AI Projects vs. AI Products
If you're serious about business value, you need AI Product Managers, not Project Managers.
#beyondAI
An enterprise decides to “do something with AI.” A promising use case surfaces. A team is assembled. Budgets are allocated. Timelines are drawn. And someone asks, “Who’s going to manage this project?”
That question, on its own, isn’t wrong.
AI initiatives, like any other initiative, require direction and effective delivery. But too often, we try to solve the delivery challenge before we've truly understood the problem. We treat AI initiatives as if we already know what needs to be built, and we assume that whatever gets delivered will automatically be adopted once it’s rolled out.
But if we’re being honest, many of those promising AI use cases are framed by external consultants eager to sell their services, or by managers who may understand the business context but don’t have the time to sit down with real end users. They rarely investigate what needs to be solved first or how a solution should evolve with user feedback. The thinking starts and ends with solutions, not with adoption.
And when you think in solutions, then yes, execution becomes your focus.
But the reality is this: building AI for internal use doesn’t fail because teams can’t execute. It fails, just like many technologies before it, because users don’t adopt what’s been built.
And when I talk about adoption, I don’t mean rollout. I mean real usage. I mean users solving their problems with your product, so that the business starts seeing real value in return.
Now, if you can answer the following question with a confident yes, feel free to skip the rest of this article:
“Do you really know that what you’re building will be adopted?”
Why Project Thinking Doesn’t Work for Internal AI Initiatives
I see a project as a clearly defined mandate: deliver something based on predefined requirements, with a set start and end date, and within a fixed budget and resource plan.
That’s a project.
And yes, building AI systems can be handled as if they were projects. Someone defines what needs to be delivered, what resources are available, and by when it should be done. Then the execution begins.
But we also need to ask: what is the deeper motivation behind delivering AI systems inside companies?
The main ambition — and it’s the core promise that’s fueled the AI hype — is simple. AI is expected to reduce or avoid cost, and ideally increase revenue. It’s positioned as a technology that can offer insights and generate content at scale, enabling companies to operate far more effectively and efficiently. So even though we may phrase AI use cases as technical solutions, they come with very real business expectations.
And here’s the tension: if the expectation is business value, then simply delivering an AI system is not enough. You may have executed successfully, but still failed to meet the expectation.
Project Management helps you deliver something. But it doesn’t guarantee that what you deliver will generate value. So the real question is: why are so many companies still treating AI as if it were just another IT project?
If we take the business ambition seriously — if we believe that AI is meant to deliver measurable value — then we need to stop thinking in terms of projects. We need to think in terms of products. And just by using the word “product,” we already imply a responsibility toward sustainable, economic value creation. Projects, in contrast, are primarily measured by efficiency: did we deliver on time and within scope?
Those are two entirely different goal systems.
When we treat internal AI solutions as projects, we imply that success is predictable. That if we define the scope clearly and execute well, value will follow. But if that were true, we’d all be billionaires by now. We would just need to execute AI projects efficiently, and the money would come.
Of course, reality tells us something else.
Startups are the perfect example. Some set up their entire organization, from team to product to operations, and still fail. They execute well, and yet they miss the target. Which proves something important: value is not created through flawless project execution. It is created through product-market fit, adoption, and constant iteration.
And the same is true for AI in companies.
Every internal AI initiative carries an economic ambition. But we can execute flawlessly and still fail, and many companies are now seeing exactly that. Even those who claim high adoption rates often admit they don’t see any impact on their bottom line.
That’s the real red flag.
Because if adoption is high, but the financial benefit is missing, something is off. And what that usually means is this: the solution may be valuable to users, but it is not valuable to the business.
This isn’t rare. In fact, it often traces back to the earliest phase of the initiative, where decisions are made about where to invest time and resources. Too often, those decisions aren’t grounded in a clear understanding of how value will actually show up in business terms.
So, if we want to increase the chances of creating value — not just for end users, but for the business as well — we need to change the way we approach AI initiatives. We need the right frameworks, the right mindsets, and the right operating models.
And in my experience, no successful internal AI initiative was ever truly a project. Some were labeled as such, but if you look closely, the ones that succeeded didn’t follow classical project patterns. They did everything necessary to ensure real impact, even if that meant going far beyond the original project scope.
The uncomfortable truth comes when those teams, having built something that works and delivers value, realize they’ve just taken the first step. Because once a “project” is delivered, the real work begins. Suddenly, it’s no longer a one-off project. It’s an ongoing responsibility.
What do we call an ongoing project with no real end?
I’d like to call it a product ambition.
A product, in the best case, runs an indefinite number of projects — each designed to keep it relevant, effective, and trusted. And once you see AI solutions through that lens, the need to build a proper product organization becomes clear. Classical project delivery setups simply don’t support these ambitions.
In the worst case, companies make AI investments based on the assumption that they’ll pay a one-time delivery cost, without considering the long-term operational or maintenance costs. That’s how bad investment decisions are made. That’s how ROI expectations go unmet.
So yes, I’m not a believer in AI projects.
They’ve never delivered on the promises the AI industry continues to make.
Those promises require a deep shift: a real product operating model, a product-driven mindset, and people who know how to manage all of this in one cohesive effort.
Only then can we build AI solutions that do more than just function.
They deliver value — and they continue to do so.
AI Project Management Still Matters
Now, just to be clear: I am not arguing that project management is useless in AI initiatives. Far from it.
Project management still matters — especially once the fog begins to clear.
Every product ambition, once it reaches a certain level of maturity, needs structure. It needs clear priorities, reliable timelines, and accountability. It needs someone to drive progress, manage complexity, align stakeholders, and make sure that what’s been decided actually gets delivered. In this sense, project management brings discipline to the chaos. It’s the engine room that ensures the ship keeps moving.
And if you’re a Product Manager, especially in the AI space, you’ll quickly realize this: at some point, you will need to act like a Project Manager (and in agile contexts they might be named a Product Owner). Not because your title says so, but because the product demands it.
Once you’ve shaped the problem space, validated the key assumptions, and defined what success looks like, the focus shifts. You move from learning to delivering. And when that happens, it’s not strategic vision that gets the product over the line. It’s a clear execution.
That’s where the strengths of project management come in.
And the best AI Product Managers know how to borrow from that skillset when the time is right.
You need to plan without becoming rigid.
You need to track progress without turning into a micromanager.
You need to manage scope and expectations while still keeping your eye on long-term value.
It’s a balancing act — and the more mature your product becomes, the more of that balance is required.
But here’s the key difference: as a Product Manager, you never fully become a Project Manager. You carry a different mandate. You are still responsible for the direction, for making sure the team is solving the right problem in the right way, for protecting the product from drifting into mere output.
So, yes, you adopt project management skills.
You just don’t lose your product mindset in the process.
In fact, I’d argue
The strongest AI Product Managers are those who can switch seamlessly between strategy and execution.
They know when to step back and reframe the problem, and when to lean in and drive delivery. They don’t draw a hard line between “thinking” and “doing.” They understand that impact requires both.
This is especially important in internal AI product work, where the boundaries between roles often blur, and where the absence of clear product ownership makes it easy to fall into delivery for delivery’s sake.
So, rather than dismiss project management, let’s integrate its strengths.
Because once your strategy is in place and your product has a clear path forward, execution becomes the strategy.
And if you don’t own that execution, someone else will — and they might not be carrying the product's intent the way you do.
What We Really Need to Acknowledge
If there’s one thing I wish more companies would admit out loud, it’s this:
AI is not just a technical initiative. It’s a product journey.
And as long as we treat it like a project, with fixed timelines, one-time delivery expectations, and predefined scopes, we will continue to fall short of its potential. Not because the teams aren't capable. But the framing is wrong from the start.
AI products live at the intersection of uncertainty, complexity, and change. They don’t just automate tasks. They reshape how people work. They challenge established processes. They require trust, behavior change, and new ways of measuring success. And that’s exactly why they need product thinking at the core.
The uncomfortable truth is that AI success doesn’t come from good project management alone. It comes from having people who know how to manage assumptions, not just resources.
People who understand how to balance strategic ambiguity with operational clarity.
People who can translate potential into progress, even when no one is quite sure what “done” really looks like.
And that means building your AI initiatives around Product Managers, not just Project Managers.
It means giving AI efforts the same treatment you’d give any real product: a clear strategy, ownership across the lifecycle, and an operating model designed for continuous discovery and delivery.
Because
if you want AI to be more than a prototype or a solution that no one uses, you need to treat it like something that lives. Something that evolves. Something that will never be “finished.”
And that, by definition, can’t be a project.
JBK 🕊️