Many decision makers fail not because of the technology, but because of a lack of structure: too high expectations, too broad goals, or a pilot without success criteria.
Getting started is much easier when you have your first AI Use Case Treat it like a product: with a target image, database, test plan and measurable benefits.
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Step 1: Identify processes (time wasters, sources of error)
A good AI Use Case Is small enough to learn quickly, but relevant enough to show real added value.
Don't start with the question “Which AI do we want to use? ”, but with “What problem is costing us time, money or quality today? ”.
If you clarify early on who feels the benefits in everyday life, acceptance increases almost automatically because the team experiences the improvement instead of just hearing about it.
At the same time, it's worth taking a quick look at governance and risk, because depending on the area of application, requirements for transparency, documentation and human supervision can become relevant. (EUR-Lex)
Find an AI use case: processes with potential benefits
The first step is always the process view, not the tool.
Collect candidates where there is noticeable friction today: media breaks, manual transfers, long turnaround times, many queries or recurring errors.
Practical heuristics for an AI use case: When employees say “I do it the same way every day,” that is a signal. When they say “It's different every time,” you need very clear criteria and data.
Systematically identify time wasters and sources of error
Go through 2 to 3 core processes with departments and mark:
- Steps involving a lot of manual effort
- Steps with Quality Issues or Complaints
- Steps that delay decisions due to lack of information
Supplement this with simple figures: How many cases per week? How much time per case? What are the costs of errors?
This creates a resilient longlist that does not consist of good feeling.
Prioritizing for the First AI Use Case
Prioritizing 2 by 2 has proven effective:
- Business benefits high or low
- Feasibility high or low
Your first AI Use Case Should be “benefits high, feasibility high.” Everything else is more like step two or three, not the start.
Step 2: Assess the data situation (what is available, what is not?)
In the second step, it is decided whether an idea becomes a project.
For everyone AI Use Case The following applies: Without suitable data, there is no stable production use, at most a demo.
Therefore, check early on which data is available, how it is maintained and whether it can be used legally and organizationally.
Create data inventory for the AI use case
Create a short, practical overview:
- What sources are there (ERP, CRM, tickets, email, documents, sensors)?
- Which fields are decisive for the use case?
- How up-to-date is the data and how complete?
- Who is the data controller and how is access regulated?
The problem is often not “too little” data, but inconsistent formats and lack of access channels.
Realistically assess data quality and bias in the AI use case
Ask specifically:
- Does the data set reflect reality well or only partial areas?
- Are there systematic gaps, for example specific customer groups, regions or products?
- Are labels or target values reliable if you want to train a model?
If you work cleanly here, you'll save weeks later in the pilot phase.
Established frameworks such as the NIST AI RMF help to think about risks in a structured way because they consider risks not only technically but also organizationally. (NIST publications)
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Step 3: Select a suitable AI tool (buy vs. build)
The third step is about the tool and architecture decision.
The key question is: Are you buying a ready-made solution, configuring a platform, or building your own system?
There is no general answer. But there are clear criteria.
Buy criteria for the AI use case
A buy approach often works when:
- The process is close to standards, for example support classification or document search
- Time to value is more important than maximum individualization
- You have little in-house ML engineering or MLOps experience
- Compliance and operation should be supported by the provider
The advantage is speed and a predictable project scope.
Build criteria for the AI use case
A build approach often works when:
- The AI Use Case Is a real differentiator
- You use proprietary data or processes that do not cover standard tools well
- You need long-term control over the model, costs, and roadmap
- You already have engineering resources and operational expertise
In practice, it is often a hybrid: buy basic technology but control the technical logic, data pipelines and integrations yourself.
A techno economic analysis that compares productivity gains, costs and maintenance costs can also be helpful for the build versus buy balance. (EconStor)
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Step 4: Define pilot project (4-6 weeks)
A pilot is not a showroom, but proof under real conditions.
Bet for your first AI Use Case Deliberately set a clear time frame of 4 to 6 weeks so that decisions are not postponed and the focus remains.
It is important that the pilot comprises real users, real data and a real process step.
Pilot setup for the AI use case: goal, scope, roles
Before you start, define:
- Objective: What decision should be possible after 6 weeks?
- Scope: Which process step is being tested and what is deliberately left out?
- Roles: Professional Responsibility, IT, Data Protection, Security, Operations, Sponsor
In this way, you avoid the pilot becoming an endless project.
Define success criteria for the AI use case in advance
Set 3 to 5 measurable criteria, such as:
- Time saved per process
- Error rate or rework decreases
- Response quality or hit rate increases
- Acceptance within the team, for example usage rate or satisfaction
Only when these criteria have been defined is the technical design worthwhile.
Repeatable processes, from deployment to monitoring, are important for production-related pilots. MLOP's guidelines provide tried and tested samples for this. (Microsoft Learn)
Step 5: Scaling & Performance Measurement (KPIs)
The fifth step is the difference between pilot and productive use.
Scaling does not mean “more users,” but also operation, responsibility, measurability and continuous improvement.
To do this, you need a KPI set, an operating model and clear rules on how changes are tested and rolled out.
KPIs for the AI use case: business, quality, risk
Think of KPIs in three levels:
- Business impact: time, costs, throughput, revenue, service level
- Model and Output Quality: Accuracy, Precision, Recall, Quality Ratings
- Risk and Operation: Failures, Escalations, Drift, Availability, Costs per Process
Especially with generative AI, you should define additional measurement points for output quality and usability, otherwise the benefits remain difficult to prove. (Google Cloud)
Monitoring and Drift: The AI use case does not automatically remain good
Models and data are changing. Processes are changing. User behavior is changing.
That is why a productive AI Use Case At least:
- Quality and fault monitoring
- Drift indicators for data and output
- Feedback loop from department and company
- Plan for retraining or rule adjustments
Practical advice on drift and monitoring can be found in AWS Prescriptive Guidance and Microsoft contributions to drift management, among others. (AWS documentation)
Governance and Compliance for the AI Use Case
Depending on the application, regulation can become relevant, particularly when decisions affect people, for example in HR, credit, insurance or security.
The EU AI Act follows a risk-based approach and places requirements on human supervision and other protective mechanisms for high-risk systems, among other things. (EUR-Lex)
In practice, this means: Check early on whether your AI Use Case Fall into a sensitive area, and document purpose, data, limits, and responsibilities from the start.
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Conclusion: Successfully complete AI use cases
If you consistently go through the five steps, a reproducible entry path is created that reduces anxiety and speeds up decisions.
You start with a clear problem, secure the database, choose the appropriate implementation path, test in a focused pilot and scale with KPIs and operating concept.
This is exactly where the AI Company helps: We help you get your first AI Use Case To prioritize in a structured manner, pilot cleanly and then safely put into productive use. If you want, we can talk about your initial situation, your data and a realistic pilot plan without obligation.
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Questions about the AI use case in the company
Which AI use case examples are suitable for the start?‍
Recurring, data-driven tasks such as ticket triage, document classification, summaries, knowledge search, forecasting or quality control are typical. It is important that the process is measurable.
How big should a first AI use case be?‍
Small enough to be tested in 4 to 6 weeks, but big enough to improve a noticeable bottleneck Ideally, it concerns a clear process step and a clearly defined user group.
When does buy make more sense than build with the AI use case?‍
Buy is often useful when the use case is close to the standard and should deliver benefits quickly. Build is more worthwhile when the use case is a differentiator or requires very specific data and logic.
What data do I need for an AI use case?‍
At a minimum: accessible data sources, sufficient quality and a clear understanding of which fields control output. For many use cases, a good, consistent database is more than a large amount of data.
Which KPIs are really decisive for an AI use case?‍
Choose a few but hard indicators: time savings, error rate, turnaround time, service level, and costs per transaction. In addition, there are quality metrics and operating figures such as drift or failure rates.
