AI use case workshop with technical feasibility check | Anexia

For the AI potential analysis, we do not collect an endless list of possible model functions. We look at concrete tasks, how they are carried out today and the effort required for a usable result. A suitable use case needs usable inputs, a responsible business unit and a way of assessing quality. High visibility alone does not make an idea a good first project.

In the AI feasibility check, benefit assumptions and technical prerequisites are recorded separately. If data rights or reliable examples are missing, this is a prerequisite to be clarified and not a hidden implementation task. For the AI workshop, we also take into account integration, ongoing knowledge maintenance and human review. These factors can significantly change the expected advantage. The prioritised candidates receive a limited next step with a concrete learning question. A pilot should show, for example, whether answers are usable from a business perspective or whether documents can be structured reliably enough. The AI use case analysis thus delivers a traceable selection and an actionable test plan for the most important ideas.

The assessment within the AI feasibility check combines business impact with data access and verifiability. An attractive demonstrator is not automatically the most sensible first production use. For the AI use case analysis, we document the relevant assumptions and explain the reasons for the chosen option. Key alternatives are considered against the same business requirements. This makes later adjustments easier when load, data volume or organisational responsibility change.

A sound decision also takes maintainability and handover into account. In the AI potential analysis, we check which skills are available internally and what additional support is needed. This connects technical possibilities with a realistic operating model. The client can see which tasks will recur after the project and which decisions remain with them.

Estimated time savings can be completely eaten up by additional review and incomplete process integration. In the AI feasibility check, we treat this point as a concrete test question. A risk is linked to the affected process, its possible impact and a suitable measure. Where prerequisites are still missing, the statement is explicitly left open until the necessary investigation has taken place.

We test assumptions on real cases and define which metrics in the pilot decide on continuation or adjustment. For the AI workshop, we record the starting point, tested configuration and result. Acceptance refers to the agreed scope. Open limitations are documented visibly and assigned to an owned next step. This allows your team to distinguish between proven functionality, a deliberate decision and an assumption that has not yet been confirmed.

Project example as an illustrative scenario

The following scenario is an example application and not a customer reference. A company is considering AI for knowledge search, quote texts and document processing, but can initially only fund one use case. In the AI workshop, these candidates would be examined using real work examples. For each task, the current effort, available data and the consequences of incorrect results would need to be clarified. An impressive demonstration alone would not justify a priority.

For the AI use case analysis, the participants would also consider the later review and integration effort. A text function could be prototyped quickly but require a high level of business rework. A smaller document process, on the other hand, could have clear inputs and measurable results. Missing rights of use or inconsistent master data would become visible as prerequisites. For the preferred candidate, the workshop would formulate a verifiable question: which task is to be done better under which conditions, and how would an inadequate result be recognisable?

The result of the AI potential analysis would be a reasoned selection with a concretely defined pilot assignment. It would cover the required data, business review criteria and a decision on continuation or termination after the trial. The remaining ideas would be documented together with their open prerequisites. This would allow the company to commission a first step without anticipating a comprehensive AI platform or an undefined scope of transformation. The prioritisation would remain open to review when new information becomes available.

Our experience and our engineering approach

We combine custom software development with the technical tasks of rollout and further development. For the AI use case analysis, we bring the perspectives of application, integration and operations together in a shared workflow. The business requirements remain connected with the technical decisions. You work with a defined scope of services and traceable results.

Our experience should be concretely verifiable for your project. That is why, in the technical discussion, we explain how we investigate the relevant risks, which work results are planned and how your team can assess them. For the AI potential analysis, we align the roles involved with the actual need. Available references are placed in context with the respective project and company; confidential customer details are not used as general advertising claims.

Effort for the AI potential analysis

The number of participants, data preparation and the desired technical trials determine the scope. We therefore calculate the AI workshop on the basis of a clearly defined scope and explicitly stated prerequisites. An initial analysis step can make sense if key technical information is still missing. Work packages and acceptance criteria can then be agreed more reliably.

The AI use case analysis can be organised as a limited project, as a joint implementation with your team or with subsequent support. Rights of use, access, documentation and responsibilities are clarified in the contract. Ongoing operating hours, response times and external consumption costs are separate agreements. This makes it clear which services are provided once and which tasks recur after rollout.

Scope limits of the AI workshop

The workshop does not provide a blanket guarantee of cost-effectiveness and does not replace a later review of actual usage behaviour. For the AI feasibility check, we already set out this boundary in the offer. Additional services that are needed are included with their business justification and are not silently assumed. This allows your team to plan suitable internal skills and divide the overall work sensibly between those involved.