How Structureless AI PoCs Lead Nowhere
Everyone agrees by now: AI-first thinking doesn’t work.
But here’s the thing — it still happens, all the time.
You might not hear anyone say it out loud anymore, but you can see it in action every day:
“We’ve got this data — can we do something with it?”
“Is there any way AI can help us monetize this?”
These behaviors still drive many projects forward — often unconsciously.
This isn’t just a messaging problem.
It’s a structural issue that leads to a common outcome:
PoCs launched without a solid business scenario tend to become technical experiments that fail to inform meaningful decisions.
Separate Ideation from Business Logic
Phrases like “Can we do something with this data?” aren’t necessarily bad starting points. They can serve as prompts for exploration.
But the real problem arises when such phrases are interpreted — or used — as concrete strategic instructions, without being reconstructed into a valid business scenario.
Sometimes, frontline staff interpret them as directives from leadership and proceed with PoCs without any hypothesis or business validation logic.
Other times, the person who made the comment genuinely believes in the power of AI to “figure something out,” and ends up rejecting more structured, hypothesis-driven approaches proposed by others — seeing them as off-target or unnecessary.
In both cases, the result is the same: a project built on vague expectations, where AI is implicitly expected to deliver value on its own.
It proceeds in form but lacks the structure needed to drive real decisions — and quietly fails to produce outcomes that matter.
Business logic only works top-down
When AI and data initiatives fail to deliver value, it’s often because the upstream business scenario wasn’t designed in the first place. What generates business value is a scenario like:
What value will we provide, to whom, and what problem are we solving to achieve that?
Only within such a structure can the role of AI or data be determined.
This order can’t be reversed.
Starting with the idea, “Can we do something with AI?” is fine in itself. But to turn it into a real business initiative, you’ll have to reconstruct the logic in this order:
Value → Purpose → Problem → Solution Strategy → Tools
You have to build from the trunk of the tree — business fundamentals — and only if necessary, tools like AI and data emerge as a means to an end. This is a top-down construction rooted in customer value and business vision.
If you start from the branches — AI and data — that aren’t necessarily connected to the trunk, the business rationale for investment becomes vague. This is a bottom-up construction that starts from tools. And since computing power can deliver high output, it’s like having a powerful engine with no compass — the risk of missing the mark increases dramatically.
Even if the initial idea came bottom-up from a business hunch, you need to restructure it top-down when building business logic.
The structure of the field is also to blame
In practice, many projects proceed to PoC with only the decision to “use AI and data” having been made — often between executives and sales — with no clear hypotheses or problem definitions. Meanwhile, the field teams face structural limitations in objectives, budgets, and time, leaving no room to even propose a proper hypothesis-testing process.
As a result, PoCs end up being just technical experiments, disconnected from real business decision-making.
These structural issues are often beyond the control of field teams. Either the upstream business logic is missing, or the motivation behind the investment is vague — like, “It’s DX, so we have to do AI.” (Sometimes this stems from investor pressure.)
Once execution begins, it’s very hard to “enlighten” stakeholders. And team members who try to point out the structural issues are rarely rewarded — sometimes even seen as obstructive — and there’s no room for detours.
In fact, in some cases, the most “rational” move for those facing unreasonable project structures is to quietly navigate around the core problems, produce a nominal success report, use it for PR, and exit early. The stronger the emphasis on “success” and career advancement, the stronger the incentives for this kind of outcome.
Hypotheses and problem setting must come first
This also affects how success is defined, but valuable business decisions don’t come from simply adopting outcomes that happen to look good — like an obvious correlation.
They come from making the right decision based on sound business logic, and then using data to support and validate that logic.
To do that, you first need to be able to ask:
How do we form a hypothesis?
What exactly is the problem?
Say you want to improve ice cream sales, and you suspect that sales are related to temperature. That’s a hypothesis. How would you verify it? Maybe by incorporating weather data.
What matters is that you had a clear hypothesis — which told you what data was necessary.
On the other hand, if you just analyze sales and customer data blindly, without knowing what you’re trying to decide, you won’t gain meaningful insights.
This is where the gap lies between “AI and data” as a starting point and a practical approach.
Note:
As of early 2025, executing general-purpose analysis has become significantly easier thanks to the integration of language models and analytics tools. For example, you can simply say, “I want to do this kind of analysis for this purpose,” and the language model will propose a method — and in some cases, even carry it out end-to-end. In fully automated setups, the process is handled entirely by the model. In other cases, a human intermediary — using the model to research methods — enables the same result seamlessly. This is no longer hypothetical; it’s already real.
In this context, language models serve as a tool for directing the analysis, while analytics tools carry out the execution.
That’s why what becomes even more important is the ability to ask:
“What should we analyze — to support which business value?”
“What else needs to be validated — beyond analysis itself?“
Scenario design — the logic behind the validation process — now holds even more value.
That kind of knowledge, judgment, and creative flexibility belongs to the domain of decision-makers and management. And its importance has only increased.
Structural thinking is also critical for investment decisions
When planning investment objectives, the absence of the following distinction increases risk:
- Is the goal short-term and measurable (e.g., cost reduction)?
- Or is it long-term and qualitative (e.g., developing new services or repositioning a brand)?
If this distinction is blurred, and all initiatives are evaluated by uniform KPIs or ROI logic, then meaningful long-term projects get marginalized. Investment and strategy instead get funneled into low-risk, low-return areas — which are highly measurable, profit-linked, and crowded with competitors. Capital easily defines the winners there.
What matters is whether the PoC aligns with your intent
Messages like “Let’s leverage AI and data” still appear today — though perhaps with different intentions than in the past.
In my view, these kinds of messages sometimes act as a kind of litmus test, surfacing less experienced audiences — not by targeting them directly, but by seeing who naturally responds.
When outsourcing PoCs or implementation support, it’s important to be clear:
Is this about deploying tech and infrastructure,
or about supporting business hypothesis testing?
You need to communicate your intent — and understand the vendor’s intent as well.
In closing
To create value with AI and data, you need a logic structure that starts with:
Hypothesis → Problem Setting → Strategy → Tools
If you start with the tools, decision-making gets shaky, execution spins its wheels, and the investment won’t pay off.
You can start with “Can we do something with AI?”
But unless you have the structure to turn that into a value-creating process,
your chances of success will depend heavily on luck.
That’s the real trap in this space.
And to be clear: this article is just a way to supplement experience around PoCs.
There are organizations now that structure business logic from the start, with verification scenarios that go beyond technology. That accept escalations from the field, and correct their targets accordingly. Those organizations probably learned from some degree of PoC failure — and had people in management who took a fresh look at AI and data projects.
If the PoC you’re seeing right now lacks business logic, looks like a tech-only test, or reports success with unclear business impact,
then it might be time to review it from a management perspective.
LONGBOW provides advisory services—primarily in Japanese—focused on addressing the lack of business logic in PoCs and AI initiatives. We support clients from hypothesis formulation through to scenario design.
While our core services are in Japanese, we also offer English content for informational purposes. English support is available via written correspondence, such as email or shared documents.