From AI Use Case to Pilot Project

Many AI projects fail not because of the technology – they fail because no one laid the groundwork. No clearly qualified use case. No validated data foundation. Unresolved compliance requirements. Missing success criteria. Anyone who doesn't answer these questions before the actual project starts is building on sand.

This article shows what concretely needs to be done in the preparation phase to prevent an AI pilot project from heading into a dead end from the start – structured, focused, and without months of conceptual work. What your company needs to deliver and what we take on as part of our strategic consulting is shown step by step in this article.

What is an AI use case – and why is it the right starting point?

An AI use case is a clearly defined application scenario in which artificial intelligence improves, accelerates, or replaces a concrete business process. A good use case is not "introduce AI in the company" – that's a vision, not a use case. A use case is: "Automatically categorize and prioritize incoming support requests" or "Review contract documents for deviating clauses."

The use case is the right starting point because it deliberately limits complexity. Instead of converting an entire company to AI, you tackle a single, clearly measurable application case – and check all necessary prerequisites before the first development budget flows.

What is an AI use case? An AI use case describes a specific business process or decision situation where AI measurably helps – with clearly defined input, output, and success criteria. It is the starting point of every structured AI entry.

What makes an AI use case suitable for piloting?

Not every use case can be sensibly piloted. Three criteria determine whether a use case is the right starting point:

• Data availability: The necessary data already exists – in structured or at least structurable form. Projects that first need to collect new data are not suitable as an entry point.
• Measurable success: There is a clear KPI that can be used after the pilot to determine whether the system delivers what it should. Without measurable success criteria, no reliable rollout decision can be made.
• Limited scope: The use case is complex enough to show real value – but limited enough to pilot and evaluate it with manageable effort.

The three most common mistakes in use case selection

The patterns that cause AI initiatives to fail at this stage are almost always the same – and they arise long before the first model is trained. More on this here:

• The use case is too large: "Improve customer service with AI" is not a use case. "Automatically classify and route initial requests" is one.
• The team doesn't check the data foundation: Many assume that data is available and usable – and realize the opposite only when the project is running.
• No one defines success criteria: Without clear KPIs, a pilot cannot be evaluated. The result is endless discussions instead of a decision.

What the preparation phase delivers – and what it doesn't deliver

The preparation phase is not a project period in which an AI system is completed and delivers results. That would be an unrealistic expectation. What it delivers: creating the foundations on which an AI pilot project does not fail.

At the end of the preparation phase, you know whether your use case is viable. You know whether your data is sufficient. You know which compliance requirements apply. You have a clear picture of the pilot setup, defined success criteria, and a roadmap based on facts – not assumptions. The pilot project itself begins after that. But it begins on solid ground.

Phase 1: Use case qualification – who brings what?

The first phase has a clear goal: Together we clarify whether the chosen AI use case is viable – or whether we need to adjust it. It is deliberately kept short because speed is crucial here: Better to know early that a use case doesn't fit than late.

What your company delivers in Phase 1:

• An initial description of the business problem or process you want to improve
• An internal contact person with expertise about the process
• Access to existing data sources or a description of them
• An assessment of the urgency: Why is this topic relevant now?

What we take on in Phase 1:

• Structured use case analysis according to defined criteria (data availability, scope, measurability, risk)
• Workshop or structured interview for use case clarification
• Written recommendation: Pilot possible / adjustment necessary / different use case more sensible
• If needed: Suggestions for alternative or complementary use cases from comparable projects

Output Phase 1: Qualified AI use case with scope definition, initial success criteria hypothesis, and data pre-check.

Phase 2: Data check and AI compliance – who bears which responsibility?

The second phase is one of the most critical in the entire preparation process – because this is where the most frequent surprises await. Poor data and unresolved compliance issues are according to McKinsey the most common causes of failed AI initiatives worldwide. Anyone who recognizes them before the project start saves considerable time and costs.

What your company delivers in Phase 2:

• Access to the relevant data sources – directly or through a description of the database structure
• Information about internal data protection guidelines and existing GDPR consents
• Contact to the internal legal or compliance department, if available
• Clarification: Which data may leave the company – and which may not?

What we take on in Phase 2:

• Technical data check: Is the data complete, consistent, and AI-ready?
• AI compliance screening according to GDPR and EU AI Act: Which risk category does the use case fall into? Which requirements apply?
• Documentation of open compliance questions with concrete solution proposals
• Clarification: Which data do we need to prepare before the pilot?

What does an AI compliance check specifically include?

The EU AI Act, which has been gradually coming into effect since 2024, classifies AI systems according to risk classes – with sometimes significant consequences for requirements and permissibility. As part of the preparation, we pragmatically examine:

• Risk classification: Is it a minimal, limited, high, or unacceptable risk system?
• Data protection: Which personal data flows in – and on what legal basis?
• Transparency obligations: Do users need to know they are interacting with an AI system?
• Internal guidelines: Do existing IT security or data protection guidelines restrict the pilot?

AI Compliance Checklist before project start: ✅ Risk class of the use case determined according to EU AI Act ✅ Processed data categories documented ✅ Legal basis for data processing checked ✅ Transparency obligations towards users clarified ✅ Internal security guidelines aligned ✅ Open questions documented and prioritized

Output Phase 2: Data readiness report, AI compliance screening, list of open questions with solution proposals.

Phase 3: Pilot definition and AI project management – who structures what?

In Phase 3, we translate the qualified use case into a concrete pilot specification. This is not yet development. It is the precise planning that ensures we later develop in the right direction – and don't have to correct during the project.

What your company delivers in Phase 3:

• Final approval of the pilot scope based on the results from Phases 1 and 2
• Two to three internal test users who will actively use and evaluate the later pilot
• Confirmation of success criteria: What must the pilot show for a go decision to be justified?
• Feedback on queries within a maximum of 48 hours

What we take on in Phase 3:

• Technical pilot specification: Scope, architecture proposal, interfaces, model selection
• Resource and time planning for the actual pilot project
• Structured AI project management: Weekly goals, tracking mechanism, escalation path
• Abort criteria: When do we stop the pilot – before further budget flows?

What does sensible AI project management look like for a pilot project?

AI projects work structurally differently than classic IT projects. Requirements change, model outputs surprise, data quality turns out to be a problem. Whether agile or classic approaches work better depends on the context – for AI pilots, almost everything speaks for iteration.

For the pilot project, we rely on a lean, iterative model:

1. Weekly goals instead of milestones: Each week has a clearly defined result – no diffuse "we're working on it."
2. Transparent tracking: You can see at any time where the project stands and what comes next.
3. Escalation path: If a technical problem or compliance question endangers the pilot, we escalate immediately – instead of circumventing it internally.
4. Clear abort criterion: If it becomes apparent that the use case is not pilotable, we actively recommend stopping. This saves more budget than a bad pilot that we build to completion.

Output Phase 3: Complete pilot specification, resource and time plan for the pilot project, defined success criteria and abort criteria.

Phase 4: Success measurement and AI roadmap preparation – who decides what?

The final preparation phase ensures that a reliable decision is actually possible after the pilot. Because a pilot without previously defined evaluation logic provides no decision basis – it provides data that everyone subsequently discusses.

What your company delivers in Phase 4:

• Confirmation of the defined KPIs and thresholds for pilot evaluation
• Clarification: Who in the company makes the final go / no-go / pivot decision?
• Initial assessment: Which follow-up projects would be considered in case of a successful pilot?

What we take on in Phase 4:

• Setup of the reporting framework for later pilot evaluation
• Template for pilot documentation: What do we measure, how, by whom?
• First draft of an AI roadmap structure – as a basis for strategic planning after the pilot
• Closing document of the preparation phase: everything that enables the project start in one structured document

Which three decisions can follow after the pilot?

We define the evaluation logic now – not only when the pilot is completed. Three outcomes are possible, and all three are valuable:

A no-go is not failure – it is a result. It prevents months and budgets from flowing in a direction that would not have worked. That is the actual value of careful preparation.

How do you build an AI roadmap from a successful pilot?

If the pilot receives a go decision, the crucial foundations for a reliable AI strategy are on the table: a validated use case, a verified data foundation, clarified compliance requirements, defined KPIs, and first real performance data.

On this basis, an AI roadmap can be built that is based not on assumptions but on facts – with realistic time horizons, prioritized use cases, and clear resource requirements for the next 6 to 12 months.

Why good preparation significantly reduces the risk of poor AI projects

The alternative to structured preparation is well known: dive directly into development, with assumptions about data and scope, unresolved compliance questions, and missing success criteria. This almost inevitably leads to an expensive dead end.

McKinsey documents that companies that enter AI with a use-case-based approach and clear preparation structure achieve significantly higher success rates than those that directly start large AI transformation projects. The logic is simple: Those who answer the right questions before money flows make better decisions about whether and how money should flow.

Which bottlenecks cause AI initiatives to fail most frequently – and how to structurally avoid them – we have written separately.

Conclusion

Good preparation does not guarantee a successful AI pilot. But it prevents a pilot from failing for the wrong reasons – because of unresolved data questions, missing success criteria, or unknown compliance risks.

What your company must bring: an initial AI use case, data access, internal contact persons, and decision-making readiness. What we bring: structure, analysis, AI project management, and an honest assessment – even when it states that the chosen use case is the wrong entry point.

Would you like to know whether your AI use case is suitable for a pilot project – and what would need to be clarified concretely in the preparation phase? Talk to us in a free initial consultation, non-binding and directly related to your use case.