No Use Case, No Value. Now You're Getting the Invoice.

For years, a badly managed use case had a cost you could hide. Development time, salaries, and the opportunity cost of building the wrong thing instead of the right one. All of it buried inside headcount budgets and shared project pools, spread across quarters, visible only to the people close enough to see the waste.

Those days are over.

AI runs a meter. Every prompt, inference, and model call costs something, and the bill arrives every month. Welcome to what’s being referred to as Tokenomics. The practice of managing how much an AI system consumes and costs to run, so that usage stays tied to real value rather than spiraling into invisible waste.

If the use case was never properly defined, if nobody ends up using what you built, or if the problem you solved turns out not to be the real problem, then that waste now has a cost associated with it. And that number lands on the CFO's desk.

This is why I keep coming back to no use case, no value. The principle was always true. AI has just made it impossible to hide.

The mistake we keep repeating

Let me describe how it usually goes, because most of us have lived some version of it.

The business sends over a request. We, as data and AI people, tend to believe we have understood it the moment we read it. And we start building, immediately.

Three or four months later, a stakeholder asks the questions that should have been answered at the start. Why are we doing this? What was the actual problem? What were we trying to achieve with this model, this dashboard, this agent?

What’s become clear is that this is a management problem, rarely the technology itself. It sits at the interface between business and data teams, right at the start, in how the use case gets defined. We tend to reach for technical solutions first, but we need to take a step back and make sure those initial conversations happen.

The principle I keep coming back to: fall in love with the problem, not the solution. You have to understand the true problem before you have any right to talk about how to solve it.

Usage is a proxy, not value

This is the trap a lot of teams fall into. We measure a use case by adoption. The dashboard gets opened a lot, so the assumption is that it creates value. The copilot gets used every day, so we assume it is working.

But adoption is only a proxy. I’ve seen enough cases where usage is high but value is zero. Or even worse: usage is high and value creation is actually negative.

So when somebody shows me a graph of adoption climbing up and to the right, my first question is always the same: What is the business value being generated? More revenue, fewer churned customers, lower cost to serve? If the line goes up and nothing moves in the business, I get suspicious. Adoption is an important, supporting signal but it’s not value.

The value barely ever sits in the solution. Technically speaking, a model has a value of zero. A dashboard has a value of zero. Data has a value of zero. They have costs. And they only generate value in the context of the business problem they solve. Get the problem wrong, and there’s no value to attach the solution to.

Why use cases fail before they start

No use case, no value. What I mean by it is if you can’t clearly state the business problem, who owns it, what success looks like in business terms, and what it requires to get there, you have no basis for building, no basis for prioritizing, and nothing to point to when the bill arrives.

Many organizations struggle to do this for most of their AI use cases. When we look at why, the same three problems keep recurring.‍

1. ‍The use case was never actually defined. A request came in, the team assumed they understood the business need, and they moved straight to execution. The real problem, who owned it, what solving it would change, none of it was made explicit. The misalignment surfaces late, after significant investment, and significant credits.
2. ‍Value was assumed, not specified. Somebody believed the thing would be valuable, but there was no hypothesis. A value hypothesis is specific. This use case, if it works, saves X hours a week, or reduces churn by Y percent, or cuts cost to serve by Z. Something you can test or compare against every other option competing for the same budget.‍
3. There’s no system of record. The decision happened in a slide deck, and then everyone moved on. The data team went to the next initiative, the business stakeholder went to the next priority, and the use case is technically live. Today this gets managed in Excel, Miro, PowerPoint, or Confluence. All useful tools, but none of them are designed to actively manage value across the full lifecycle. So nobody can say whether it worked, and the credits keep being charged.

All three trace back to the same root cause. Value was never made explicit before the work began, so it can never be measured after, and never justified on the invoice.

"But we have use cases"

Many leaders tell me they already do this. They have a backlog, an intake process, a prioritization framework. My response is always the same: I ask to see the value hypotheses for their top ten use cases. That request usually exposes the gap.

Having a use case on a list is not the same as managing one. A Jira ticket, a line in a roadmap, a request that got triaged into the backlog, none of those is a use case in the sense I mean. They record what someone wants built. They rarely record why each item matters, what success looks like in business terms, or how you'll know the money was well spent.

A properly managed use case has a business owner, not only a data owner. It has a value hypothesis specific enough to compare against everything else competing for the budget. Success was agreed before building started, not invented when the review came around. And someone stays accountable for tracking whether the value actually showed up, long after the technical work is done.

A use case, properly managed, represents the business context. All your use cases represent the business layer. A semantic layer of business problems and value potentials. The foundation to demonstrate what you're trying to achieve and at what cost. Most organizations have a task layer, not a value layer, and in the AI era, that gap now comes with a monthly invoice.

Also watch: How Data and AI Impact Management Unlocks Business Value

Output vs outcome

There’s a mindset underneath all of this, deeply rooted in product management: outcome over output.

I usually explain this with a simple example. If I bring 200 slides and you find two of them valuable, my output was 200 and my outcome was two. The other 198 had a cost. They just didn’t have a return.

For years, data teams have been measured on output. This could be model accuracy, dashboards shipped, or clicks.

“Here is the dashboard, good luck with it.” Data teams have been handing things over the wall for years, and being measured on the handover, not the impact.

The shift happens the moment a team starts thinking in outcome. Outcome is the part of the output that creates value for the person on the receiving end. And when a data team makes that shift, the way the business sees them changes. They stop being a cost center that hands work over the wall. They start being able to show how much value they generate and what it costs to generate it.

That is the move from cost center to profit center.

The numbers are already there

This is not a theoretical concern. At this year’s Gartner Data & Analytics Summit in London, the overarching theme was “value”. How do you prove it? How do you prioritize for it? More than twenty sessions circled the same question.

The research behind that challenge more or less points in the same direction. Gartner found that only 28% of AI use cases in infrastructure and operations fully meet ROI expectations. PwC found that 56% of CEOs reported no meaningful revenue or cost improvement from AI. And Bain & Company found that despite 81% implementing beyond pilots, 64% struggle to demonstrate clear ROI or business value.

Think about what this means in practice. You built something, ran it long enough to realize it was not working, and the entire time it ran, it was generating a bill.

That pressure is landing on real people. 61% of senior leaders now feel more pressure to prove the return on AI investments. And the leaders feeling most of it are the ones who approved the investment and can’t show what it returned. In almost every case I’ve seen, the reason is the same. Value was never managed from the start. The invoice just made the gap impossible to ignore.

Also read: The Trillion Dollar Question: Why Can’t 56% of CEOs Still Not Prove AI's Value?

Where do you start?

When people ask me where to start, I usually ask two questions. Do you really want to manage value in the future? And if so: are you willing to manage the business context professionally?

Why I’m asking is because it requires commitment, endurance, consistency, and investment, just like any other goal.

If you can answer both questions with a clear “Yes”, here are the best tips as of today:

1. Start with what you have. I get asked all the time when the right moment is. We are not mature enough yet, we are at the very beginning. You can’t start early enough. If you have ten AI use cases sitting in a collection right now, take those ten and write down, for each one, what the problem is, what the outcome should be, what the value hypothesis is. There is no too early. Everything you save by waiting, you pay back later at double or triple the effort.
2. Bring the business into the room. I still see data teams designing use cases bottom up, from the data, with the best intentions, and never really involving the business. The business people are the experts on the business problem. We are the experts on the solution. The conversation worth slowing down for is the one before the building starts. They own the problem, we own the solution, and neither side should be doing the other's job.
3. Be honest. Do not oversell, and do not overpromise value you are not yet sure how to deliver. I’ve seen plenty of cases where the business attached enormous numbers to a use case, the portfolio filled up with figures nobody could defend, and the whole value exercise fell over the first time someone challenged it. Bring realism in early. A value that is too high is as useless as one that is too low.

None of this is rocket science or magic. It’s a systematic approach. A joint effort between business and data and AI teams. To demonstrate business impact - finally.

The CFO is not going to stop asking about the bill. And I personally do believe this matters beyond the budget pressure. The organizations that build this discipline are the ones that will actually realize the value they have been promising. They finally have the management layer to run their technology properly, and that is what makes the difference.

No use case, no value. The meter is running. How you define the use case, long before the bill arrives, decides whether that spend comes back as value.

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