#51 - One KPI To Rule Them All - Part 1/3
The One Metric That Unlocks Every Other Sign of AI Value
#beyondAI
If you build internal AI products, you’ve probably felt it: the disconnect between all the work you’re doing and the recognition you’re not getting. The pitch goes well. The prototype works. The model scores look great. But months later, you’re still struggling to answer one quiet, persistent question:
“Did it really change anything?”
This series is for those of us who carry that question around. Not just as a reporting requirement, but as a personal weight. Because building AI is no longer the hard part.
Proving it mattered is where most teams falter. And what makes it harder is that the outside world doesn’t see the difference. External AI teams get clean metrics: revenue uplift, conversion rates, churn rate. But inside the enterprise, success hides behind foggy processes, political handoffs, and silence. Sometimes your solution is used, but invisible. Sometimes it’s unused, but still alive in infrastructure. And sometimes it’s brilliant on paper, but completely irrelevant to how work actually happens.
In this series, I’ll explore why internal teams need a different approach to measuring value. Because in these enterprise environments, what looks like success can quickly become a mirage.
And unless we learn to measure what matters — early, honestly, and with the business in mind — AI will continue to be a story of missed potential.
Let’s change that.
The Value Mirage of Internal AI
Why success is harder to prove than most teams expect
Internal AI teams are growing. Their ambition is high. Their technical capabilities are maturing. Models are getting stronger. Prototypes come together faster. And the tooling, thanks to foundation models and better infrastructure, is finally catching up to the promise of enterprise AI.
But despite all this visible progress, one question still lingers at the core of almost every internal AI product conversation:
“How do we know this actually delivered business value?”
And often, there’s no clear answer. Not because people aren’t trying, but because in the world of internal AI, proving value is uniquely difficult. Much harder than it seems from the outside. And often far harder than expected by stakeholders who equate technical performance with business success.
It’s not that these products fail outright. In fact, that’s what makes this so tricky. They work. They run. They score. They process. They sometimes even deploy. But something’s missing. Something that’s felt in the silence that follows a well-rehearsed demo, or in the polite nods after a slide titled “accuracy > 90%.”
That silence is the absence of connection between what was built and what the business feels. It’s the moment when your audience is no longer listening to how it works. They’re wondering why it matters.
At the beginning of any internal AI journey, the terrain is foggy. We usually start with a hunch, a pain point, or a well-meant ambition like “let’s automate this process”. But when it comes to identifying the right KPI — the one that will later prove the solution’s worth — there’s rarely a straight answer.
And to be honest, that’s normal.
You don’t always know upfront what to measure. You don’t always know how value will show up. Maybe it’s cost avoidance. Maybe it’s faster cycle time. Maybe it’s fewer escalations, better targeting, or less manual rework. But these outcomes often sit far downstream from the product itself. They depend on behavior change, process integration, and adoption across multiple teams. And that makes them slow, indirect, and politically fragmented.
So the question of “what should we measure?” isn’t just hard. It can feel paralyzing.
And yet, most of us are still asked to provide a business case before we build. To define impact. To attach a number. We try our best. We make projections. We pick a few KPIs that might signal value later. But the truth is: we don’t know yet. We can’t know yet.
This is what makes internal AI different. In external products, success leaves a trail. Users pay, or they churn. Growth can be tracked. Revenue is visible. There are signals that tell you whether your product has found a market or not.
But inside the enterprise that trail disappears.
There is no revenue line for your AI product. No invoice to point to. No CAC or LTV. Just internal teams who may or may not use what you’ve built, and may or may not tell you when they stop. Your AI might be deployed, but bypassed. Integrated, but distrusted. Impressive, but irrelevant.
You rarely know the moment your product fails. Because internal failure is quiet. It doesn’t come with angry customers or public reviews. It comes with workarounds. With shadow spreadsheets. With tools that are “live” in infrastructure but dead in behavior. It comes with silence.
And in that silence, a deeper risk starts to grow: credibility loss.
Because the business remembers the investment. They remember the pitch. They remember the promise. But if no one can confidently say what changed, the narrative starts to shift. “AI” becomes something that consumes resources, not something that creates value. Sponsors lose interest. Teams lose momentum. And the next idea becomes harder to fund. Not because it’s worse, but because trust has eroded.
This is the real danger. Not technical failure, but the slow erosion of confidence in internal AI as a whole.
We’ve all seen it. Maybe we’ve even built it. That internal AI product ambition that did everything right on the surface — accurate predictions, beautiful dashboards, flawless deployment — but ultimately delivered nothing of value. Not because the product was broken. But because no one used it. Or because the way it was used never translated into measurable change. Or because the people it was supposed to help never changed how they worked.
These aren’t edge cases. They’re common. And they’re exhausting. Because the effort is real. The intentions are good. But the outcome? The outcome vanishes. And it leaves behind a question that haunts many internal AI teams more than they’d like to admit:
“Did we build something valuable or just something impressive?”
But i think we can overcome that struggle with just one KPI. At least at the beginning. I’d like to describe my thoughts with a Tolkien’s Lord of the Rings analogy:
One KPI to rule them all,
One KPI to find them,
One KPI to bring them all,
and in the outcome bind them.
Let me explain why.
One KPI to Rule Them All
The insight that reshaped how I measure internal AI success
For a long time, I searched for the one perfect metric. The one that would finally tie our internal AI products to real business value.
But the difficulty was this: internal AI products touch multiple business processes and workflows. Some of those generate revenue. Others protect it. Some AI products are meant to avoid costs by preventing additional headcount. Others reduce costs by cutting down on external spend, like consultant fees. And some act as enablers - helping the business grow revenue, indirectly. In some cases, all three apply.
So I tried cost savings metrics. I tried process efficiency metrics. I tried risk reduction and effort elimination. I even tried composite scorecards with weighted proxies.
And sometimes, those metrics helped tell the story.
But only on slides, in steering committees, or in follow-up emails.
Too often, they were fragile signals. Too slow to emerge. Too easy to challenge. Too detached from real behavior.
That’s when I realized something both practical and deeply grounding:
Before you can prove value for the business, you must first prove value for the end users.
And the best signal is usage.
Used voluntarily. Used consistently. Used by the right people, in the right moments. Because in internal AI work, nothing else matters without adoption.
That was the shift for me. That was the moment the fog began to lift.
So when I say adoption is “One KPI to rule them all” what I mean is this:
Adoption is the only early signal that has the power to connect — or rule over — every other KPI you’ll eventually need.
Model performance metrics like precision, recall, and latency
Business metrics like time saved or compliance risk reduced
Outcome metrics like reduced handling time, increased conversion, or fewer escalations
They — the KPIs you’ll later be asked to report on — are all governed by adoption.
If no one uses your AI product, none of those metrics matter. They become disconnected ideas in your head, or worse, misleading decorations on a dashboard.
So yes, them refers to the entire family of downstream KPIs.
And adoption is what makes them possible. It gives them form. It gives them context.
It’s the keystone.
But adoption isn’t just a positive signal. It’s not just the green light that tells you your product is alive. Adoption is also your first diagnostic tool.
It’s the earliest and most reliable sign that something might be wrong. If adoption drops. If it never starts. If it spikes in one team but not another.
You don’t need to speculate. You investigate.
And that investigation leads to real insights:
Does the user journey feel intuitive?
Are people skipping steps or overriding AI decisions?
Is the integration too shallow, or too disruptive?
Are teams using it in unintended ways that reveal new value?
Or is the AI not accurate enough?
In this way, adoption becomes both proof of momentum and a system of early warnings. It helps you see what’s working. And it helps you find what isn’t — before failure becomes political or irreversible.
I’ve seen technically strong AI products die quietly because nobody changed how they worked. And I’ve seen modest solutions take off because adoption came early and the team had the humility to listen, iterate, and respond.
That’s when I began to treat adoption rate not as an afterthought, but as the one KPI that governs all the others. It’s the only one that’s visible from the beginning.
And it’s the only one that unlocks the rest.
No, adoption won’t tell you everything. It won’t quantify ROI or certify value to the CFO. But it will do something even more essential. It will show you if what you’ve built is real enough to be used — and alive enough to be improved.
That’s why, in internal AI product work, adoption is not just a signal. It’s the signal that rules them all.
One KPI to Find Them
Why adoption reveals the business value you were looking for all along
When we begin building an internal AI product, we always start with purpose. We don’t build blindly. We listen to the pain points. We work with users. We try to understand the operational logic behind the problem. And at the same time, we aim to connect that user problem to something bigger — to business outcomes.
Will this solution help us reduce cost?
Will it eliminate waste or manual rework?
Could it avoid future risks?
Or will it enable revenue, directly or indirectly, by improving decisions, speed, or scale?
We don’t ignore these questions. In fact, we often write them into the product brief or the business case.
But let’s be honest — defining the exact metrics and logic to measure them, especially upfront, is hard. Sometimes frustratingly so.
Because even when we’re clear on what the AI solution should help achieve, we’re rarely clear on how that impact will show up in the data.
And even if we define a KPI with a stakeholder early on, it often turns out to be:
Too far downstream
Owned by another team
Mixed with dozens of other influencing factors
Or worse, tracked in a report that nobody actually trusts
So we make our best guess. We write down the metrics we think will prove value later.
But more often than we’d like to admit, those guesses don’t hold. And we realize, three months in, that the KPI we picked either can’t be measured cleanly or doesn’t reflect the real outcome we’re driving.
This is where adoption changes everything.
Because once people start using your AI product — really using it, in live processes, under real conditions — suddenly the fog begins to lift.
You see how the product is being used. You learn which teams engage with it naturally and which ones don’t. You observe where trust builds and where friction still exists.
And most importantly, you start to see which outcomes are actually being influenced — and how.
The business KPIs you couldn’t quite measure before, now they start surfacing. Still not through your own analysis, but through the people using the product every day.
A sales team might say, “We’re closing leads faster now.”
A customer service leader might report, “Escalation rates have dropped.”
A compliance officer might notice, “We’re catching issues earlier.”
These signals don’t come from the AI team. They come from the business. And that makes them powerful.
So when I say “One KPI to find them” I mean this:
Adoption helps you find the true business KPIs — them — that are affected by your product.
Not in theory and projection. But through lived usage.
Because once a product is adopted:
It becomes observable
It enters real workflows
It creates data you didn’t have before
It starts conversations you couldn’t have earlier
And that’s when the right metrics begin to emerge. Not as guesses, but as patterns. Not as assumptions, but as evidence.
It’s humbling, really.
Because it reminds us that no matter how sharp our thinking, we don’t control all the value. Some of it is outside our reach.
It’s embedded in business metrics we don’t own.
It’s locked in processes we can’t fully observe.
It’s shaped by stakeholder behaviors we don’t manage.
But once our product is being used those metrics start to show up.
The business begins to bring them forward. Suddenly, people don’t just ask you what the product is doing. They start telling you what it’s changing. That’s when you know your product is real.
So yes, adoption doesn’t just rule the other KPIs. It helps you find them. It gives you access to the only thing that ever reveals business value for internal AI: the lived behavior of people who trust what you’ve built.
One KPI to Bring Them All
How adoption becomes the gravitational pull that turns an AI product into a business asset
There’s a moment in the life of a successful internal AI product when you start to notice a shift. It no longer feels like you’re pushing. It no longer feels like you’re convincing people to try it, chasing usage reports, or writing follow-up messages just to keep the spark alive. Instead, things begin to pull.
A new team reaches out.
A stakeholder from another business unit asks if they can join the next pilot.
Someone you’ve never met references your product in a planning meeting.
What started as a focused product now has momentum. And that momentum did not come from technical excellence alone. It came from adoption through relevance.
So when I say “One KPI to bring them all” I still mean the business KPIs we’ve been trying to measure from the start.
Cost reduction.
Cost avoidance.
Direct or indirect revenue enablement.
Them — the KPIs we struggled to define at the beginning — finally start showing signs of life once the product is used.
But something else happens, too. Adoption does not just bring the metrics into motion. It brings in the people behind the metrics. The business owners, the adjacent teams, the leaders and enablers who start building on top of what you’ve created.
It becomes something people talk about. Something that touches other systems, other teams, other goals. Adoption pulls it all together. It is the gravitational force that begins to draw the organization in.
And that pull is what activates your KPIs in a way that dashboards never could.
You start seeing shifts in:
Cycle times
Manual workarounds
Time-to-resolution
Uplift in conversion or sales readiness
Effort allocation across roles
Forecast accuracy
Process completion rates
But now, it is not you reporting these shifts. The business starts noticing them.
Finance might begin modeling cost avoidance based on changes in headcount planning.
Operations might share how throughput has increased with no additional staff.
Risk or Legal might recognize that an early-warning AI system is now embedded in their compliance checks.
These conversations do not happen when a product is in development. They do not even happen when it is just launched. They happen when adoption is real.
Adoption brings something else that most metrics cannot.
Alignment.
When the product is used, everyone around it starts working differently. Enablement makes sense. Feedback becomes targeted. Governance becomes active, not theoretical.
Executives stop asking why you built it — and start asking what more it could do.
Your AI product stops being an initiative. It starts becoming infrastructure.
And here is the truth I’ve learned:
All the metrics in the world are meaningless until people care. And people do not care until they use it. And once they use it — when it actually helps them — they start becoming part of the story. They bring others in. They speak for the product. They make your business case stronger than any model ever could. AND they make the outcomes transparent only they have control and access to.
And in the Outcome Bind Them
How adoption transforms usage into proof, and AI products into trusted outcomes
By the time adoption is established — when people are using your AI product not out of obligation or curiosity, but because it genuinely fits how they work — something important starts to shift. Not suddenly. Not dramatically. But steadily, and unmistakably.
The product begins to create more than activity. It begins to create results.
And yet, those results — the ones we aimed for in the beginning — are rarely immediate. They do not appear in clean, self-contained dashboards. They do not arrive in tidy before-and-after comparisons. Instead, they begin to surface in the rhythm of the business. In meetings. In feedback loops. In operational metrics that slowly start to move.
This is the moment where everything we hoped to measure — those elusive business KPIs we tried to define at the start — begin to settle into form. And they do not just emerge. They become bound to the product itself.
That is what I mean when I say: “And in the outcome bind them”. Until this point, many of those KPIs felt abstract.
Cost avoidance.
Cycle time reduction.
Conversion uplift.
Revenue growth.
We mentioned them in our business case. We tried to estimate them. But we also knew — quietly — that measuring them would be hard. That they lived downstream, in systems we did not control, owned by people we were not sure would pay attention.
But once adoption takes hold, once the product is used in daily work, something subtle but powerful changes. Those same people begin to see the impact for themselves. Not because we told them to, but because it shows up in their reality.
A team lead starts saying, “We do not have to double-check these entries anymore.”
A controller notes, “We are spending less time on reconciliations.”
A compliance owner quietly shares, “We have reduced our response time without adding staff.”
These are not claims. They are experiences. And in that moment, the KPIs we sought to measure begin to belong to them. That is the binding.
Because adoption alone is not the outcome. Adoption is the start of a pattern — one that allows us to observe, learn, and begin making connections we could not make before.
Now, we are no longer speculating. We are seeing relationships between usage and impact. We are finding leading indicators. We are discovering how certain behaviors, when supported by the AI product, correlate with improved business performance.
And crucially — we are no longer the only ones doing this work. The business starts participating. They bring their own data, their own stories, their own versions of the value narrative. And suddenly, we are not measuring in isolation anymore. We are measuring together. This is the moment internal AI products move from experimental to essential. Not because everything has been quantified. But because the product is no longer defended by the product team — it is spoken for by the business.
That is when outcomes become stable, when metrics become trusted, when sponsorship becomes continuous.
And it all begins because adoption has created enough usage to make impact visible. Enough trust to make collaboration possible. Enough real-world relevance to make measurement credible.
So yes, adoption rules the KPIs. It finds them. It brings them into motion. But most importantly, it binds them to outcomes — to the kinds of results that teams can feel, leaders can report, and businesses can build on.
Final Reflection
When I look back at the internal AI products that truly made a difference, it was never the technical elegance that convinced the business. Not the architecture. Not the pilot results. It was because we found ways to make the product useful. Genuinely useful. For real people, in real moments of their work.
That usefulness led to something rare: engagement.
Participation in user acceptance tests went up. Feedback became honest. And ultimately, adoption took hold.
Adoption is where it all starts. And this beginning is not easy.
But once you understand that you do not need to focus on any other metric first, it becomes much easier to allocate your precious resources to the right tasks. You stop chasing hypothetical KPIs and start building something real.
Because without adoption, all other KPIs remain out of reach. They stay theoretical and fragile. They stay disconnected from the truth of how people work.
This article was all about that one insight: Why adoption rate is the only metric that matters in the beginning.
In the second part of this series, I will go deeper into the how. How to define adoption. How to track it. And how to use it as a compass to guide product decisions, stakeholder alignment, and even long-term funding.
Hope to see you there.
JBK 🕊️