting moves.

I watched this play out at a major financial services firm. A large investment of time and capital was made over many years, and meaningful results were delivered from a talented team. Release frequency improved materially. Teams were better aligned. The metrics looked strong at program close. Two years later, the gains had largely reversed. Leadership was gone, new perspectives were brought in, reductions happened, and trust vanished. This did not happen because the organization lacked commitment, and not because the consultants did poor work. Because capability transfer was not in the scope, and the change had not been made foundational to how work was done at the enterprise, it did not stick when pressured by new voices. They had been handed a transformation. They had not been built into an organization that could own one.

McKinsey’s organizational health research adds an edge to this: performance transparency (the internal capacity to measure, see, and act on improvement feedback) appears in only four percent of transformation programs. The single practice most correlated with sustained gains is the one that almost never makes it into the statement of work.

The Structural Problem

Harvard Business Review noted last year that the traditional transformation model, rooted in Lewin's 1950s "unfreeze, change, refreeze" framework, was designed for discrete projects. It was never designed for an environment where the external landscape evolves faster than any single program can address. The organizations that outperform are the ones that have built change into their operating rhythm permanently, not as an initiative, but as a core organizational competency.

What Comes After Transformation

There is a better model, and I have seen it work at Fortune-50 enterprises with organizational gravity and firm resistance to change. The organizations that break the transformation cycle share a common set of characteristics, and none of them are mysterious.

These organizations treat their delivery model as a product. A product has an owner, a roadmap, a continuous improvement cycle built in. They apply the same rigor to the machinery of delivery that they apply to the products they ship. Ownership is permanent. Investment is operational, not capital. The improvement cycle does not close.

They build internal instrumentation before anything else. You cannot improve what you cannot see, and most organizations cannot see their own delivery system with any precision. The ones that sustain their gains have invested in flow measurement, quality signals, and structural friction visibility. They have built the systems to surface problems continuously, which means that when outside expertise comes in, it can go deeper faster, because the organization already knows where the drag is.

They close the loop between technology adoption and delivery capacity. This is the most urgent gap right now. AI tooling has accelerated development velocity at a pace that most downstream governance, quality, and release infrastructure was never designed to absorb. The organizations getting this right are not just adopting the tools. They are retooling the systems downstream to match the velocity the tools create upstream. The ones who are not are building fragility they cannot yet see.

And they develop leaders who own the change, not just sponsor it. Executive sponsorship is table stakes and everyone knows it. What actually determines whether an organization sustains its gains is whether there are leaders inside the delivery organization, not above it, with the knowledge, authority, and credibility to make ongoing modernization decisions without escalating every one of them. Those leaders are not hired. They are built, deliberately, over time.

“Most organizations are not stuck because they lack the ambition to change. They are stuck because they have built every muscle except the one that makes change self-sustaining.”

This is what I mean by continuous modernization. We do not want to launch another program. We do not want to rebrand a transformation. We aim to co-create an operating model where the organization's ability to detect what needs to change, sequence it, execute it, absorb it, and apply it to the next thing is built permanently into how the business runs. The measure of success is not program completion, but organizational self-sufficiency: the point at which the external partner is no longer necessary because the capability is owned.

AI Is Accelerating the Gap

A few weeks ago I published a piece on the AI Development Lifecycle. The argument: AI-native work operates by different rules than conventional software delivery, and organizations governing it with models built for a different era will underperform. I stand by every word. But there is a layer underneath it that connects directly to everything I have said here.

AI is accelerating the divergence between organizations that have built the continuous modernization competency and those that have not. If you have the internal instrumentation, the adaptive governance, and the change-ready delivery culture already in place, AI adoption amplifies your advantage. You absorb the capability, retool downstream to match the new velocity, and come out ahead of every competitor still running the transformation program model.

If you do not have that foundation, AI adoption accelerates your fragility. It makes a poorly-structured delivery system produce more output, more quickly, in more directions, with less visibility. That is not speed. That is scale-up failure on a faster timeline.

The organizations that win this era will not be the fastest AI adopters. They will be the ones with the absorptive capacity to translate AI capability into sustained competitive advantage. Closing that gap is, without qualification, the most consequential work available to enterprise technology leaders right now.

The Human Cost

I want to close with something that does not surface in executive conversations the way it should, because it lives below the waterline in the people doing the work.

The program manager who has built the deck, run the kickoff, earned the team’s trust, and delivered the results. Three times now, on three different initiatives with three different names. The technology leader who has stood in front of her organization and made the case for the new way of working, again, with genuine conviction, watching the skeptics in the back row who have seen this before. The delivery professional who poured himself into the work, watched it take hold, believed it this time, and then watched a leadership change quietly undo two years of progress in a single quarter.

These are not cynics. They were believers. And somewhere along the way, the model made them cynical.

That is the human cost of perpetual transformation. It does not show up in a program dashboard. It shows up in the leader who stops raising her hand. The engineer who updates his resume every time a new initiative kicks off. The institutional knowledge that walks out the door because they stopped trusting that the work would last.

When my son Ford was born, something shifted in how I think about the things worth doing. I wrote about it at the time: the move from training for vanity to training for longevity. The work I am most proud of in my career has that quality. It lasted. The organizations that own it today do not think of it as a transformation they survived. They think of it as how they operate. Change is not a program to them. It is a reflex. A competitive advantage.

That is the version of your organization that we need to build. Not the one that completes the next transformation. The one that never needs another one.

Ready to build the muscle?

TSG helps enterprises build the internal capability to modernize continuously, not episodically. If your organization is ready to stop starting over and start building change resiliency, let’s talk.

There is a particular kind of truth that only becomes visible in retrospect. The kind that was always operating in the background, shaping outcomes, but had no name yet. The product model is one of those truths.

The Mountain as a Product Lab

There is a particular kind of truth that only becomes visible in retrospect. The kind that was always operating in the background, shaping outcomes, but had no name yet. The product model is one of those truths.

In the winter of 2011, somewhere in the Appalachian highlands during a cold-weather direct action training exercise, a group of U.S. Army Special Forces soldiers were doing what they always did: they were breaking things. Never carelessly, always deliberately. The cold had stiffened the articulation points on a prototype boot designed for rapid elevation changes. A moisture-wicking base layer was wicking in the wrong direction under sustained exertion. A load-bearing integration panel on a plate carrier vest was creating friction at exactly the wrong point of the hip flexor during a tactical low-crawl.

I know this because I was tracking it. Not from a desk at Under Armour’s headquarters in Baltimore. Because feedback like this was the product. Between 2010 and 2013, I served as Global Product Line Manager for apparel, footwear, and accessories within Under Armour’s Military Tactical Business Unit. During that period, our team did something that the formal language of product management had not yet found words for. We built a real-time product development cycle, tested not in a usability lab or a survey panel, but on the side of mountains and inside live training environments.

We didn’t call it continuous discovery. We didn’t call it an empowered cross-functional team. We didn’t have a product development lifecycle document or a project backlog. We had soldiers, altitude, cold, and mere minutes between prototype and field correction. And looking back now, as enterprise organizations worldwide struggle to internalize what the market now calls the product model, I believe those mountain exercises were not just early prototyping. They were an early expression of the only way product has ever actually worked.

“The mountain doesn’t lie to you. A soldier in a cold-weather direct action environment cannot tell you politely that your moisture management system has a problem. He shows you. His core temperature tells you. His movement efficiency tells you.”

The moment you put distance between the people building a product and the people using it, whether that distance is geographic, organizational, or mediated by requirements documents, you introduce a translation layer that distorts feedback. Requirements become proxies for needs. Specifications become proxies for outcomes. The organization starts optimizing for hitting the proxy rather than achieving the underlying objective.

On the mountain, there were no proxies. The boot either performed at the required temperature gradient or it did not. The vest either distributed load acceptably during a six-hour movement or it created injury vectors. This is, in miniature, exactly what the modern product model attempts to replicate at enterprise scale. The difference is that in 2026, we have tools that can make the feedback loop not just fast, but effectively continuous, and intelligent in ways that were previously impossible.

What We Mean When We Say “Product Model”

The phrase has proliferated enormously in the last several years. Like most good ideas that achieve mainstream adoption, it has picked up conceptual debris along the way. Let’s be precise.

A product model is not an organizational chart. It is not a methodology. It is not agile, or lean, or any named framework whose certification you can hang on a wall. It is something more fundamental: the answer to the question, how does your organization decide what to build, learn whether you built the right thing, and improve continuously on the basis of that learning?

Practitioners across multiple traditions have arrived at the same destination by different roads. Marty Cagan and colleagues at SVPG define the product model around a single organizing principle: empowered teams, given a clear problem to solve and the authority to solve it, consistently outperform feature teams executing a predetermined roadmap. Teresa Torres, in Continuous Discovery Habits (2021), operationalizes what this looks like in practice: small cross-functional teams maintaining ongoing, structured contact with customers on a weekly cadence, using that contact to generate and test assumptions before committing resources. Enterprise scaling frameworks like SAFe extend these principles into larger organizational contexts, addressing the coordination and governance layers that emerge when dozens of teams need to operate coherently toward shared outcomes.

These are not competing philosophies. They are different lenses trained on the same operating reality. What they share is more important than what distinguishes them.

Both SVPG and SAFe converge on a distinction that sounds simple and is, in practice, organizationally difficult.

Project model: A fixed scope is delivered by a fixed date. Success equals delivery. The team disbands. The learning dissipates.

Product model: A continuous mission is pursued by a persistent team. Success equals outcome. The learning compounds. The capability grows.

What strikes me about the cross-functional team structures that serious enterprise organizations are now formalizing is not that they are new. It is that they are a formal articulation of something effective product teams have been doing informally for decades. In garages, on factory floors, in field training environments, and increasingly inside AI-augmented operations centers. The terminology evolves. The underlying logic does not.

The deeper challenge is not conceptual clarity. Most product leaders understand the distinction between project and product. The challenge is organizational: implementing a product model on top of an organizational architecture designed for projects. Funding models, governance structures, headcount allocation, performance measurement, and leadership behavior all evolved to support project delivery. Changing the operating model without changing those underlying systems produces the hybrid that large enterprises know all too well: agile language on top of waterfall execution.

Research across multiple sources puts the failure rate of product model transformations in large enterprises between 70 and 97 percent. The model works. The foundation underneath it usually does not.

Testing on the Side of a Mountain

When I joined Under Armour’s Military Tactical Business Unit, the company was navigating a fascinating inflection point. Under Armour had built its brand on performance compression wear for athletes, a domain where feedback loops are relatively controlled. You put an athlete in a garment, they compete, you observe and iterate. The cycle, while fast, operates in a bounded environment.

The military and tactical context was categorically different. Our customers were not competing in a stadium. They were operating in environments that no human-factors lab could replicate: sustained cold-weather movement at altitude, rapid transition between exertion states, load-bearing configurations that interacted unpredictably with layering systems, and mission contexts in which a product failure was not a performance inconvenience but a potential safety or operational incident.

Standard development timelines were insufficient. What we needed was something closer to what Teresa Torres would later describe as continuous discovery: a state in which the team maintains ongoing, direct contact with the people who use the product in the conditions where it actually operates. We arrived at this not through design theory, but through necessity.

Prototype garments and load-bearing equipment were introduced into live training exercises with operational units. We didn’t send a survey. We sent the product into the environment and established feedback mechanisms with the users inside it: performance debrief sessions, materials analysis after sustained wear, biomechanical observation during training scenarios, and direct dialogue with operators about what the product did and did not do under stress.

The feedback was not filtered through a procurement specification. It came directly from people whose professional lives depended on their equipment performing correctly. The valuable-feedback-to-junk ratio was extraordinary. And the iteration cycle compressed to hours rather than months, driven by the structure of training cadence.

What the Mountain Taught About Scale

The most important lesson from that period was about the relationship between user proximity and product quality. Every layer of organizational distance between builder and user is a translation layer. Requirements become proxies for needs. Specifications become proxies for outcomes. Roadmaps become proxies for strategy. And organizations gradually shift their energy from solving customer problems to defending their interpretations of them.

On the mountain, that dynamic was impossible. The feedback was unambiguous and immediate. And because the team was embedded close enough to receive it directly, we could act on it quickly.

This is, in miniature, exactly what the modern product model attempts to replicate at enterprise scale. The difference is that in 2026, we have tools that can make the feedback loop not just fast, but effectively continuous, and intelligent in ways that were previously impossible.

A Wireless Insurance Provider Discovers What We Already Knew

Consider a wireless insurance provider, the kind that sits behind your carrier’s handset protection program, handling device replacement claims and customer service for a few million policyholders.

In 2019, their call center was the canonical project-model operation. Agents handled claims according to a script developed by a product team that had last conducted systematic customer interviews eighteen months earlier. The claims processing platform, built in 2014, had been maintained through a series of discrete projects, each scoped and funded separately, each delivering against a requirements document already outdated by the time code shipped.

The system worked, in the narrow sense that claims were processed. But the metrics told a more complicated story. Average handle time was well above industry benchmarks. First-call resolution was declining. CSAT scores were flat at a level leadership characterized as “acceptable,” which in practice meant they had stopped trying to improve it.

The issue was not effort or talent. It was architecture: the organizational architecture of how the product was developed and improved. There was no continuous feedback loop between the people experiencing the product and the people shaping it. There was no persistent, empowered team with a clear outcome mission, ongoing customer access, and the organizational authority to act on what they learned. There was a roadmap. There were project teams to execute it. And there was a growing distance between what customers actually needed and what the organization was building.

The Transformation

Beginning in 2021, the organization restructured around a product model. The change was not primarily technological. It was organizational.

A cross-functional team was assembled around the claims resolution product: engineers and a product manager, yes, but also a UX researcher embedded in the call center two days per week, a data analyst with direct access to call recordings and resolution metadata, and a customer success partner maintaining direct relationships with frontline agents who provided ongoing feedback on product friction.

The team’s mandate was redefined from “deliver features on the roadmap” to “improve claims resolution outcomes.” This shift, from output accountability to outcome accountability, is the pivot on which effective product models turn. Cagan and his SVPG colleagues call it the move from feature teams to empowered product teams. Torres calls it the shift from build-trap thinking to continuous discovery. Whatever you call it, the organizational experience is the same: the team stops asking “what did we ship?” and starts asking “what did we change?”

The critical shift was governance, not technology. Leaders gave up control of the what, specific features and delivery dates, in exchange for greater influence over the why: strategy, outcomes, mission. Most found this harder than they expected.

Within twelve months, average handle time had decreased significantly. Not because of a technology revolution. Because of a series of small, evidence-based product improvements discovered through continuous engagement with agents and customers: a simplified device verification flow, a proactive status update system that eliminated a large share of inbound status-check calls, and a contextual knowledge surface that surfaced relevant policy information to agents at the precise moment they needed it. None of these improvements appeared on the original roadmap. All were discovered through ongoing, embedded customer contact.

Then AI Arrived, and the Cycle Got Faster

In 2023, the organization began integrating AI-powered capabilities: an AI-assisted triage system that pre-classified inbound claims by complexity and routed them accordingly; a real-time transcription and sentiment analysis system providing supervisors with live call quality indicators; and an automated processing pathway for straightforward device replacement claims requiring no human agent involvement.

This reflects an industry-wide shift. According to McKinsey’s 2024 analysis of AI in insurance, UK insurer Aviva deployed more than 80 AI models in its claims domain, cutting liability assessment time for complex cases by 23 days, improving routing accuracy by 30 percent, and reducing customer complaints by 65 percent. Broadly, AI assistance has reduced overall claims resolution time by as much as 75 percent in high-performing implementations, from 30 days to under 8, with routine claims moving from a week or more down to 24 to 48 hours.

The AI system didn’t tell us what to build. It told us, faster and more clearly than anything before it, what wasn’t working, and for whom, and under what conditions.

What these AI systems did, beyond their immediate functional benefits, was something more structurally significant: they created an unprecedented volume of continuous feedback. Every automated claim that succeeded or failed generated data. Every sentiment inflection in a live call was timestamped and tagged. Every routing decision could be evaluated against its outcome. The product was, for the first time, generating its own discovery data at scale.

This is where the product model and AI become genuinely inseparable. Not because AI replaces the human judgment at the center of good product work. It does not, and the organizations that believe it does will discover this expensively. But AI provides the feedback infrastructure that makes continuous discovery operationally feasible at enterprise scale.

What the Frameworks Got Right, and Where Most Enterprises Are Still Stuck

Multiple research streams now agree on why product model transformations fail at scale. The model itself works. The failure is almost always organizational readiness, not conceptual validity.

The root cause is consistent across frameworks and traditions. Cagan and his SVPG colleagues argue in TRANSFORMED (2024) that the degree of change required is more disruptive than most companies expect, not because the principles are complicated, but because they require genuine behavioral change from senior leaders, not just organizational restructuring. SAFe’s enterprise guidance points to the same failure mode: building a product-based way of working on top of an organizational culture optimized for centralized decision-making and project delivery.

Both diagnoses point to the same root cause: product model adoption is a leadership transformation before it is an organizational one. You can restructure teams, rename roles, and stand up discovery rituals without changing the underlying dynamic in which senior leaders maintain control over the what, the specific features, timelines, and delivery commitments, rather than the why, the strategy, outcomes, and customer problems the team is authorized to solve.

This produces the hybrid that large enterprises know all too well. Teams that are nominally empowered but practically constrained. Discovery rituals that generate insight but no authority to act on it. Roadmaps that are labeled outcomes but function as feature delivery plans. The language of product model maturity layered on top of project model execution.

The Three Foundations

Across the practitioners and frameworks that have studied enterprise product transformation most rigorously, three non-negotiable conditions emerge. Each is load-bearing. Addressing only one or two of them produces stall, not transformation.

Team and technical agility. Teams need the structural conditions to move fast: small enough to coordinate without ceremony, persistent enough to develop domain knowledge over time, and cross-functional enough to carry a problem from discovery through delivery without handoffs that introduce delay and distortion. Technical practices matter here too. Continuous delivery, automated testing, and modular architecture are not engineering preferences. They are organizational capabilities that determine how quickly a team can act on what it learns.

Strategy clarity and outcome orientation. Teams need to know what they are optimizing for at a level of precision that makes daily decisions possible without escalation. OKRs and similar instruments are not about measurement. They are about communication: translating organizational strategy into team-level operating clarity. Without this, empowerment is directionless and accountability is theater.

Leadership behavior, not just structure. The product model asks senior leaders to give up control of the what in exchange for greater influence over the why. Most senior leaders in large organizations have built their careers on being good at controlling the what. The transition requires a genuinely different kind of leadership fluency. As Torres frames it in Continuous Discovery Habits, the team closest to the customer should have the most authority over the solution. In most large organizations, the inverse is true. Changing this requires sustained behavioral change at the leadership level, not just a new org chart.

The Leadership Paradox

The hard work is not in the ceremonies or the tooling. The hard work is in the leadership decisions that either enable or disable the model.

Dedicated cross-functional teams require leaders to give up the resource allocation control that matrix organizations are designed to provide. Outcome-based deliverables require leaders to accept the ambiguity of result-based measurement rather than the false certainty of feature delivery metrics. Continuous learning requires leaders to treat team capability development as a strategic investment rather than a cost to minimize.

In the Under Armour military context, this leadership question was answered by the nature of the customer relationship. When your end users are operational units with specific mission requirements and the organizational credibility to specify what they need with clarity and authority, it is very difficult for a product leader to substitute their own judgment for that of the user. The customer context forced a kind of organizational humility that civilian product organizations often have to cultivate deliberately.

The best product leaders I have worked with share this quality: a genuine conviction that the customer’s reality is the product’s truth, and a willingness to let that reality reshape their assumptions in real time.

A Brief History of How We Got Here

2010 to 2013. Under Armour’s Military Tactical Business Unit embeds prototype garments and equipment in live training exercises, creating near-real-time feedback cycles that anticipate the continuous discovery discipline by a decade. The operating insight: user proximity is not a research technique. It is an organizational design principle.

2017 to 2020. Two bodies of product thinking mature in parallel. Cagan’s revised INSPIRED (2017) and EMPOWERED (2020) formalize the vocabulary of empowered product teams and outcome orientation for technology companies. Enterprise scaling frameworks evolve their product delivery competencies to place customer-centricity and continuous feedback at the center of delivery at scale.

2021. Teresa Torres publishes Continuous Discovery Habits, providing the operational framework for what weekly customer engagement by the building team actually looks like in practice.

2022 to 2024. Large industrial, financial, and insurance organizations begin serious attempts at product model adoption. TRANSFORMED (2024) and LOVED (2022) complete the SVPG series. Research from McKinsey and others begins documenting the gap between pilot success and enterprise-scale transformation.

2023 to 2024. Generative AI and intelligent automation begin augmenting product operations at scale. McKinsey reports that leading insurers are deploying dozens of AI models in the claims domain, achieving routing accuracy improvements of 30 percent or more and customer complaint reductions of 60 to 65 percent. The AI system becomes feedback infrastructure, generating continuous discovery feedback from every customer interaction.

2025 to present. The distinction between “building a product” and “operating a continuously improving system” collapses. Full AI adoption in the insurance sector jumped from 8 percent to 34 percent year-over-year between 2024 and 2025. The product model becomes the only viable organizational architecture for companies competing in AI-native markets.

The Road Ahead

The fundamental logic of the product model is not new. It is, in its essentials, the same logic that governed effective product development long before anyone had a name for it. It is the logic of the mountain exercise, of the embedded prototype cycle, of the team that stays close to the user and lets user reality shape product direction. What is new is the scale at which that logic can now operate, the speed at which feedback can now travel, and the intelligence that AI can bring to the task of making that feedback legible.

For a wireless insurance provider running AI-powered claims triage and automated processing, the product is no longer a static artifact that improves in discrete quarterly releases. It is a living system that learns from every interaction, surfaces its own improvement opportunities, and delivers value in increasingly precise alignment with individual customer needs. The product model is not just the organizational structure that governs this system. It is the only organizational structure that can keep pace with it.

When I was tracking boot performance on a hillside in Appalachia, I had no idea I was doing product management. I was just trying to make something work better for people who needed it to. That impulse, that fundamental orientation toward the user’s reality, is the origin of everything that the product model tries to formalize.

AI gives us tools to pursue that impulse at a scale and speed that would have seemed implausible five years ago. The product model gives us the organizational architecture to use those tools well. And the mountain, wherever you find it, gives us the reminder that none of this was ever about the framework. It was always about getting close enough to the truth to do something useful with it.

The shift from project model to product model is not, in the end, a transformation of process. It is a transformation of relationship: a fundamental reorientation of the organization toward the people it serves, sustained by the structures, disciplines, and now the intelligent systems that make that reorientation durable. Organizations that make this shift will compound their advantage over time. Those that do not will find themselves increasingly unable to explain why their investments in technology and talent are not translating into outcomes. The gap between those two futures is the gap between project and product.

— Adam Mattis

Working on This Problem? The Select Group Can Help.

The distance between understanding the product model intellectually and executing it inside a real organization, with real teams, real stakeholders, and real AI infrastructure to integrate, is where most transformations stall. That gap requires more than a framework. It requires the right people, the right capability, and a partner who has built at the intersection of technology and organizational change.

The Select Group (TSG) has spent more than three decades helping Fortune 500 and mid-sized companies turn ambitious initiatives into working outcomes.

Headquartered in Raleigh, NC and operating across the United States and Canada, TSG delivers end-to-end technology consulting across Data and AI, Digital Transformation, Cloud and Infrastructure, and Agile and Product practices. Their adaptive model brings together solutions strategy, high-caliber technical talent, and dedicated engagement oversight: the three things most organizations are missing when a product transformation quietly becomes just another project.

If your organization is asking how to move from shipping features to creating outcomes, how to build continuous discovery into your operating rhythm, or how to make AI a genuine part of your product feedback loop rather than just a technology investment, TSG is the partner equipped to help you answer those questions with action, not just analysis.

Connect with Adam on LinkedIn to continue the conversation.

References

[1] Scaled Agile, Inc. (2025). The Product Operating Model at Scale: Three Keys to Implementation Success. Executive Guide, April 2025. scaledagile.com

[2] Cagan, M., Hickman, L., Jones, C., Idiodi, C., and Moore, J. (2024). PRODUCT IS HARD: SVPG Box Set. John Wiley and Sons. ISBN: 9781394326266. svpg.com/books

[3] Torres, T. (2021). Continuous Discovery Habits: Discover Products that Create Customer Value and Business Value. Product Talk LLC. ISBN: 9781736633304. producttalk.org

[4] McKinsey and Company. (2025). The Future of AI in the Insurance Industry. mckinsey.com

[5] Datagrid. (2025). 42 Insurance AI Agent Statistics (Adoption and Impact). datagrid.com

BLUF: The Software Development Lifecycle was built for a world where software shipped on a schedule. Unfortunately, our AI-augmented world doesn’t work on a schedule. CIOs who keep mapping AI initiatives onto SDLC assumptions are doing worse than just slowing things down. They’re structurally misaligned with how value actually gets created now, and the gap between organizations that figure this out and the ones that don’t is widening faster than most people realize.

The pattern is undeniable. The AI pilot lands well. Leadership gets excited. Someone stands up a project team, drops it into the delivery framework, and the whole thing slows to a crawl. Eighteen months later, the board asks what happened, and the honest answer nobody gives is: we tried to deliver a different kind of work using a process designed for a different era.

I’ve seen this play out time-and-time again; delivery frameworks failing, people scrambling, only to be left asking “why?”. The teams aren’t failing. The technology isn’t failing. The system is failing. (Where have I seen this movie before?) In this case, the system is the SDLC.

Let’s be clear, this isn’t a process optimization problem that we can “Lean” our way out of. You can’t iterate your way out of it, either. What’s breaking is more fundamental than that, and the organizations getting ahead of it aren’t doing it by running better retrospectives.

What the SDLC Was Built For

To understand why it’s breaking, it helps to understand what it was designed to solve.

Enterprise software development in the mainframe and early client-server era was expensive, slow, and largely irreversible. Hardware constraints meant that rework was operationally dangerous. Requirements had to be locked early. Testing had to be exhaustive. Release cycles were long because deployment itself was a high-stakes event with real failure modes.

In my early days of operations consulting, I remember working with an enterprise client who was certain there was no way they could release more frequently than once per three years due to the dangers of the “corporate load.” We managed to reduce that cycle to a quarterly release, but doing so safely required re-architecting the entire tech-stack and a good bit of their business operations as well. Too many consultants are quick to peg this a “leadership problem” without thoroughly understanding the complexity of the operations. The point is, the SDLC as we know it served a purpose. And, even in 2026, in some enterprises, that context still exists.

The SDLC emerged as a rational response to those constraints. Define the work, sequence it, gate it, ship it. That model worked remarkably well for decades, and it produced a generation of enterprise delivery professionals who built discipline around it.

Agile modernized the cadence. Two-week iterations instead of multi-year waterfall projects. Incremental delivery instead of big-bang release. Continuous feedback instead of requirements locked in Phase 1. It was a genuine improvement, and it’s the right model for a wide class of software work.

But here’s what Agile didn’t change: the underlying assumption that the job is to build a defined thing to a defined specification, and ship it.

That assumption is what AI breaks.

AI Development Doesn’t Have a Finish Line

Traditional software is deterministic. Given the same inputs, a well-built system produces the same outputs. You can write a requirement for it, test against it, and know when you’re done. The definition of done is binary: it works as specified, or it doesn’t.

AI systems are probabilistic. Given the same inputs, a model produces outputs that vary based on training data, fine-tuning decisions, inference parameters, and the statistical properties of the task. There is no requirement you can write that fully specifies model behavior in advance, because the model’s behavior is an emergent property of training on data, not a direct implementation of logic.

This has concrete consequences for how an enterprise manages delivery. You don’t write requirements for a model. You define a problem, form a hypothesis, run experiments, evaluate outputs against business outcomes, deploy, observe real-world behavior, and iterate based on what you learn. The specification for success is probabilistic and contextual, not binary and universal.

A model that performs well in evaluation can perform differently in production because production data has different statistical properties. A model that performs well in one customer segment can underperform in another. A model that performs well in March can drift by September because the world it’s predicting has changed.

There is no “shipped” state that ends the development work. The model in production is a living artifact. It requires ongoing monitoring, evaluation, retraining, and refinement as long as it’s in use.

The model in production is still a development artifact. There is no “done.”

Sources: Andrew Ng, “AI Transformation Playbook,” Landing AI; Sculley et al., “Hidden Technical Debt in Machine Learning Systems,” NeurIPS 2015; Google, “Practitioners Guide to MLOps,” 2021

When You Force AI Into an SDLC Container

When AI development gets managed through SDLC assumptions, the consequences are predictable and consistent. I’ve seen the same patterns across clients regardless of industry, size, or technical maturity.

• Requirements get written for unknowable things. Business stakeholders are asked to specify model behavior before anyone has run a single experiment. The result is requirements that are either so vague as to be useless or so specific as to be wrong. Neither gives the team what it needs to deliver actual business value.

• Iteration velocity becomes a fiction. AI development work doesn’t decompose cleanly into stories and tasks. The value isn’t in completing a ticket, it’s in learning whether a hypothesis is true. A sprint where the team ran three experiments and learned that two approaches don’t work is a successful sprint. In an Agile framework, it looks like zero velocity. So teams game the metrics.

• Definition of done gets misapplied. A model is never done; it’s just performing at a certain level given current data and conditions. Organizations that apply traditional done criteria to AI work end up either declaring victory on systems that aren’t actually working, or never declaring anything done because the goalpost keeps moving.

• Production becomes an afterthought. SDLC treats production as the end state. In AI, the production signal is a development input. What happens when real users interact with the model is data you need to improve it. Organizations that separate development from operations for AI work are cutting off their most valuable feedback source.

• Governance creates adversarial dynamics. Phase-gate reviews were designed to catch scope creep in projects with known deliverables. They are not equipped to evaluate AI initiatives where the right path forward isn’t knowable in advance. So they slow things down without adding value. Teams learn to perform for the review rather than doing the work that matters.

Sources: Amershi et al., “Software Engineering for Machine Learning: A Case Study,” Microsoft Research, 2019; Sculley et al., NeurIPS 2015

The ADLC Is a Different Set of Physics

The AI Development Lifecycle isn’t a replacement for discipline, but a discipline built for different physics. And the differences aren’t cosmetic.

Where SDLC optimizes for predictability, ADLC optimizes for learning velocity. The measure of a healthy ADLC isn’t how closely the team hit their iteration commitments. It’s how quickly they moved from hypothesis to validated outcome, and how much of what they learned in production shaped the next iteration.

Where SDLC treats production as the endpoint, ADLC treats production as a continuous feedback source. Monitoring, observability, and model performance tracking aren’t post-delivery operations concerns. They are core development infrastructure that has to be designed and built before the first model ships.

Where SDLC separates development from operations, ADLC requires them to be integrated. MLOps, the operational discipline that makes this integration work, has matured considerably. But most enterprise organizations haven’t restructured their teams to take advantage of it, because doing so requires dismantling organizational boundaries that have been in place for decades.

Where SDLC measures quality as conformance to specification, ADLC measures quality as performance against outcomes.

A well-functioning ADLC asks:

• What business outcome is this model driving?

• How is that outcome trending over time?

• Where is the model underperforming relative to opportunity?

A well-functioning SDLC asks:

• Are we on schedule?

• Are we on budget?

• Did we hit the acceptance criteria?

Those are the right questions for the wrong problem.

The organizations getting real value from AI are not the ones with the best SDLC hygiene. They’re the ones that stopped asking their SDLC to do work it wasn’t designed to do.

Sources: Kreuzberger et al., “MLOps: Overview, Definition, and Architecture,” 2022; Google, “Practitioners Guide to MLOps,” 2021

Why the Mismatch Keeps Winning

If the problem is this clear, why is it so consistent? Because the incentive structures that govern enterprise technology delivery are built on SDLC assumptions, and those structures don’t change just because the work does.

• Funding models expect a scope, a timeline, and a deliverable. Capital expenditure frameworks are designed around projects with terminal endpoints. AI capabilities don’t have terminal endpoints. Organizations that fund AI initiatives the same way they fund a new ERP implementation end up either underfunding continuous development work or forcing it into artificial project structures that don’t reflect how the work actually operates.

• Governance frameworks expect phase gates. ADLC’s continuous iteration model looks, to a traditional governance body, like a team that never finishes anything. The natural institutional response is to add more structure: more gates, more documentation requirements, more review cycles. The structure makes the problem worse.

• Organizational metrics reward on-time, on-budget delivery of defined scope. They don’t have vocabulary for “we ran twelve experiments, eight didn’t work, four produced insights that changed our approach, and we’re now three months ahead of where we’d be.” That outcome looks like replanning. It gets scored as underperformance.

• Talent structures were built around role definitions that separate development from operations. The people who build software and the people who run it are different teams, different career tracks, different management chains. AI work requires them to be the same conversation. Reorganizing around that requirement is hard, and most organizations haven’t done it.

The people running these frameworks aren’t the problem. They built strong discipline for real problems. The issue is the assumption that the same framework governs work that operates by completely different rules. That assumption is invisible until it costs you something.

For CIOs, the practical consequence is a team executing well on the wrong model and getting punished for it. That is a leadership problem, not a delivery problem. It won’t be solved by better retrospectives.

What the Organizations Getting It Right Do

The enterprises I’ve seen navigate this transition successfully share a consistent set of moves. None of them are complicated in concept. All of them are hard in practice because they require changing structures that have organizational momentum behind them.

• Reform the funding model first. AI capabilities need to be funded as ongoing operational investments, not capital projects. The SAFe Lean Portfolio Management model offers a practical solution: replace traditional project-based funding with Lean Budgets allocated to value streams, governed by guardrails rather than detailed upfront business cases. Funding flows to the capability continuously, and reallocation decisions happen at cadence-based portfolio sync events rather than through formal change control. For organizations with existing SAFe adoption, this isn’t a new concept, it’s applying LPM discipline to AI investment the same way you’d apply it to any product value stream. For organizations without it, the principle still translates: fund the team and the capability, not the project. Treat improvement over time as a success criterion, not an exception. Frame it as a shift from “build and done” to “build and run” — a model finance already understands from infrastructure investment, and one that SPM practitioners recognize as the natural extension of participatory budgeting into AI operational spend.

• Redesign governance, don’t bypass it. The question shifts from “is this project on track against original scope?” to “is this capability performing against business outcomes, and are we learning fast enough to keep improving it?” Portfolio Kanban gives you the structural answer here: AI initiatives flow through portfolio-level visibility as epics, with progression gates tied to hypothesis validation rather than phase completion. Instead of asking “did we finish the build phase,” you’re asking “did we validate the business hypothesis that justified this investment?” That’s a meaningful governance event. Pair that with Weighted Shortest Job First prioritization at the LPM level and you have a framework for making active reallocation decisions based on learning, not waiting for a quarterly steering committee to approve a change request. For organizations with mature SPM functions, this maps directly to the strategic portfolio review cadence: AI capabilities belong on the portfolio Kanban board alongside other value stream investments, governed by the same outcome-based guardrails, not siloed into a separate “AI governance” structure that operates outside normal portfolio discipline.

• Change what you measure. The delivery metrics that matter for SDLC tell you nothing useful about AI initiative health. The metrics that matter for ADLC: how quickly are we moving from hypothesis to validated outcome? Is model performance improving or degrading over time? What’s our data drift detection latency? How much of what we observe in production is feeding into the next development cycle?

• Integrate development and operations structurally. Some organizations start by creating AI capability teams that own both development and operations for specific use cases, then use those teams as a model for broader structural change. The common thread is making the production signal a first-class development input, whatever structural form that takes.

• Sequence the transition intentionally. The organizations that try to shift everything at once tend to create chaos without benefit. The ones that do it well start with one or two AI capabilities of sufficient strategic importance to justify building the right infrastructure around them, then use those as proof points to drive broader organizational change.

A Note on What This Is Not

This is not an argument against rigor. SDLC disciplines emerged because software development without structure produces expensive, late, and incorrect software. The discipline is real. The need for discipline in AI development is equally real.

What ADLC rejects isn’t rigor. It rejects the specific application of SDLC-shaped rigor to AI-shaped work. The rigor looks different: hypothesis-driven experimentation instead of upfront requirements, outcome metrics instead of acceptance criteria, continuous monitoring instead of pre-release testing, production observation instead of deployment as endpoint.

This is also not an argument that SDLC is dead. A significant portion of enterprise technology work is still traditional software development, and it should still be managed with SDLC discipline. The point is that organizations need to distinguish between which work fits which model, and stop applying SDLC assumptions uniformly across a portfolio that increasingly contains AI capabilities requiring a different approach.

The CIOs I see struggle are the ones who either try to run AI work like software development, or overcorrect by treating all AI work as uniquely ungovernable. The right answer is structural clarity: different work requires different delivery models, and the organization needs the capability to run both.

What This Is Asking of You

The technology leaders positioned well on the other side of this transition aren’t the ones who ran the fastest pilots. They’re the ones who recognized early that delivering AI value requires different organizational infrastructure, and made the structural investments to build it.

That work is not glamorous. Funding model conversations with CFOs who have capital budgeting frameworks that are forty years old. Governance redesign with boards who want phase gates and milestone reviews. Metric evolution with business stakeholders who want to know what they’re getting and when. Team restructuring with engineering leaders who have built their careers around role definitions that need to change. None of it generates the announcement that a new AI product went live. All of it determines whether the AI capabilities you’re building actually improve over time, or plateau and decay.

The data on where most organizations are right now isn’t encouraging. McKinsey’s 2024 State of AI report found that while 72% of organizations had adopted AI in at least one business function, fewer than 10% described themselves as having mature AI deployment capabilities — defined as the ability to move from pilot to production to continuous improvement systematically. The gap between “we ran some pilots” and “we have an organizational capability to develop and operate AI” is where most enterprises currently live.

That gap isn’t a technology problem. It’s an organizational architecture problem. And it won’t close by running better sprints.

The CIOs who look back on this period with satisfaction won’t be the ones who ran the most pilots. They’ll be the ones who built the organizational infrastructure AI actually requires.

Sources: McKinsey Global Institute, “The State of AI in 2024,” May 2024; Gartner, “AI Engineering: A Key Capability for Scaling AI,” 2023

The Takeaway.

The SDLC was a genuine achievement. The discipline it created, applied at scale, produced decades of reliable enterprise software delivery. That’s not nothing.

The work has changed. The work of developing AI capabilities operates by different rules, and the organizational infrastructure required to do it well is different from the infrastructure enterprises have spent decades building. The gap between those two realities is where most AI investment gets lost right now.

The leaders who navigate this well won’t be the ones who shipped the most pilots. They’ll be the ones who recognized that the organizational architecture question is the real one, made the structural investments to answer it, and built the continuous capability to develop, operate, and improve AI in a way that compounds over time.

That’s harder work than optimizing a sprint ceremony. It requires holding the tension between near-term pressure that is absolutely real and structural change that takes longer than a quarter but matters more than any individual initiative. And it requires someone in the room willing to name the actual problem instead of proposing a better version of the solution that isn’t working.

If you’re a CIO working through what that transition looks like in your organization, I’m interested in that conversation. The variables are real, the path is navigable, and the distance between organizations that figure it out and the ones that don’t is compounding faster than most people want to admit.

Care to chat? Find me on LinkedIn.

BLUF: Enterprise leaders are making AI efficiency decisions optimized for the wrong time horizon. The demand consequences are economy-wide, they’re already starting, and they belong in your model now, regardless of what you sell or who you sell it to.

You’ve seen the Block announcement. Jack Dorsey cut nearly half his workforce and framed it as an AI efficiency play. Then every board and CEO watched the stock jump 24%.

Now the question is sitting on the desk of every enterprise CEO: how much of this applies to us?

Over the last eighteen months I’ve been in that room across enterprise clients and PE advisory work, all around the world. The pattern I keep seeing is consistent enough that I want to name it directly, because I don’t see it being named at all anywhere else.

The leaders making these decisions aren’t wrong that AI creates genuine efficiency opportunity. They are, in many cases, running an incomplete model. They’re solving for the cost variable while leaving the demand consequence out of the forecast entirely. The incentive structure they operate inside makes that omission not just understandable but, in the short term, rational.

That’s the barrel of the gun the global economy is staring into. And before I explain the demand consequence, I want to address an error I see in almost every version of this conversation: the assumption that demand risk only applies to companies that sell directly to consumers or to knowledge workers specifically.

It doesn’t. The ripple is economy-wide, and world-wide. If your current model doesn’t account for that, it isn’t a complete model.

The efficiency gain is undeniable. The question is whether you’re modeling the full cost of how you capture it — and on whose watch the bill lands.

The Pattern I See

Let me be specific, because vague warnings about systemic risk are easy to dismiss.

In PE-backed enterprises, the pressure comes from the fact that the cheap pandemic-era debt they leveraged to gain liquidity in their acquisitions is now expensive. Variable interest is compressing margins, and the board needs EBITDA improvement to support an exit thesis. AI-driven workforce reduction is a clean story. It improves the multiple, it’s defensible to the next buyer, and the timeline to exit means the operational consequences of moving too fast land after the transaction closes. The incentive to optimize for the near-term number is almost perfectly constructed.

In public enterprises, the mechanism is different but the outcome is similar. Quarterly earnings pressure, institutional investor expectations, and compensation structures tied to near-term performance create a planning horizon that systematically discounts consequences arriving beyond the current cycle. When Block cut four thousand people and the stock jumped 24%, every public company CEO with a board to answer to ran the same mental calculation. The market just told them what it rewards.

In both cases, what gets left out of the model is the same: a serious analysis of what happens to the broader economy, and ultimately to demand across every sector, when you systematically eliminate the wages of people who spend almost all of what they earn.

That omission isn’t stupidity, but a systemic byproduct of how decisions get evaluated and rewarded. But it’s still an omission, and the consequences will impact us all.

The Economy Runs on Middle-Class Spending.

Here is the foundational fact that most AI workforce conversations skip entirely: consumer spending accounts for approximately two-thirds of U.S. GDP. Not corporate investment. Not government spending. Consumer spending.

Source: U.S. Bureau of Economic Analysis; BLS Consumer Expenditure Surveys, 2024

The professional middle class, the knowledge workers earning roughly $75,000 to $150,000, represents the core engine of that spending. Not the wealthiest households, whose consumption is increasingly driven by asset appreciation rather than wages, and who spend a smaller share of each incremental dollar. Not lower-income households, who are already stretched. The middle. The people buying cars on five-year loans, renovating kitchens, taking the family vacation, replacing electronics, furnishing homes, and spending steadily on Amazon.

When those wages disappear, the spending doesn’t get redirected. It contracts. And that contraction doesn’t stay contained to the categories those workers were buying from directly.

Consider the chain from a single layoff decision: A financial analyst at a mid-size firm loses her job to AI automation. She stops the kitchen renovation mid-project. The contractor who had two months of work loses the job. The contractor’s spending at the local hardware supplier drops. The supplier’s transaction volume, processed through a payment platform, declines. The building materials distributor sees order volume soften. The trucking company that moves those materials runs fewer loads. The fuel supplier sells less diesel. The regional bank that lends to all of these businesses sees loan demand weaken.

Your SaaS payment platform never sold anything to a financial analyst. But it just felt her layoff.

There is no sector structurally insulated from a contraction in the spending of people who earn $75,000 to $150,000.

This is not a hypothetical chain. It is how economic multipliers work, and it is why the Federal Reserve, the Congressional Budget Office, and every serious macroeconomic model treats middle-income wage contraction as a systemic risk.

What the Data Shows

If you look at the numbers, the canary has already died in the coal mine. But you need to know where to look.

The displacement is concentrating in exactly the income segment that drives the spending the broader economy depends on. In January 2025, the U.S. Bureau of Labor Statistics reported the lowest rate of job openings in professional services since 2013, representing a 20% year-over-year drop. Analysis by Vanguard found that hiring for positions paying over $96,000 annually had reached a decade low. White-collar job postings fell 12.7% between Q1 2024 and Q1 2025, while white-collar wage growth stalled even as blue-collar wages continued to rise. This is a structural shift in the professional labor market, not a cyclical one.

Sources: U.S. Bureau of Labor Statistics, January 2025; Vanguard Research, 2025; Revelio Labs White-Collar Labor Market Analysis, 2025

The consumer data is catching up. McKinsey’s ConsumerWise research found that 75% of U.S. consumers reported trading down in at least one spending category through 2025, with sentiment declining considerably from the start of the year. Cardboard box shipments — a reliable proxy for goods movement and middle-market consumer activity — have fallen to multi-year lows, with the decline concentrated among lower- and middle-income households.

Sources: McKinsey ConsumerWise, December 2025; Cascade Partners U.S. Consumer Economy Analysis, October 2025

There is a nuance here worth naming. Recent Moody’s Analytics data shows that the top 10% of earners now account for nearly half of all U.S. consumer spending, and that spending has been sustained by asset appreciation rather than wages. Goldman Sachs Research found no significant statistical correlation between AI exposure and broad labor market measures as of mid-2025, and projects that AI-related displacement effects tend to be temporary at the aggregate level.

Sources: Moody’s Analytics/Wall Street Journal, February 2025; Goldman Sachs Research, August 2025

Here is why those findings only reframe the problem.

An economy increasingly dependent on top-earner spending sustained by asset prices is an economy with a fragile consumption base. Mark Zandi at Moody’s said it directly: relying on stock-market-driven spending from the top 10% makes the economy more vulnerable, not less. The professional middle class — the segment now in the AI displacement crosshairs — is precisely the stabilizing layer between subsistence spending at the bottom and asset-driven spending at the top. Remove it, and you destabilize the entire economic engine.

For enterprise leaders, the diagnostic question is this: what percentage of your revenue — directly or through one or two steps in the value chain — is downstream of consumer discretionary spending driven by professional middle-class wages? For most Fortune 1000 companies, that answer is closer to ‘most of it’ than any current model reflects.

Reskilling Isn’t the Solution

There’s a standard response to this concern: workers will reskill. New jobs will emerge. Technology transitions always create as many opportunities as they eliminate.

Partially true and wildly overstated as a complete answer.

The WEF’s Future of Jobs Report 2025 — surveying over 1,000 employers representing 14 million workers — projects 92 million jobs displaced by 2030 and 170 million created, a net gain. But the WEF itself noted that these are not direct exchanges happening in the same locations with the same people. The challenge is the gap — geographic, temporal, and skill-based — between where jobs vanish and where new ones appear.

Source: World Economic Forum, Future of Jobs Report 2025

That gap is where the spending contraction lives. The net employment number over five years does not prevent a demand trough in years two and three. And years two and three are on your planning horizon.

The workers most exposed right now are mid-career knowledge professionals: analysts, coordinators, compliance roles, financial planning support, junior legal and consulting functions. These are not people who absorb a multi-year retraining program without meaningful financial disruption. The AI engineering roles being created require mathematical foundations that six-week bootcamps don’t produce. The trades pipeline isn’t designed for this volume or this demographic.

More fundamentally: every previous automation wave pushed displaced workers toward cognitive roles. This wave is targeting the cognitive layer directly. The refuge that absorbed previous displacement is being automated. The historical analogy being used to reassure people doesn’t hold in the way it’s being applied.

For enterprise leaders, the practical implication is this: whether or not displaced workers eventually find new roles, the demand consequence of the transition period belongs in your model. Reskilling optimism doesn’t eliminate the timeline mismatch. It just means you didn’t account for it.

What the Full Analysis Looks Like

The organizations I’ve seen navigate this well — the ones that captured genuine AI efficiency gains without creating the downstream problems I’m describing — ran a four-part analysis that most enterprise AI workforce discussions skip.

• First: demand exposure modeling. Not just cost reduction projections, but an honest mapping of your revenue base’s downstream exposure to middle-class wage contraction. This isn’t limited to your direct customers — it follows the value chain: who buys from your customers, and what drives their purchasing power? That exposure doesn’t show up in standard scenario planning, but it needs to.

• Second: workforce transition sequencing. The speed and order of headcount reduction matters as much as the final number. Where is human judgment genuinely irreplaceable in your value chain? What institutional knowledge leaves with the people you’re cutting? Which transitions can be managed through attrition and redeployment, and which can’t? Organizations that answer these questions before executing perform meaningfully better in the medium term than the ones that optimize for the speed of the announcement.

• Third: the trust variable. Your remaining workforce is watching. Your customers are watching. Your partners are watching. The reputational and cultural consequences of how you navigate AI workforce decisions will outlast the efficiency gain in both directions. I’ve seen organizations cut to a better multiple and lose the talent and customer relationships that justified the multiple in the first place.

• Fourth: competitive positioning for the recovery. The demand softening I’m describing is not permanent. Economies adapt and new equilibria form. The organizations positioned to capture the recovery are the ones that managed their human capital intelligently through the transition, not the ones that cut fastest and rebuilt from scratch. What does your organization need to look like on the other side of this, and are your current decisions moving you toward that or away from it?

This is a four-variable problem being solved as a one-variable problem in most boardrooms right now. That gap is where the real strategic risk lives.

The Silence is Deafening.

Most enterprise AI workforce discussions I’m aware of are structured only around a single question: “where can AI replace headcount?” This is not the wrong question, but only part of what needs to be considered.

The missing conversation is around the second-order consequence of these decisions on the broader demand environment, organizational capability, customer relationships, and competitive positioning. And are we prepared to manage those consequences, or are we going to discover them reactively in three to five years?

That conversation requires integrating workforce strategy, demand modeling, value chain analysis, and competitive scenario planning in a way that most enterprise planning cycles don’t naturally produce. It also requires someone willing to say the uncomfortable thing in the room: that the efficiency number your board is excited about is achievable, and the demand consequence might also be real, and both of those things can be true simultaneously.

In PE-backed environments especially, that conversation is hard to have because the exit thesis depends on a clean story. A clean story and a complete analysis are not always the same thing. The ones I’ve seen go sideways are usually the ones where that tension got resolved in favor of the story.

The Takeaway.

I’m not arguing against AI adoption. I’m arguing for a longer planning horizon and a more complete model.

The enterprise leaders who look back on this period with satisfaction won’t be the ones who cut fastest. They’ll be the ones who ran the full analysis, mapped the demand exposure across their value chain, sequenced the workforce transition thoughtfully, preserved what was valuable in their organizations, and came out positioned to capture the next growth cycle rather than be forced to rebuild from zero.

That’s harder work than a cost reduction initiative. It requires holding the tension between near-term pressure that is absolutely real and medium-term consequences that are equally real but less immediately visible. And it’s genuinely difficult to run that analysis from inside the same incentive structure that’s creating the problem.

If you’re working through AI workforce decisions right now and you want a thinking partner who will run the full model — not just the efficiency math, and not a pitch for a predetermined answer — I’m interested in that conversation.

Find me on LinkedIn. Tell me what you’re looking at. That’s where the useful conversation starts.

I thought I was fast. The data disagreed.

I’m 43, and I recently took my pitbike to the track convinced I still had it. The lap times said otherwise. Turns out I’m not the only one with a perception gap.

A 2025 METR study found that experienced developers using AI coding tools actually took 19% longer to complete tasks, while believing they were 20% faster. The gap reveals something important: agentic AI isn’t a plug-and-play productivity boost. It’s a fundamentally different way of working that requires deliberate adoption.

Since ServiceNow Knowledge 25, I have been experimenting with agentic AI both on my own and with clients. What follows is what I’ve found to work, what I’ve broken, and the lessons I’ve learned so you don’t have to.

The Opportunity

By late 2025, industry surveys suggested 80-90% of developers were using AI tools in some capacity (per Stack Overflow’s 2025 Developer Survey and Google’s DORA Report). But most are still on autocomplete. Agentic tools (systems that reason, plan, execute multi-step changes, and iterate on their own) are an entirely different animal.

What we’re talking about is the speed at which a team can experiment and innovate. Complex refactors that used to eat an iteration. Migrations that would’ve taken months. Architectural changes that were “too risky” because nobody had time. Agentic tools make these manageable.

Early adopters report release cycles speeding up significantly in some cases. Fewer bugs because agents enforce standards automatically. For a growing org, this means scaling without ballooning headcount. Your senior team punches above their weight. Agents handle CRUD apps, simple tools, and API glue code at dramatically lower labor costs than a decade ago. Some teams report saving several hours per developer per week. For a 50-person team, that’s meaningful.

But remember: when you automate a broken system, you’re only perpetuating the pain inherent in that system. The productivity can be achieved, but only when the workflow is dialed in first.

For the Executive

If you’re in the C-suite, here is the BLUF: agentic teams should augment your organization to speed innovation and time-to-value. They should not replace actual humans.

The companies getting this wrong are using AI headcount math: “If agents can do 30% of the work, we need 30% fewer developers.” That’s a mistake. The companies getting it right are using AI leverage math: “If agents handle the repetitive work, our developers can ship features that were previously out of reach.”

Junior devs (yes, we still need junior devs) onboard faster with AI as a supervisor. Automated deployments minimize downtime. Predictive maintenance catches issues before production. The outcome you should expect: more predictable quality, fewer post-release fires, lower maintenance bills, and a team that can take on more ambitious projects.

The question to ask your CTO isn’t “how many heads can we cut?” It’s “what could we build that we couldn’t before?”

The question to ask your CTO isn’t “how many heads can we cut?” It’s “what could we build that we couldn’t before?”

The Human Element

The biggest challenge to agentic AI adoption, same as with any change, is people. Specifically, the fear of being replaced.

Your senior developers have spent years building expertise. When you introduce a tool that can “write code,” their first thought isn’t “great, I’ll be more productive.” It’s “am I about to be automated out of a job?” That fear is rational, and ignoring it guarantees failed adoption.

The only solution is trust, and trust is built over time. This isn’t a technology rollout; it’s a change management exercise. Kotter’s framework applies: create urgency around the opportunity (not the threat), build a coalition of early adopters who can demonstrate success, communicate relentlessly that the goal is augmentation not replacement, and generate visible short-term wins that benefit the team.

The teams that succeed are the ones where developers see agents as tools that make their work better, not threats that make their work disappear.

How to Think About Tools

The market has split into two categories: IDE-first copilots that augment your editor line by line, and agentic systems that plan and execute multi-step changes with human checkpoints. Different problems, different tools.

Choosing which is best in your context is simpler than you may think. For daily flow (small tasks, quick completions, staying in the editor), you want a copilot like Cursor or GitHub Copilot. For complex work (large refactors, multi-file changes, architectural migrations), you want an agentic tool like Claude Code that can read your entire repo and execute a plan across dozens of files.

Don’t standardize on one tool. Smart teams use different tools for different jobs. The pattern I’m seeing work: Copilot for daily flow and peer reviews, Claude Code for planned refactors and migrations, Cursor for deep codebase work.

If your organization has strict data privacy requirements (fintech, healthcare, defense), tools like Tabnine offer air-gapped deployment. If you want agentic capabilities baked into CI/CD, GitLab AI and Apiiro are worth evaluating. If you have non-developers drowning in requests for simple automations, Claude Cowork (research preview, requires Claude Max subscription) can offload that demand to business teams directly.

Don’t chase features. Switching tools every month means you never build muscle memory or prompt engineering skills. Each switch resets your learning curve. Commit to one tool for 60-90 days. Learn its patterns. Evaluate alternatives after you’re proficient.

Security

This is where orgs suffer the most anxiety. That anxiety can lead to missteps. They treat agentic AI like another dev tool when it’s actually a new attack surface. Generative AI creates content. Agentic AI executes commands. Different beast.

Compromised agents mean data leaks or malicious behavior. Dependency risks are huge: outdated libraries, unvetted code.

The threat landscape includes prompt injection (adversaries force agents to execute malicious commands), cross-agent privilege escalation (low-privilege agents manipulated to trick high-privilege agents), non-human identity compromise (hardcoded API keys giving attackers months of access), and MCP server vulnerabilities (shadow agents linking LLMs to corporate databases without oversight).

Mitigation: Zero Trust for Agents. Every tool call and API request verified, logged, scoped. Think of agents as digital insiders. They can cause harm unintentionally or deliberately if compromised. Establish governance: ownership, monitoring tied to KPIs, escalation triggers, accountability standards. Treat LLM-generated code as untrusted. Sandbox everything. Enforce pre-commit hooks, automated scans. Least-privilege, no blanket access.

Code Quality and Maintainability

AI-generated code quality varies wildly. Some companies see huge gains, others see nothing. The difference is process discipline.

The “vibe coding” backlash is something you can’t ignore. There’s an “AI slop” crisis in production codebases and it is creating problems greater than any low-cost offshore dev shop of the early 2000s. One researcher documented a junior engineer merging 1,000 lines of AI-generated code that broke a test environment. The code was so convoluted that rewriting from scratch was faster than debugging.

Treat AI code like contractor code. Review it like it came from someone who doesn’t know your codebase. AI generates plausible code. It doesn’t understand why your team made specific architectural decisions.

Automate testing. Non-negotiable. The tools can run tests and iterate. Claude Code can run your test suite and fix failures before presenting code for review.

Don’t skip planning. Make the agent generate an architectural plan and review it before code gets written. This will catch misunderstandings before they become debt.

Watch for over-engineering. Agents go too far. One team reported Claude building a database-backed session management system when JWTs would’ve sufficed. Review for simplicity, not just correctness.

Studies suggest agent-generated code can spike complexity significantly and increase static warnings. Documentation matters more than ever. Humans carry mental context about why decisions were made. AI doesn’t. Require inline documentation explaining the reasoning, not just the what.

A Framework for Scaling

Once you move past pilots into production agentic systems, you need structure. A three-layer framework helps:

Layer 1: Model Routing (The Traffic Cop). Not every query needs your most expensive model. Route simple questions to fast, cheap models; route complex reasoning to powerful ones. This is how you control costs while maintaining quality. Without routing, you’re either overpaying or getting bad results.

Layer 2: Agent Orchestration (The Team Manager). Some tasks are too big for one agent. Orchestration breaks them down, assigns parts to specialized agents, and combines the results. One agent searches, another calculates, a third summarizes. The orchestrator keeps it cohesive. This is how you handle complex, multi-step problems.

Layer 3: Context Patterns (Memory and Flow). Agents forget. Context patterns are reusable templates for handling memory, conversation history, and reasoning flow. Chain-of-thought prompting. Memory buffers for key facts. Structured inputs that guide better outputs. This is how you make the system feel coherent over time instead of starting fresh every interaction.

The layers stack: routing picks the right tools, orchestration assembles them into a team, context patterns make the whole thing coherent. Teams using this framework report significant reductions in refactoring time.

The Cost

Your API bill is just the tip of the iceberg. I learned this the hard way running experiments in my office. What started as a few hundred bucks a month in API calls turned into weeks of engineering time spent optimizing prompts, building infrastructure that could actually scale, navigating security compliance reviews that nobody budgeted for, and standing up monitoring stacks to figure out why things were breaking. That “reasonable” API bill became unreasonable fast. DX research and industry reports consistently show total costs running 5-10x higher than what shows up on the invoice.

The cost iceberg: Your 200-token demo becomes a 1,200-token production interaction once you add conversation history, user profiles, and system state. Demo failures become complex recovery workflows. Your token math assumes perfect inputs; production assumes everything breaks. Your systems weren’t built for AI, and AI wasn’t built for your systems. “Simple” integrations turn into building APIs that don’t exist.

Subscription tools (Copilot, Cursor, Windsurf) run $10-40/seat/mo. Predictable. API tools spiral fast: an agent on complex tasks can consume 5-10M tokens monthly, costing $45-150/mo for Sonnet on a single workflow. Multiply across a team and things can get expensive fast.

Optimization strategies: Smart prompting can cut token usage significantly (PromptLayer research shows 40%+ reductions with concise prompts). Model routing (cheap models for simple tasks) can save up to 85% on certain workloads according to LMSYS benchmarks. Caching reduces redundant calls. Open-source frameworks (LangChain, LlamaIndex) lower costs vs proprietary options but require more expertise.

For 10-20 developers, budget $200-800/dev/mo in subscriptions, with total business cost at 5-10x that when you add integration, training, and maintenance. Enterprise implementation runs $50K-200K for rollout.

The economics still work. A skilled US developer costs $100-150/hr fully loaded (salary plus benefits, taxes, overhead). A $2K/mo AI investment delivering 3x on routine tasks pays for itself quickly.

Risks

Agentic AI isn’t perfect. Without oversight, agents spit out bad code that takes longer to fix, or nuke your production DB. (Looking at you, Replit horror stories.) Stack Overflow’s 2025 Survey found only 3% of developers “highly trust” AI output, while 46% actively distrust it. Maintainability is a top concern: DX research shows code quality impacts vary wildly by organization.

Hallucinations and errors: agents “vibe code” without understanding, creating vulnerabilities. Over-reliance degrades dev skills. Diminishing returns if your team’s already saturated with AI IDEs. Accountability shifts to humans overseeing agents. Track provenance or you’re the one holding the bag.

Treat it like buying a used sauna on Facebook Marketplace. Great deal if it works. Inspect it first.

What to Do Monday Morning

Build a team of two engineers. Give them 30 days to see what an agentic team can deliver within your organization’s architecture.

1. Map the process: what did they build, how did they build it, what did the agents actually do?

2. Map the bottlenecks: where did agents struggle, loop, or produce garbage?

3. Map the wins: what shipped faster than it would have otherwise?

4. Map the friction: what was harder than expected, what required workarounds, what annoyed the team?

After 30 days, look at the results. Consider improving the outlined friction. Decide if the juice is worth the squeeze.

One caveat: if your value streams, business architecture, or technical architecture are already a mess, fix that first. AI doesn’t fix broken systems. It automates them. You’ll just experience the same pain you already have, faster and more often.

That’s it. Not a massive transformation initiative. Not a tool procurement process. Two engineers, 30 days, and an honest assessment.

Closing

Agentic AI is a fundamental economic shift in software development. Not a productivity tool. The orgs capturing value treat this as an operating model change, not a tool purchase.

The cost of not leveraging this is greater than you think. Ask why your competitors are adopting it, and what you’re missing.

The era of “magic” AI coding is over. The era of managed, verified, economically rational AI engineering has begun.

Sources

METR Study: “Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity” (arXiv:2507.09089, July 2025). metr.org

Stack Overflow: 2025 Developer Survey. survey.stackoverflow.co/2025

Google DORA: 2025 State of AI-assisted Software Development Report. cloud.google.com/devops

Anthropic: Claude Opus 4.5 System Card and SWE-bench Verified Results (November 2025). anthropic.com

DX: AI-assisted Engineering: Q4 Impact Report (2025). getdx.com

LMSYS/RouteLLM: Model routing research (ICLR 2025). github.com/lm-sys/RouteLLM

PromptLayer: “How to Reduce LLM Costs” (2024). blog.promptlayer.com

The big news is… that I’m not ready to share the big news yet. 😊

It’s been nearly three months since my sabbatical and my subsequent decision not to return to my role at SAI after the new year. At the time, I thought I knew exactly what I was going to do, but the more time I spent with Jenelle and Ford, the less clear the path forward became.

I’m currently considering three different options. Each is great in its own way, and I anticipate making a decision within the next two weeks.

It’s a complicated world out there right now, but one that’s also insanely fascinating.

No matter the decision, expect some cool new events, writing, and nerd-media to drop in the very near future.

Stay tuned.

Cool House Projects

With a little extra time on my hands, I’ve had the opportunity to take on some house projects that I had been putting off. Here are the details on the “big three”.

The Garage.

The garage is my favorite room in the house. Yes, you read that right.

I spend a lot of time out there. Between tinkering with Ford’s dirt bike (yes, you read that right too), working on the cars, and hitting the gym, I spend a ton of time out there. Being the motor-head that I am, I’ve always wanted a cool garage—and I finally built it out.

I went with a dark grey flake polyaspartic on the floor and black epoxy on the upright cinder blocks. The floor came out amazing, thanks to Signature Concrete Coatings. I have no idea why I waited so long.

I had the ceilings painted black by Mathias Painting, and Hexglow lights installed by Jado Customz. Everything looks better than I anticipated. The garage has turned into such a cool space.

Paint.

Ever since finishing Ford’s room, Jenelle and I have been really into color washing. Last year we painted our parlor room navy and immediately wished we had color washed the space instead. We also knew I didn’t have the skills to do it properly, so we leaned on our friends at Mathias.

There’s still a lot of work to do in the room, but it’s off to a great start. Next up: art, chairs… actually, we’ll probably turn the whole thing over to our friend Kelly at GoldLeaf Interiors.

Next up was the bedroom. There wasn’t anything wrong with it, aside from the fact that I royally screwed up a wall when touching things up. So, while fixing that, we decided to do a little extra. We lightened the color of the room and did a modified color wash. We painted the trim and doors the same color as the walls, and decided to leave the ceiling tray white to help keep the room feeling “tall.”

The Cars.

The last of the big-three projects was to have the cars ceramic coated. In our family, we only buy black cars. If you’ve ever had one, you know how great black looks when clean—and how horrible it looks when dirty. Additionally, you’re probably more familiar with swirl marks in your paint than most other car owners.

With my car being six years old, Jenelle’s car having soft paint typical of a GM product, and our desire to hang on to these rides for several more years, we decided to invest in a quality ceramic coating service.

Daniel at Perfect Finish did a three-step paint correction on each vehicle, applied the Modesta BC-X 5-year coating, and added a protective film to my windshield.

Both rides came out great. My 6-year-old Mercedes looks better than anything I’ve seen on the showroom floor recently.

If you care about the finish on your car, ceramic is a must (just be sure you care for it properly).

The Adam Mattis Show

I have built two pretty solid podcasts over the years. My first, The Everyday Executive, which I launched after Orion’s Kin was cancelled. I learned a lot about production during that experiment. I shut EdE down after I inherited Agile Amp’ed while at Accenture. It was pretty popular in our community, but more curated than I would have preferred.

Where I really hit my stride with podcasting was when a rogue team and I took Business Agility Now to a top 5% podcast on iTunes and Spotify while at Scaled Agile. I had a lot of freedom in terms of style with this one, but I was still constrained from fully expressing my natural voice.

As things changed last year at SAI, I put the podcast on the shelf after we published our last episode in June.

Now that I’ve created some separation and had time to reflect, I’ve come to realize how much I miss recording. Beyond that simple realization, I’ve learned that what I miss most is the opportunity to get to know interesting people and their stories—and to help give people great content to promote whatever it is they’re doing.

So, I’m getting back to it. And I’m doing it my way, in my own voice, with The Adam Mattis Show. I’m excited to host a show where I can be myself: a little punk rock, a little moto, a little nerd, and a lot of interesting people.

I intend to release 2 episodes per week—one car-thought and one long-format. The first will drop on February 2 with a hotly anticipated guest: my wife, Jenelle. This one will be fun, funny, and uncomfortable. I’m much more excited about it than she is.

Please subscribe on your favorite platform via the links at TheAdamMattisShow.com. I will publish on Spotify, Apple Podcasts, and YouTube.

A Business You Should Know:
The Balcerzak Group

Annie Balcerzak is a badass.

In the world of real estate, it can be hard to stand out. Annie and her team at The Balcerzak Group don’t have that problem. Their style, their approach to business, and their approach to people are second to none. The driving force behind that is Annie.

If you follow Annie on Instagram, you’ll quickly see a lot of her personality—and also her deep knowledge about property maintenance, staging, rehab, and more. They really are in a class of their own.

With all of that said, what is most remarkable about Annie is her approach to people, business, and life. We are aligned on so many perspectives, and I find myself learning a little more from her almost daily.

If you’re in the Baltimore metro area and need a real estate partner, check out The Balcerzak Group.

Oh, and you may catch her on an upcoming episode of The Adam Mattis Show too.

In Closing.

My sabbatical helped me realize that I needed to make a change and focus on my little dude, Ford. The time away made me understand that I had to shift things so I could spend more time with him.

I’m excited for the next step in my career, and I’ll be able to share those details soon.

But for now, please take a lesson from what I’ve (again) learned the hard way: family first. You can never reclaim time, so protect it with everything you’ve got. Also, if you ever find yourself in a situation where you feel like you can’t be yourself, consider that it may be time to change your circumstances.

Until next month!
-Adam

The engagement season of a relationship is peak euphoric happiness, and I don’t think enough couples take full advantage of it.

Before you get defensive, hear me out.

Do you remember your first relationship? Probably in high school or college. Do you remember how it felt? The butterflies, the thrill of seeing that person, holding their hand, the longing to spend every waking moment together?

Most people call this “puppy love.” Some lucky ones turn it into a lifelong relationship, but many don’t, and they end up holding those early feelings as the gold standard for what “right” should feel like in a relationship.

Then life happens. Dating in your 20s is full of exploration, meeting new people, figuring yourself out, and cycling through a lot of relationships. Inevitably, people put up walls and become jaded. This isn’t unique to men or women. I’ve felt it myself and watched it happen to plenty of my (female) friends too.

If that pattern carries into your 30s, things get even harder. You’ve become independent and comfortable being alone. Relationships feel riskier because you’re weighing the safety of your single life against the vulnerability of letting someone in.

Dating apps only make it worse. One weird feeling, one fight, one “not sure?” and thousands of other options are just a swipe away.

Despite all of this, most people eventually find someone they’re willing to risk it all for. The relationship blooms, someone pops the question, and, if it’s truly right, those early feelings of first-relationship euphoria come flooding back. You feel safe and confident about the future. You’re excited, your person is excited, and everyone around you is excited for you. In this sweet phase, before wedding planning kicks in, a couple genuinely enjoys each other. Everything feels easier, the guard is down, the air is sweeter, and life just feels great. (If that’s not the case, that’s a different conversation.)

Then wedding planning begins. Trust me: the stress and pursuit of perfection is never worth it. The day will be beautiful even if the timeline, seating chart, and flowers aren’t perfect. People will have fun. The whole thing will fly by in a blur. Don’t waste a single minute of your engagement stressing over things that won’t matter the second the wedding day starts.

After that? Life.

Am I saying happiness fades after marriage? Absolutely not. But it does change. Life changes it. Careers, building a home, growing individually and together, starting a family, raising kids, moving. It all reshapes what happiness looks like and how it feels.

Now happiness feels like watching your partner succeed, like solving problems and overcoming adversity together. It feels like building a safe place for your family. And time passes faster than you’d ever want. Some days you grow closer, some days you drift a little, but the commitment to always finding each other keeps everything moving forward.

I love my wife and I love my family. I couldn’t be happier with where we are.

So why am I writing this?

Yesterday I was walking around North Hills in Raleigh with Jenelle and Ford when I saw a couple get engaged. In an instant, I was transported back to proposing to Jenelle in Ebensburg in 2018. Almost immediately after the ring went on her finger, I heard someone ask, “When’s the wedding?” I wanted to run over and shout, “No! Enjoy every single minute of this. Don’t rush!”

But that would’ve been weird, so instead I’m writing this.

If you’re in that magical engagement season right now, please don’t let it slip by. Savor the butterflies, the ease, the pure joy. Those feelings don’t disappear after the wedding; they just evolve into something deeper and more enduring. But this particular sweetness? It only lasts for a little while. Make the most of it while it’s yours.

If you opened this email based on the subject and hope of some drama, I’m sorry to disappoint. But, the related blog post is something that every guy should read.

With that out of the way, it’s time for some life changes and a fresh start.

Something shifted in me on the flight home from my 8th trip in 9 weeks (September through November). I realized I wasn’t just jet-lagged from bouncing between APAC → ET → EMEA → MT → ET time zones.
I was physically, mentally, and emotionally exhausted from the last three years.

From losing our daughter, to fertility treatments, health scares, welcoming a newborn, and relentless pressure at work, something inside me had finally broken.

As I write this, I’m two weeks into a six-week sabbatical, asking myself one question: “What’s next?”

I know I need to get back to building and helping others.
Back to doing the things that make me feel alive.
Back to being the patient, focused father and husband my family deserves.

The exact shape of all this is still coming together, but I expect to have a much clearer picture to share when January’s newsletter lands in your inbox.

If you want to offer some unsolicited feedback or advice, feel free to hit “reply” and lay it on me. I want to hear all of the ideas!

Until then, I hope you have a wonderful Christmas and get to spend as much time as possible with the people you love most.

Cheers to ‘26!

-AM

Men: Chase Adrenaline - Save Your Marriage

I am an adrenaline addict, and over the years I’ve lost a lot chasing the rush I used to get from combat, motocross, and starting companies… except recently, the only thing I was hunting was the next work crisis.

Work travel. Bills. PE pressure. Fertility hell. Dramatic life changes. Somewhere in there the fire went out and I didn’t even notice until I was sitting in a dark garage staring at empty space that my motorcycles used to fill.

A lot of men are one mid-life itch away from an affair, a heart attack, or just checking out completely. Why? Because we stopped feeding the beast and started numbing it instead.

If this feels too close to home, have a look at THIS post and start making some changes.

If you’ve got a family, a wife, or a pulse, you need this one.

Then forward it to the brother who’s quietly dying on the couch.

What I Read in 2025

From Jack Carr to Brown Bear Brown Bear and everything in between, I have put together a list of everything I read in 2025. It’s crazy to think that I’ve been building my “what I read” list for almost ten years now. This years list is a little thin, but there is still some gold.

Have a look HERE!

Savannah Homecoming

Jenelle and I have been slipping down to Savannah, Georgia for quick escapes ever since we moved to Raleigh in 2020. For me it always feels like a bit of a homecoming. I lived there from 2004 to 2009, first with the Army and then at VeriSign after I got out. I even owned a rental property in the area until 2021.

What I love is how Savannah seems to have grown up right alongside me.

Early days with the boys we never left the River Street to Broughton Street corridor. Life revolved around bars and meeting SCAD girls. That was it.

Later when I was working on Oglethorpe I suddenly cared about the city’s history, quiet cafés, and networking at First City Club (RIP).

Now when we come back with Jenelle and these days with Ford we’re drawn to completely different corners. River Street is still River Street, but the old west-end industrial lots have turned into slick Marriott properties and our favorite hotel The Thompson sits on what used to be marsh. The whole place has matured, picked up great restaurants and shops, and traded a lot of drunk soldiers for strollers (Fort Stewart isn’t what it used to be).

These days we’re the ones pushing a stroller through the squares, lingering in Forsyth Park, and hunting down the quiet spots. Savannah’s still got its soul. I’d like to think I still have mine.

We might move back one day. For now it’s our favorite place to escape.

While we’re at it...

Monday, December 8th marks my 20th “Alive Day”, and 20 years since Kevin Smith gave up his life to save mine.

That’s all I have to say about that.

A Business You Should Know: Osteria G

Joe and Ashley have built the best Italian restaurant you’ve probably never been to.

And now they’re also building an Italian deli and an NYC-caliber steakhouse right here in Holly Springs.

What makes Joe, Ashley, and OG so special isn’t that they set out to be best-in-class restaurateurs, it’s that they set out to be good people and provide a killer experience, and that’s exactly what has made them the best in the area.

When you walk into OG, you’ll be greeted by a great hostess and a big smile from Mack working behind the bar. Next, you’re almost guaranteed to run into Joe or Ashley, who will welcome you like you’re their oldest friend.

Then the real experience begins: the food, the wine—everything is perfect. But there’s one little detail that lets you know this isn’t your grandma’s Italian restaurant: the playlist, packed with songs your mom probably wouldn’t let you listen to growing up.

This place and the people behind it are truly special. The food is outstanding.

Make the trip, you’ll be glad you did.

Osteria G
OG Deli

In Closing

It’s been one hell of a 2025, and I’m excited to make a change. Exactly what that looks like is still a little TBD, but I’m ready for it.

I hope everyone has a wonderful Christmas season. Take a minute to step back, look around, and feel real gratitude for all the goodness in your life. Despite all the chaos swirling around us, it really is a damn fine time to be alive.

Cheers,
AM

I scrolled past an Instagram reel the other day that made more sense than I expected. The creator laid it out plain: men are wired to hunt, compete, conquer, and win. We all have our own version of this. For me it was hosting a hunting TV show, starting businesses, building, competing, and winning. For others it’s learning, gaming (I’m not a fan), DJ’ing, adventure races, cars, or anything else that defines who a man is.

Then life happens. Marriage, kids, mortgages, corporate politics, pressure. That fire gets buried under routine, responsibilities, and resentment.

Single-Adam spent a ton of time on the road: new cities, new friends, new hunting spots. It filled my cup. Even after Jenelle and I were married, we kept that lifestyle, just did it together. When the pandemic killed travel I went back to my roots and got on a motocross track again. It felt good to wrench on a bike and pin the throttle.

Then work pressure ramped up. Jenelle and I lost our daughter. We fought through years of fertility treatments that finally gave us Ford. Then we had a newborn. And somewhere in all of that… I lost myself. I was living to work and survive the pressure. I lost my outlet, and I lost joy.

There were nights I’d sit in the dark garage after Ford went down and stare at where my dirtbikes used to be. I bought another new car. I felt empty. That’s when you know you’re in trouble: when the things that used to light you up feel distant and out of reach.

Many guys bury it. Misery, weight gain, food, booze. And some guys miss the rush so badly they go looking for it in the worst possible place: other women. The reel was right: the affair usually isn’t about the other woman. It’s about trying to feel young, alive, and free again, like time-traveling back to the man who didn’t have to adult 24/7.

There’s truth in the evolutionary story (men’s infidelity tends to be about novelty, women’s more about emotional connection), but biology is not destiny. Failing to give that energy a constructive outlet is, in my opinion, the ultimate form of weakness.

As a former competitive BMX rider, motocross racer, mountain biker, and a guy who got addicted to the rush of combat, I know the void better than most. When the helmets come off and the deployments end, the emptiness can be overwhelming. The same wiring that kept me alive on the streets of Baghdad or convinced me to send a 100-foot triple still tastes like dust and cordite twenty years later. That switch never turns off. It just waits for orders.

Right after I retired from the Army I was a train wreck, terrible in relationships, a reckless friend, and absolutely dangerous with Terry on our “blood-money” Harleys. How either of us are still breathing is a mystery. Most guys don’t go that extreme, but too many still answer that scream with secrecy and self-destruction instead of strategy and strength.

There’s a better way. And it will literally save your marriage, your sanity, and your soul.

1. Give the beast an outlet
   You need something that spikes your heart rate, demands focus, and leaves you physically spent.
   • Brazilian Jiu-Jitsu (getting choked unconscious by another grown man weirdly heals the soul)
   • Hunting in the back country
   • Track days, enduro rides, or throwing a barbell around until you see stars
   • Ice baths, obstacle races, ultras, whatever reminds your nervous system you’re still in the game

   These aren’t “nice-to-haves.” For me they’re maintenance, same as brushing my teeth. Over the years I’ve chased trail running, Ironmans, mountain biking, road biking, rock climbing, and travel. If you think you “don’t have time,” remember I trained for an Ironman while working 70-hour weeks and traveling constantly. You don’t find time; you steal it from anywhere you can. It’s not easy or comfortable, but it’s worthwhile.
   

2. Build your tribe
   Lone wolves die young, both literally and figuratively. Find men who train hard, have aligned values, and want to get better. The garage gym crew, Thursday-night BJJ, Bible study, the dawn ruck group; these guys become your accountability, your therapy, and often your best friends. Real masculinity isn’t suffering in silence; it’s choosing to improve your life, and it’s always easier together.

   I’ve buried too many friends who never found their five guys. Suicide rates for men 35–54 are obscene for a reason. Your tribe isn’t a luxury; it’s your insurance policy.
   

   Right now my tribe is basically me and Eric once a week. I need to do better. We all can. Here’s the dirty secret: most of us are one text away from starting one. Today, I am sending four guys: “Saturday 0600, 5-mile ruck, coffee after. No excuses.” That’s how tribes are built, one awkward text at a time.
   

3. Say the hard thing out loud
   The strongest man in the room is the one who can look his wife in the eye and say, “I love you and the kids more than anything, but some days I feel like I’m disappearing. I need to go punch myself in the face at the gym a few nights a week so I can be fully here when I come home.”
   

   Vulnerability isn’t weakness. Most women would rather buy you a gym membership than bury you at 50 from a stress heart attack, or lose you to someone else.
   

   A few months ago I felt us slipping, so I sat Jenelle down and told her exactly how hollow I was feeling. I asked her to start therapy with me, not because we were broken, but because I never wanted us to get there.

Your primal wiring isn’t the enemy. Neglecting it is.

You have a responsibility, to your family and to yourself, to be the best man you can be. Do good work, do hard things, invest in yourself, and be brutally clear about your needs. Most families don’t blow up from one big betrayal; they erode from years of laziness and unspoken resentment.

Ford is ten months old. Every time we walk into the garage he makes a beeline for his balance bike or my old dirt bike and grabs the handlebars. He’s never seen me ride, and he can barely stand on his own bike, but he already knows those machines are something special. I don’t want his first memory of Dad to be a burnt-out guy scrolling his phone on the couch. I want him to remember a man who still had dirt under his nails and fire in his eyes at 70. That story starts with the choices I make this year.

So here’s what I’m doing: I rebuilt my home gym, bought a sauna, and I’m building my garage gang. I’m shopping for a new motocross bike. And come January I’m walking away from the job that was no longer serving me. It’s time to get back to building, creating, and making the world better for my son.

I’m not saying you have to quit your job, but you do have to do something. Your older self will thank you.

Chase the rush. Build the brotherhood. Speak the truth.
Do these things relentlessly and you won’t need to chase ghosts from your past, because your present will feel alive again.

And your family gets to keep the man they fell in love with.

2025 was the year of Ford: crazy days, lots of supported steps, and roughly 4,000 readings of Brown Bear. Travel, work, and sleep deprivation meant the grown-up reading pile grew slower than usual. Still, I carved out enough pages for a handful of books that actually stuck. Some challenged me, some entertained me, a couple changed me.

Here’s the shortlist.

• Under Siege: My Family’s Fight to Save Our Nation

  I make a point of reading books by people who hold power; a key player in one of America’s most influential families offers a perspective worth understanding. The sharpest, most non-partisan takeaway was his insistence that the people closest to a problem are almost always the ones best equipped to solve it.

• 107 Days

  A minute-by-minute look at the whirlwind of a presidential campaign’s final stretch. The clearest non-partisan takeaway: real leadership isn’t about having all the time in the world; it’s about making the right calls when every hour counts and the pressure never lets up.

• Cry Havoc: A Tom Reece Thriller

  I am a huge Jack Carr fan. Though, I did not expect to enjoy this book as much as I did. Being a GWOT Veteran, Vietnam has always felt a bit disconnected from my own experience. Carr corrected that perception with his story of the father of his main character, James Reece. If you haven’t reach Carr’s previous books, don’t start here. Begin with The Terminal List and read the series in order.

• The Coming Wave: AI, Power, and Our Future

  This one is a bit of a horror story, written by one of the people who built the monsters. The next decade of AI (and synthetic biology) will be the fastest, most consequential technological leap in human history, and right now literally no one has a workable plan to keep the benefits while containing the risks.

• Trump: The Art of the Deal

  I was surprised by how much I didn’t know about Trump’s involvement in the modern history of NYC: the Grand Hyatt, Wollman Rink, Trump Tower; he really did reshape huge chunks of the Manhattan skyline in the ’80s. Love him or hate him, the guy has an almost obsessive eye for leverage, timing, and turning constraints into selling points, lessons any builder, negotiator, or entrepreneur can pocket regardless of politics.

• The Laws of Human Nature

  A 600-page dissection of why people do the stupid, brilliant, cruel, and heroic things they do. Once you start seeing the predictable patterns (narcissism masks, envy triggers, groupthink spirals, the shadow side everyone denies), you can’t unsee them in yourself or anyone else. It’s like getting night-vision goggles for human behavior.

• The #1 Dad Book: Be the Best Dad You Can Be in 1 Hour

  Yes, the title is pure clickbait and the book is basically a pamphlet wearing a hardback costume. Still read it in one sitting on a flight. Surprisingly solid reminder that 90 % of great fatherhood isn’t grand gestures; it’s showing up, staying calm when you want to yell, and asking one good question a day.

• The Technological Republic: Hard Power, Soft Belief, and the Future of the West

  A manifesto that argues the West’s real power now lives in code, networks, and engineers, not in parliaments or legacy institutions. If democratic governments keep treating frontier tech as something to regulate instead of something to wield, the next century will be written by whoever does master it first.

• Attract or Repel: Seven Keys to Magnetize Your Company

  A practical sales-culture book that lives up to its binary title: every choice you make as a leader either pulls the right people (and customers) toward you or pushes them away; there’s no neutral. The reminder that culture isn’t a poster on the wall; it’s the cumulative magnetic charge of thousands of tiny daily decisions.

• Do Hard Things: Why We Get Resilience Wrong

  A former Olympic coach tears apart the grit-is-everything myth and rebuilds resilience: real toughness isn’t pushing through pain while pretending you feel nothing; it’s learning to listen to the discomfort, respond instead of react, and choose the hard thing because it still aligns with who you want to be.

• Brown Bear, Brown Bear, What Do You See?

  This one is Ford’s #1 favorite book (and his cousin Luca’s, too.) I’ve read it no fewer than 500 times in the last 10 months.
  

• The Very Hungry Caterpillar

  Ford’s second favorite book, and I’m pretty sure it was my sisters #1 way back in the day.
  

• Goodnight, Goodnight Construction Site

  And finally, my #1 favorite book to read Ford. Though, he seems to be more interested in eating it than reading it.

That’s the 2025 roster. Fewer books than past years, but every one earned its keep, either by making me think harder, parent better, negotiate sharper, or just laugh at 2 a.m. while a toddler demanded “Brown Bear” for the 47th time.

Cheers!

I’m writing this from hotel room number... honestly, I’ve lost count. What I haven’t lost count of is the number: 79. That’s how many nights I’ve spent in hotels since February, when my son Ford was born.

When I was planning out 2025 in late 2024, penciling in conferences, partner meetings, and customer engagements, 79 nights away didn’t register as significant. It was just work. Just the job. For the last 20 years, I’ve been a “road warrior”; a badge I wore with some pride, actually (along with my Delta Diamond and Hyatt Globalist status’). The travel was just what I’ve always done. To be honest, compared to years past, 79 nights was not that many. Travel is part of the deal. And honestly? I didn’t have much to miss at home.

Then Ford arrived in February.

Here’s what I didn’t know having a kid: time doesn’t just speed up, its value completely changes. A week used to be a reasonable unit of time. Now? Ford learns something new seemingly every other day. He’s discovering his bike. He’s starting to walk - mind you, he skipped crowing entirely. Each of these moments and the “firsts” is happening on a timeline that doesn’t care about my Q3 objectives or my travel schedule.

I missed his first real laugh. I heard about it on a FaceTime call from a hotel in The Netherlands.

The math is brutal when you actually do it. Ford is almost 10 months old as I write this. I’ve been gone for 79 nights. That’s nearly 30% of his entire life so far. Thirty percent of a life I can’t get back, can’t replay, can’t experience in retrospect no matter how many photos and videos get sent my way.

I kept telling myself the travel was for him: building security, creating opportunities, establishing the foundation for his future. And there’s truth in that. But there’s also truth in the fact that he doesn’t need a foundation 20 years from now nearly as much as he needs his dad right now, today, this week.

The partnerships world has taught me a lot about value creation. We talk constantly about mutual value, about understanding what each party needs from a relationship. But somewhere along the way, I forgot to apply that same framework to the most important partnership of my life: the one with my family.

Jenelle hasn’t complained (too loudly). She’s been incredibly supportive, as she always is. But I’ve seen the exhaustion. I’ve seen her handling everything while I’m off “building the ecosystem.” And I’ve started asking myself a hard question: What ecosystem am I actually building? Because the one at home, the one with Ford discovering the world, with my wife being an absolute superhero, with moments that only happen once; that ecosystem needs me too.

So here’s what I’ve realized, sitting in hotel room number whatever-this-is: Time is the only truly finite resource we have. You can make more money. You can rebuild professional relationships. You can even switch careers. But you cannot make more time. You can’t get back the nights you missed, the mornings you weren’t there for, the small moments that seemed insignificant until they were gone.

I don’t have regrets, and I don’t live in the rearview mirror. I’ve done meaningful work this year, work I’m proud of. But its time to recalibrate. I’ve recognized that the metrics I’ve been using to measure success might be missing the most important data points.

Which brings me to the announcement part of this post: I’m taking a sabbatical through the end of the year. Six whole weeks without email or Slack to focus on my family.

Scaled Agile has always been incredibly supportive of work-life balance. I’m fortunate to work for an organization that not only offers PTO but actively encourages people to use it. So I’m taking them up on it. I’ll be stepping back from day-to-day responsibilities, from travel, from the constant motion of the last year.

I’m going to be present. I’m going to watch Ford discover things. I’m going to give Jenelle the support she has more than earned. I’m going to remember what it feels like to not live out of a Hyatt (though, I do love a good Hyatt property.)

After the new year? I’ll be making some different decisions. I don’t have all the answers yet about what that looks like professionally, but I know the questions I’m asking are the right ones.

Here’s the lesson, if you’re looking for one: Prioritize your family. They need you. Not the idea of you, not the paycheck version of you, but actually you. And take your PTO. All of it. The work will be there when you get back. I promise you that. But your kids won’t be the same age. Your partner won’t get those days back. And you won’t get another shot at these specific, irreplaceable moments.

I’ve spent 20 years being really good at showing up for work. Time to be really good at showing up at home.

See you in the new year. I’ll be the well-rested guy who actually knows what his kid’s laugh sounds like in person.

This year has been a chaotic combination of maximum stress, maximum joy, and not a lot in between.

Every moment with Ford since his February arrival has been perfection. Somehow none of the crying, sleepless nights, or moments of helplessness have actually felt stressful. I’ve stayed calm and focused on giving him what he needs, even when I can’t fully meet the moment. (Let’s just say that Jenelle and I are equipped differently.)

Ford brings me a peace and joy I never expected, and I’m deeply grateful for him.

Everything else, however, has been far less peaceful. The political, social, and economic environments are unrelenting, and that tension inevitably spills into business. At work, we’re launching a new line of business, which is both thrilling and intense. Things are moving fast, and it takes constant discipline to invest time, mine and the team’s, in the right places.

The year has felt disorganized.

That changed the moment I arrived in Japan. To be honest, it was exactly what I needed. Japanese culture radiates intentionality: appreciation of the moment, immersion in experience, respect for others, and graceful transitions from one state of being to another.

From small acts of gift-giving (omiyage), to the minimalist design of spaces, to the quiet respect for presence, to the oshibori before a meal, Japan has a way of encouraging stillness. It invites you to compartmentalize stress and be fully in the moment.

One experience captured that spirit perfectly.

When my Uber driver dropped me off at Haneda Airport, he handed me a small gold packet and suggested that I take it after a meal. It turned out to be Pabron Gold A, an over-the-counter cold medicine that many Japanese people keep on hand. It was such a simple act of omotenashi, hospitality through care and foresight. He didn’t know me, yet he wanted me to travel well. It wasn’t grand, but it was deeply human.

That brief exchange reminded me that real kindness is often practical, quiet, and rooted in empathy. You see it everywhere in Japan: in the folded towel, the carefully wrapped gift, the respectful bow. Every action serves a purpose. Every detail communicates care.

Here are a few things I’m taking home from my first trip to Japan:

What I’m Taking Home

Oshibori

Friendsgiving is going to look different this year. I plan to greet everyone with a warm oshibori, a simple cloth that in Japan symbolizes transition, hospitality, and care. It’s a reminder to pause, reset, and leave stress at the door. We could all use that kind of intentional reset before sharing a meal.

Spa

I’ve always known that I should prioritize stretching, massage, sauna, and cold plunge. This week proved that I actually need to do it. After making time for that routine four times in one week, I feel like a new man. The Japanese onsen and sento culture reminded me that caring for the body is a form of mental clarity.

Purposeful Design

We have an infant, so some areas of the home will always be a bit chaotic. But others don’t need to be. We also don’t need to fill every space. In Japan, the concept of ma, the beauty of empty space, teaches that absence is as meaningful as presence. The built-ins in our living room don’t need to be full. In fact, they shouldn’t be. I’ll do better.

Gardening

We have beautiful landscaping, but I’ve treated its care as a chore. I’m reframing it as an opportunity to focus and reset. I’ll still let the robot mow the grass, but I have a date with our trees and pruning shears soon. The Japanese principle of wabi-sabi. finding beauty in imperfection and transience, feels like the right mindset for this.

Morning Intention and Evening Journaling

The best way to organize your thoughts is to write them down. I tell others this all the time but rarely follow it myself. That’s changing. Beyond journaling in the evening, I plan to start each day by setting an intention. Whether through prayer, reflection, or quiet meditation, I want to begin the day grounded rather than reactive.

What I need to stop doing is checking email or Slack before my eyes are even ready to focus. The morning cortisol spike that comes from instant urgency helps no one.

Now, it’s Your Turn

Travel has a remarkable ability to inspire introspection. If you’ve never left your home country, buy a ticket. Go somewhere you don’t know the language. Go alone. Let the discomfort teach you. Do it now.

When you return, you’ll be closer to yourself and better able to serve others.

-AM
Arigatou gozaimashita.
ありがとうございました

Whatever the male version of a “Basic Fall B***h” is, that’s me.

Instead of tall boots and pumpkin spice lattes, I’m all about flannel, hoodies, and doing whatever I can to get back to my Orion’s Kin roots. I know how to dress, I know how to get things done outside, I flex my muscles, pound my chest, and let the world know I’m alive.

I’m not even joking. If you’ve been outside in my neighborhood in the morning when I let the dogs out, you may have actually seen or heard this. It’s a whole thing.

I’m sure one day soon this display will be horribly embarrassing to Ford.

Anyway, I hope you enjoy this month’s newsletter as we shift into fall and I move one step closer to Ford’s first Halloween.

Bonus points if you can guess what we have planned for him.

-AM

When the Knife Hand Fails

If you’ve ever served in the military, you know the look. Stern eyes. Firm jaw. The knife hand.

It’s how we communicate urgency and precision when life is on the line. I carried that same clarity into my career, and for years, it worked. Until it didn’t.

Recently, I used that same approach with a colleague overseas and learned that clarity without cultural awareness can cause damage. It took weeks to rebuild trust, and I’m still thinking about it.

Leadership isn’t about always being right. It’s about knowing when to adapt. The real tragedy of a mistake is failing to share it so others can learn.

So please, learn from mine HERE.

Ford is Jenelle + Me: Beware.

More than anything I’ve ever done in my life, I love being a dad.

Let me tell you, when you put Jenelle’s independence and my drive into a baby and shake it up, well, I’m not sure the world was ready for Ford Michael, but HERE HE IS (and he is almost mobile!)

It has been so cool to watch our little dude come into his personality. His big blue eyes and alluring smile are powerful tools, and at only eight months he is learning how to use them (well.)

Much like his dad, he doesn’t do anything small. I mean, why crawl when you can walk?

Much like his mother, he wants what he wants, when he wants it, and without compromise. And, much like his mother, the influence begins with a smile and a cute laugh. But, if the desired outcome is not delivered fast enough, he gets demanding and SPICY!

We’re in trouble, and we’re here for it!

I am excited to teach Ford how to be a strong man of character.
I’m excited to spoil him.

I’m not looking forward to correcting him when he crosses what I’m considering “character guardrails”, but anything worthwhile isn’t without its tax.

Stay tuned for halloween pictures. I THINK we (Jenelle) has landed on fighter pilot in a (stroller) jet, a flight attendant (Jenelle), and a ground crew person (me.) Or an F1 driver and pit crew. We’ll see.

Unlocking Your Humanness

Life is about more than work. Your value as a person is bigger than what you produce.

I know for many of you, neither of those statements are profound. For me, they’re new. It’s not that I haven’t said those words before, I have, probably directly to many of you reading. It’s that, for 28 years, since I was 15, I didn’t believe them for myself.

Since I was young, I’ve felt deeply flawed. Those feelings of inadequacy grew after losing so many friends overseas and especially when Kevin died so that I could live.

For years, I’ve carried guilt. I’ve worked to make an impact that might fill the void left by my nine brothers in arms who never came home. I’ve pushed myself to the edge because I believed that if I wasn’t good at work, I wasn’t a valuable human being.

I’m a flawed human. But recently, I’ve started to wonder: what if I’m not?

As I dig deeper into AI and confront the reality of our future, I’ve been reading about the evolution of physics and soft computing.

When I was in school, we were taught that the universe was a static, endless expanse of nothing. But recent discoveries suggest the universe may actually be dynamic; perhaps even “alive.” Research in cosmology (see work by Dr. Avi Loeb, Brian Cox, and others) suggests large-scale cosmic motion follows organic patterns, like oscillations or “breathing” cycles over billions of years.

Even more intriguing is research into chromosome 2, the feature that most clearly differentiates human DNA from other primates. Around 250,000 years ago, two ancestral ape chromosomes fused to create human chromosome 2. This event is well-documented in genetics (see Nature, Vol. 431, #7010, 2004) and is one of the major mutations that led to advanced brain development, complex language, and emotional depth.

It’s this kind of science that makes me realize how little we understand about what it means to be human.

When you compare the computational and electrical potential of the human brain to modern silicon chips, our biology is superior. The human brain operates at about one exaFLOP (10¹⁸ operations per second), comparable to the most powerful AI systems, but on just 20 watts of power. Our biology beats engineering.

So here’s my theory: we are closer than ever to discovering what it really means to be human, but our obsession with technology is pulling us in the opposite direction.

The more we rely on ChatGPT, the more we build simulated relationships, the more we let algorithms think for us, the further we drift from our innate intelligence, empathy, and spiritual connection. The lazier and less evolved we risk becoming.

Some use this kind of research to disprove religion. For me, it does the opposite. The more I read, the more convinced I am that we were created with intention, purpose, and power beyond comprehension. We are literal sparks of creation, capable of far more than we realize.

If only we could figure out how to unlock it.

I’m building a deeper hypothesis around this topic and would love to interview a few of you to help shape it.

If you’re interested, hit reply and let me know.

If you want to check my work…

• Chromosome 2 fusion: Verified through multiple sources including Nature (Fan et al., 2002; IJHG, 2004). This event is accepted as a defining feature of human evolution.

• Universe “as living system” hypothesis: Still speculative, but supported by analogies in quantum cosmology and biocentric models (Robert Lanza’s Biocentrism; Penrose & Hameroff’s Orchestrated Objective Reduction).

• Human brain vs. computer power: Estimated at ~10¹⁵–10¹⁸ operations/sec on 20 watts of energy, consistent with peer-reviewed neuromorphic research (University of Sussex, 2023).

A Business You Should Know: Nomad Jax

, and the recently opened Garage, by Nomad Jax is a Veteran Owned business based in Southern Pines, NC. Best described as a boutique for dudes (a Brotique, maybe?), the shops contain a little bit of everything curated during the world travels of the owners.

You can find everything from locally made, hand forged knives, to antiques, dude accessories, and cool clothes for both men and women.

These stores - the brotiques (trademark Adam, 2026) - are very unique and a lot of fun. If you’re in the Southern Pines or Carthage, NC area, please make a point to check them out. Otherwise, please check out their WEBSITE to get a feel for what they’re all about.

Nomad Jax Haberdashery and the newly opened Garage, by Nomad Jax are veteran-owned businesses in Southern Pines and Carthage, NC. Think of them as boutiques for dudes.. or as I like to call them, brotiques (trademark Adam, 2026).

These places are the kind of shops that remind you craftsmanship isn’t dead. Inside you’ll find locally made, hand-forged knives, vintage antiques, and a mix of clothes and accessories that feel like they were curated by someone who actually lives life, does hard things, and a cool design aesthetic.

Both stores reflect the owners’ world travels and their eye for quality. Everything has a story, and you can feel it the second you walk in.

If you’re anywhere near Southern Pines or Carthage, make a point to stop by. Shake a hand, share a story, sip some bourbon, and pick up something that’ll last longer than most trends.

If not, check out their website to get a feel for what they’re about — it’s worth the scroll.

In Closing.

I’m freaking tired.

As I type this, I’m sitting in Tokyo, fresh off trips to Denver for the SAFe Summit and AI Symposium, followed by SAFe Day Gov in DC, a stop in New York to support Planisware. After a quick trip home, I’ll be off again; this time to Netherlands to keynote RTE Summit, and then back to Denver for a 2026 strategy session.

After that? I’ll start my half-bbatical (what I’m calling my poor attempt at a sabbatical, split into two chunks). I value the time I have to reflect while traveling, and I love the moments spent with customers, partners, and colleagues. But I miss my family.

Truthfully, it feels pretty good to miss them. The goal is to grind for the next five years so that I can earn the freedom to spend all of my time with them.

Until then, you’ll find me on the road sharing my energy, connecting people, and enjoying some damn fine food.

Until next time,
-AM

If you have ever served in the US military, you undoubtedly know this look: stern eyes, firm jaw, and the knife hand.

To those who have not, you’re probably thinking, “what the hell is this?”

Well, this is a method used in the military to communicate direction or corrective action directly, with precision and clarity. When life or property are at stake, the knife hand comes out to accentuate a narrative, point clearly at an objective, and serve as an exclamation point.

Since leaving the military nearly twenty years ago, I have holstered the knife hand (apparently, contrary to what the military would lead you to believe, pockets do serve a purpose). But I have found the technique of clear, concise, and direct communication useful when time is of the essence or there is no room for negotiation or misinterpretation. When executed with calm and rational precision, the technique is effective.

That is, until it’s not.

When communicating with individuals in North America and across Europe, the “professional knife hand” is typically well received, especially in places like the Nordics and Germany where clarity and precision are highly valued. In Latin and South America, as well as in some states in the US, I’ve found that the technique works well only after rapport and trust have been established, whereas in places like Boston and New York, my tactfully direct style is viewed as perfectly normal.

Recently, however, I ran into some trouble and inadvertently caused embarrassment to a colleague of mine in Asia. Each culture has its own unique patterns for decision making, exercising influence, and offering direction. If you want to learn more, I recommend Kiss, Bow, or Shake Hands by Terri Morrison. I’ve found a professional edge through dedicated study and mastery of the preferred communication patterns of customers and partners in different parts of the world.

Where I failed was while sitting in the comfort of the US, offering clarity and direction to a colleague from another part of the world. Needing to quickly course-correct, I forgot about cultural nuance and brought out the professional knife hand.

In alignment with proper knife hand execution, I proceeded with professional and calm precision until my point was made crystal clear. By the time I was done, I knew I had screwed up.

No matter who you are or how experienced you are, you will make mistakes. What matters more than the mistake itself is how you recover once it’s been made. Recognizing how I made the other person feel, I snapped back into a culturally appropriate posture, humbled myself, and clarified my intent. Even with that, it took weeks of consistent, culturally aware communication to restore our productive rhythm, and truthfully, I still feel awful about it.

With leadership and influence comes responsibility, not only for outcomes, but also for leading from the front. When working with an international team, leading in a style that is well received by the various cultures of your teammates adds another axis of complexity.

I share this story because the greatest tragedy from any mistake is failing to learn from it and missing the opportunity to help others learn from it too.

So, if you work with an international team or travel internationally, I encourage you to take a few minutes to understand the cultural norms and communication styles of those you’ll be working with.

Though the knife hand is undeniably effective, it must be used with caution. You’ll likely get the result you’re after, but you may lose the relationship.

Be well.

For me, partnerships aren't just a business strategy, they're a way of life. Over the past three decades, from racing BMX bikes as a teenager to overseeing strategic partnerships at Scaled Agile today, I've learned that success is rarely a solo endeavor. The most meaningful wins, whether on a race track or in a boardroom, happen when you're helping others succeed alongside you.

Emig's Bike Shop and a Teenager's Hustle

I was never fast enough to earn a BMX sponsorship the traditional way. But I needed a way to fund my racing habit, so I approached Emig's, a local bicycle shop in Meadville, PA, with a simple proposition: give me a jersey and a discount on parts, and I'll promote your shop far beyond the track.

That handshake deal with Pat taught me my first lesson about partnerships: it's not about what you can get, it's about what value you can create. Over the years, I brought Emig's referrals and visibility, and in return, they invested more in supporting my racing. It was a true partnership where both sides won.

BMXtreme and the Power of Collaboration

The success with Emig's opened my eyes to bigger possibilities. I started collaborating with Grant Hansen, who ran an e-zine called (what we called blogs back in the '90s) BMXtreme. Through writing and connecting with a larger audience, I could provide even more value to more people.

Grant saw something in my approach and eventually invested in my vision to duplicate the success of BMXtreme in motocross. Together, we launched MotoXtreme.com in late 1999 and ran it until 2001 when my main business, Carzz, demanded my full attention. This partnership taught me that the best collaborations happen when partners believe in your vision enough to invest in it.

The “Big Break”

In 1999, while still in high school, I landed partnerships with Bulldog Bikes and PLAY Clothes, two legendary BMX brands of the time. These weren't just transactional relationships; they were built on trust and mutual respect. Working with PLAY and Bulldog reinforced a crucial lesson that later saved my life in Iraq: partnerships are as much about relationships and trust as they are about dollars.

Scaling Success

As my focus shifted from hobbies to building a sustainable business, I applied everything I'd learned about partnerships to scale Carzz, my platform that helped car and motorcycle dealerships get their inventory online before Cars.com and others dominated the space.

To prove value to my first clients, I took a partnership approach: charge only a commission on vehicles sold through the platform, and if we proved successful after the first year, we'd shift to a subscription model. This wasn't a pricing strategy, but an approach built on the lessons I learned about relationships and trust that I now recognize as partnership philosophy. We succeeded together or not at all. I repeated this approach with dealerships across Pennsylvania, Arizona, and Minnesota, building trust one successful partnership at a time.

Life and Death Partnerships

After experiencing a liquidity event from Carzz, I thought I was done with partnerships when I joined the Army. For the first year, focused on training and learning, that seemed true. But in my second year, when my unit began working with the Iraqi Police, those partnership lessons came roaring back.

Partnership takes on an entirely different meaning in life-and-death scenarios. Trust wasn’t about business success, it was about survival. These experiences taught me that the strongest partnerships are forged when stakes are highest and mutual dependence is absolute.

Learning the Other Side

After being wounded in combat and retiring from the Army, I returned to technology and spent several years learning management, advanced engineering, and architecture. Crucially, I also learned how to be a customer of vendors. This flipped perspective taught me invaluable lessons about the other side of the partnership equation: what it's like when you're the one being partnered with, not just the one seeking partnership.

Impact Over Dollars

My role running the global military and law enforcement business at Under Armour expanded my partnership responsibilities beyond just business development. I managed partnerships with organizations like Wounded Warrior Project, Special Operations Warrior Foundation, Unit Scholarship Fund, and Tough Mudder.

This period taught me that the most powerful partnerships aren't always measured in revenue, they're measured in impact. Sometimes raising awareness matters more than dollars. The best partnerships serve a purpose bigger than profit.

Complex Ecosystems

During my time as an executive at Accenture, I oversaw complex partnerships between the firm, vendors, and clients. These weren't simple bilateral relationships but intricate ecosystems where multiple parties had to find mutual value. Here I learned about identifying mutual interests across complex stakeholder groups and balancing competing needs while maintaining profitability for everyone involved.

Full Circle: Building Success at Scaled Agile

Today at Scaled Agile, my responsibilities extend beyond partnerships, but I spend significant time helping consulting partners build and evolve their businesses. I oversee software and strategic partnerships while also leading our sales engineering team.

Building programs that help partners win has been the highlight of my career. There’s something profoundly satisfying about creating systems where partners, their teams, and their customers all succeed together. And in many ways, this is where I’ve learned to apply one of the most valuable lessons first taught to me by my friend Jenna in a Baltimore bar: the power of connection. Sometimes the greatest contribution isn’t what you build yourself, but recognizing when two partners will thrive together and making that introduction. It’s the ultimate expression of everything I learned starting with that handshake deal at Emig’s bike shop.

The Mattis Foundation

Even outside of work, partnerships continue to shape my impact. Jenelle and I started The Mattis Foundation to support our community with our time, talent, and treasure, while ensuring that the legacy of the man who died saving my life overseas will never be forgotten.

Even in the foundation's early days, we've leveraged partnerships to amplify our impact. Because that's what partnerships do: they multiply your ability to create positive change.

Looking Ahead

I don’t see a time when partnerships won’t play an important part in my life, and I want Ford to grow up understanding their value as well. Yes, we can succeed on our own, but it’s easier, more impactful, and a lot more fun when we help others succeed too.

Whether you’re a teenager trying to fund your racing dreams or an executive building strategic alliances, the fundamentals remain the same: create genuine value for others, build relationships rooted in trust, and remember that the best partnerships make everyone involved better than they could ever be alone.

For me, this was a way of life long before it became a business philosophy. And it’s carried me from BMX tracks to boardrooms, from combat zones to corporate strategy sessions. The context changes, but the truth remains: we’re stronger together.

I can’t believe it’s September, and September 11th at that. Days like today remind me how life mixes weight and lightness at the same time. Some moments change our path forever, like 9/11, which still shapes how I see the world and the importance of respectful dialogue.

Other moments are smaller but no less real, like realizing I needed Pilates just to carry Ford downstairs without risking both of us tumbling.

This months newsletter feels a bit heavier. You’ll find reflections on resilience and the state of our discourse, a look at how SAFe is evolving in the AI era, and a spotlight on a friend who’s building a business that helps organizations turn data into real decisions.

You’ll also find updates from the Mattis Foundation as we open scholarship submissions and gear up for next year’s Garden Party.

The TLDR: growth, development, and building a future worth believing in.

Charlie Kirk + 9/11

It’s a strange day. I hadn’t planned to send this newsletter until Saturday, and I had something very different in mind for this section. But given what today stirs up for me, I feel compelled to share.

As many of you know, I live with CTE as a result of an attack I experienced while serving overseas. My memory can be unreliable, but I do remember the morning of September 11th.

I had just returned to my dorm at Edinboro University after morning classes. When I turned on the TV, I saw the second plane strike the World Trade Center. I sat there, stunned, alongside my roommate Jordan. Before long, I drove to pick up my girlfriend at the time and my younger sister, who was still in elementary school.

I wanted the people closest to me nearby.

That day changed the course of my life, as it did for so many. 9/11 led me to serve in the Army, to deploy to combat, to be injured and medically retired, and eventually to run for political office.

While overseas, one of the lessons that stayed with me was how deeply fear and distrust had taken root in daily life. People were afraid to speak openly, because decades of authoritarian rule had made it dangerous to voice the wrong opinion to the wrong person. Dissent and dialogue were not tolerated; if they appeared, they were quickly silenced.

At the time, I never thought anything like that could take hold in America. But over the years, I’ve seen troubling signs of our own public discourse deteriorating.

I thought it had reached its breaking point with the attempted assassination of Donald Trump. The reaction to follow was muted. After the initial shock, some voices in the media even implied the events were staged. That subtle shift away from universal condemnation toward speculation revealed a dangerous level of apathy.

And now, just yesterday, we’ve witnessed the assassination of Charlie Kirk. Whether or not one agreed with his views, his murder for openly expressing them is something that should be universally condemned.

What I’ve found even more unsettling than the act itself is the response: apathy in some corners, and in others, celebration. To cheer the silencing of a man who welcomed debate is a betrayal of who we claim to be as a society. In a nation built on freedom of speech and open exchange, there can be no room for celebration or indifference when someone is killed for their ideas.

I experienced a small glimpse of this when I briefly ran for office. Like Charlie, I welcomed dialogue with those who disagreed with me. Most people engaged in good faith and were even surprised to learn that some of my positions weren’t what they assumed. But there was always a small group who refused to engage, preferring instead to shout, insult, or dismiss. That unwillingness to even hear one another feels far more widespread today.

On this 24th anniversary of 9/11, these lessons feel especially heavy. That day reminded us of both the fragility and resilience of our society. In the years since, I fear we’ve drifted toward a culture where division and contempt too often replace understanding and community.

We have to do better. We need to lower the temperature, return to respectful dialogue, and remember that disagreement should never justify violence or dehumanization.

Losing friends over politics is painful enough. But today I’m reminded that it pales in comparison to the grief of a wife and daughters who lost a husband and father yesterday, simply because he engaged in debate.

We must do better. We must start today.

Everything hurts, so, Pilates

Why settle for “add yoga” to your bro-lifting regimen when you can dive headfirst into Pilates? That’s kind of an “Adam thing” to do: all in, or all out.

After 30 years of running, lifting, and doing hard things (without ever stretching), I’ve become increasingly stiff in the mornings. So stiff, in fact, that going downstairs often meant two hands on the rail, slowly lowering myself one step at a time.

Real old-man stuff.

I probably would’ve just kept “dealing with it” until my first morning home alone with Ford after Jenelle returned to work. Standing at the top of the stairs with him on my hip, I realized I needed to make a change.

When I started researching what that “something” should be, I kept skipping over Pilates. The whole “Pilates Princess” stereotype wasn’t on my radar. But after a consult at the LifeTime Fitness training desk, I was introduced to my now-trainer, Alex Kreager. She convinced me her program wasn’t what I’d seen on Instagram.

So what do I think? You should give it a try. I’ve seen huge improvements in foot and ankle mobility, a dramatic increase in core strength, and greater stability. All of that has carried over into running, lifting, and the other shenanigans I enjoy.

The most valuable part, though, has been the chance to calm my mind and focus on breathing.

My advice? Try it, but start 1:1 with a trainer. Once you’ve worked through your, as Alex calls them, “unique deficiencies,” you can always move into a group setting. But the individual start is where the real transformation begins.

SAFe + AI-Native

At this year’s SAFe Summit & AI Symposium in Denver, the message was clear: the future of business operations isn’t about leaving SAFe behind, but modernizing it and embracing AI as a native capability.

SAI launched AI Native as a distinct initiative to help enterprises achieve ROI from AI while staying rooted in SAFe. The new EDGE framework (Exponential, Disruptive, Generative, Emergent) reframes how organizations navigate complexity, while updated courses, ROI tools, and guidance shift the focus from outputs to outcomes.

The takeaway: agility must evolve, operating models must adapt, and community connection matters more than ever.

We’re excited to bring to market the first in a series of courses designed to build AI competency in enterprises. The AI-Native: Foundations class is a powerful starting point for anyone, in any role.

👉 Check out upcoming sessions here
👉 Read the full SAFe Summit & AI Symposium recap here

A Business You Should Know: Decision-Ready Analytics

My friend Liz is kind of a big deal.

In addition to being an amazing friend and neighbor, she’s a data and analytics wizard.

After many years leading a data practice within a consulting firm, she took some time away to reflect on what she wanted to do with the next phase of her career.

The result: Decision-Ready Analytics. A firm built to cut through the noise and help organizations turn data into decisions with clarity, confidence, and ROI.

They deliver both build services (migrations, AI readiness, implementation across tools like Azure, AWS, Snowflake, Databricks, Tableau, and more) and strategy/advisory services for companies that have already invested in data platforms but aren’t seeing results. Their approach is people-first, tool-agnostic, and focused on measurable ROI,

If you or your business are tired of drowning in data without direction, check them out: Decision-Ready.com.

Mattis Foundation

We will open the submission portal for the 2026 Kevin J. Smith Memorial Scholarship on Novmber 3, 2025. Please take a look at the nomination requirements and share them with any well-qualified students you know. We’re proud to offer a $5,000 annual scholarship, renewable for four years.

Also, Jenelle is planning the 2026 Garden Party, and is looking for two others to join her! If you are interested, please reach out to her at jmm@mattisfoundation.org,

Follow us on Instagram to stay up to date—we’ve got some fun things in the works.

Thank You!

Some months bring a heavier mix of reflection and reality, but they also remind us why growth and connection matter so much. My hope is that something here sparks a thought, a conversation, or even a small change that makes tomorrow a little better than today.

Thanks, as always, for reading and for being part of this journey with me.

Keep steady. Keep building. And if something here struck a chord, I’d love to hear your thoughts.

Cheers,
Adam

I had a bit of fun with this post and developed a short graphic novel to go with it. Have a look HERE!

In 2020, our company was a household name, but a slow, fractured, siloed version of itself. We were known more for what we used to be than where we were going. Every business unit had its own roadmap. Product teams were buried under layers of approvals. Customer insight? Delayed, diluted, and distant.

Releasing anything meaningful in under 12 months felt like fiction.

We called on our trusted partner, ApexWorks, to help address the issue—and they introduced us to the Scaled Agile Framework (SAFe).

It wasn’t love at first PI. But over time, step by step, ART by ART, we saw the fog lift. Strategic alignment became real. We began operating with a shared purpose and a clear focus on delivering value. Dependencies that used to take months to resolve began clearing in days. And not just through dependency management—but by thoughtfully investing in our business and technical architecture, we eliminated most dependencies entirely.

Our portfolio began to flow.

By 2022, we were more than agile: we were becoming a digital-first, customer-obsessed organization.

And it paid off.

We started launching new product lines twice as fast. NPS climbed. Employee engagement skyrocketed. We had a system – a real operating model – that linked strategy to execution across the entire enterprise.

I believed in SAFe then. I still do.

But by 2025, something felt… off.

Not broken. Not slow in the old way. But outpaced.

We were releasing incrementally every two weeks, teams were testing and validating bets in hours. We carefully planned our increments, but digital-native disruptors were letting AI generate and prioritize entire backlogs in real time, based on simulated customer data and enterprise capacity.

We weren’t losing. But I could feel the gap closing.

So we adapted. Again.

We re-engaged ApexWorks. We partnered directly with the experts at Scaled Agile. Together, we asked:

What if AI wasn’t just bolted onto SAFe, but embedded at its core?

We started by bringing AI into our Product Management practice. Innovation cycles that once took days now took hours. We could hypothesize, prototype, and validate new products—both physical and digital—using AI-driven digital twins. They weren’t perfect proxies for real users, but they got us to a 90% solution faster than ever.

From there, we scaled. With our AI-enabled work management system, Atlascore, Features, Stories, Dependencies, Risks, even PI Objectives, were generated for consideration in near real time.

And PI Planning changed; not by shrinking, but by shifting focus.

Instead of spending two days grinding through prioritization and backlog breakdown, much of that work was already system-generated and validated before anyone entered the room, the two days of PI Planning became about context, creativity, and connection: building shared understanding around business priorities, aligning on AI-generated objectives, pressure-testing risks, and negotiating complex dependencies.

Teams left not just with plans, but with energy. Planning was no longer bogged down in logistics. Teams had space to think about how to deliver impact, not what to build.

Another big shift? It wasn’t just in planning. It was in doing.

At first, teams worried about being replaced. But AI didn’t take their jobs, it made them faster, sharper, more relevant. Agentic AI handled the busywork: data cleaning, test framing, compliance checks, documentation; while teams focused on creating, innovating, solving.

The results spoke for themselves:

• Defect rates down 42%.

• Market windows were hit.

• The right things delivered the first time, validated before a single line of code was written.

Today, our Portfolio Kanban is powered by AI-driven digital twins. Strategic bets, like entering a new DTC channel, are modeled before investment. We simulate the impact on customer journeys, enterprise architecture, financials, and value flow. We don’t argue; we analyze. AI shows us which initiatives align, which cannibalize, and which are likely to stall.

And with ServiceLine, our connected workflow and operations intelligence platform, we see how work flows across the enterprise in real time. Architecture and operational debt are mapped, measured, and prioritized alongside new investments.

PI Planning is still sacred. Still human. But now, 70% of the heavy lifting is already done by AI. The ART teams focus on delivering outcomes, not debating outputs. And with the noise cleared away, collaboration has never been sharper. Conversations aren’t “what do we build?” but “what’s the best way to deliver customer impact?”

We’re not just digital-first anymore. We’re AI-augmented.

Customers get features they didn’t know they needed, right when they need them.
Product Managers spend less time debating, more time delighting.
Engineers innovate instead of refining backlogs.
Strategic pivots happen in days, not quarters.

And SAFe?

It’s never been faster. Or smarter.

In 2020, SAFe helped us scale agility.
In 2025, SAFe+AI helped us evolve it.

The next chapter? It’s already in simulation.