How do you choose the first AI use case for your organization?
The introduction of Generative AI and Large Language Models (LLMs) has sparked a wave of enthusiasm in the business world. When a management team decides that the organization "needs to do something with AI," the first instinct is often to organize a broad brainstorming session. Almost immediately, the whiteboard fills up with hundreds of ideas: from automated marketing campaigns to fully autonomous customer service bots and smart contract analysis.
This enthusiasm is a valuable starting point, but without a rigorous selection method, it inevitably leads to paralysis. An organization cannot successfully implement twenty AI projects at the same time, especially not in the initial phase. The challenge lies not in coming up with applications, but in ruthlessly prioritizing them. How do you separate the hyped castles in the air from the pragmatic, value-creating projects? In this article, we discuss a structured method to inventory and score AI use cases, so that your first project lays a solid foundation for broader AI adoption.
The pitfall of wishlist inflation
A common phenomenon in initial AI explorations is what we call 'wishlist inflation.' Teams allow themselves to be guided by what seems technologically possible in demonstration videos, rather than looking at what the business actually needs. The result is a list of 'nice-to-have' features that are technically complex, solve few business pain points, and often end up as failed Proof of Concepts (PoCs).
To prevent wishlist inflation, you must reverse the inventory process. Do not start with the technology, but with the existing friction in your business processes. Do not ask your employees: "How could you use ChatGPT in your work?" This question steers toward technology-driven solutions. Instead, ask: "Which repetitive, text-rich tasks take up most of your time each week, but require the least strategic thinking?"
By focusing on pain points, throughput bottlenecks, and repetitive administrative work, you build an inventory based on actual operational needs.
The fundamental difference: The learning pilot versus the value pilot
Before you start prioritizing the use cases, it is essential to determine what type of pilot you are going to run. Not every AI project has the same goal in the initial phase. We distinguish two main categories: the learning pilot and the value pilot.
The Learning Pilot (Focus on adoption and culture)
A learning pilot is designed to familiarize employees with AI concepts. A typical example is providing Microsoft Copilot or ChatGPT Enterprise licenses to a selected group of employees, with the freedom to discover applications themselves. The primary goal here is cultural change, adoption, and overcoming initial hesitation. The ROI is indirect and difficult to measure in hard figures. A learning pilot is fantastic for building support, but should never be presented as a project that will immediately save costs.
The Value Pilot (Focus on measurable ROI)
A value pilot is a tightly defined project focused on one specific process. For example: automatically categorizing and summarizing incoming emails for the customer service department. Here, the goal is indeed measurable: reducing the Average Handling Time by an x percentage. For a structured 30-day AI pilot, you always choose a use case that qualifies for a value pilot. Only by showing hard, measurable results can you convince management to free up budget for subsequent steps.
The 4 pillars of use case prioritization
Once you have identified a list of 10 to 20 potential value pilots, you must rank them objectively. The most effective method is to assess each use case along four critical axes: Business Value, Technical Feasibility, Data Readiness, and Risk.
1. Business Value
What is the potential impact if this use case is successfully implemented? Value can manifest in various forms:
- Time savings and efficiency: Reducing hours spent on manual work.
- Quality improvement: Reducing human errors in document review.
- Customer satisfaction: Shortening response times for support tickets.
For a sound estimation of the financial impact, it is advisable to calculate the AI ROI beforehand. This provides a baseline against which you can measure the outcome of the pilot.
2. Technical Feasibility
How difficult is it to build this solution today? This requires a realistic look at the current state of the technology. An LLM is excellent at summarizing text and extracting entities from documents. However, an LLM is unsuitable for complex mathematical predictions without external tools. Questions you should ask here are: Is integration with our current systems (APIs) possible? Do we have the right internal or external expertise in-house?
3. Data Readiness
This is the most underestimated pillar in B2B AI projects. An AI model is only as good as the data it has access to. If you want to build a chatbot that answers employee questions about HR policy, that policy information must be structured, up-to-date, and digitally searchable. If the required data is fragmented across ten different, closed systems in outdated PDF formats, your data readiness is low.
Often, Retrieval-Augmented Generation (RAG) is used to feed the data to the model. For a successful first use case, you need data that is relatively clean and easily accessible. For more technical context on this, you can refer to external sources such as this explanation of RAG for beginners.
4. Risk and Compliance
Every use case carries risks. What is the impact if the model hallucinates (generates incorrect information)? With an internal tool that summarizes long contracts for a lawyer (who performs the final check), the risk is low (human-in-the-loop). With a fully automated chatbot that emails legally binding promises to customers, the risk is unacceptably high for a first pilot.
In addition, you must take privacy legislation (GDPR) into account. Use cases that process a lot of Personally Identifiable Information (PII) or medical data automatically score lower on desirability for a first project due to high compliance requirements. Drafting a sound AI policy helps to establish frameworks for these risks.
Why 'Volume × Time Savings' predicts better than enthusiasm
When scoring Business Value, many organizations fall into the trap of the 'wow factor.' Suppose the marketing department wants an AI tool that generates fully personalized, interactive corporate videos based on customer data. The enthusiasm in the boardroom is enormous. It sounds revolutionary. However, this task may currently only be performed twice a month, costs a lot of money to integrate, and is technically very complex.
On the other hand, there is the purchasing department. They receive 200 long supplier terms and conditions in PDF format daily, where an employee must manually search for deviating payment terms. This is an extremely boring task that no one gets excited about. But the math doesn't lie: 200 documents per day × 5 minutes savings per document = over 16 hours of savings per day. Furthermore, the technical complexity (text extraction) is very low.
For your first use cases, the boring, repetitive, high-volume task is almost always the better choice. It delivers results faster, touches the core of operations, and funds more experimental projects in the future through the realized savings.
Risk spreading: Avoid dependency on a single department
A critical point in the prioritization phase is broader adoption within the organization. If you select the top three use cases and they all turn out to relate exclusively to the IT department (for example, AI code generation), then AI within your company will unintentionally be labeled as 'an IT party.' The same applies if AI becomes purely a marketing toy; you then risk the IT department blocking implementation due to security concerns.
Therefore, ensure that your prioritized list is diverse. For the first series of pilots, choose projects that affect different departments. This builds broader support and ensures that knowledge about AI integration spreads throughout the entire organization. A cross-functional steering committee is essential to keep this process on track.
The AI Use Case Scoring Matrix
To turn theory into practice, you can use the matrix below in your decision-making process. Have your working group score each use case on a scale of 1 to 5 per category. Then, add up the scores. The projects with the highest total score rise to the top of the pile for execution.
| Criteria | Score 1 (Poor/Low) | Score 3 (Average) | Score 5 (Excellent/High) | Weight |
|---|---|---|---|---|
| Value (ROI & Volume) | Incidental use, marginal time savings, or abstract value. | Regular use, saves 1-2 hours per week per employee. | High volume, daily use, structural cost or time savings. | x 2 |
| Feasibility (Technical) | Requires fundamental research, no standard models available. | Combines existing APIs, requires some custom integration. | Standard functionality of current LLMs, easy to fit into workflow. | x 1 |
| Data Readiness | Data is unstructured, analog, or heavily polluted. | Data is digital but scattered; cleanup is required. | Data is centralized, structured, clean, and directly accessible (API/DB). | x 2 |
| Risk & Compliance (Note: here 5 is positive = low risk) | High chance of brand damage, heavy GDPR data, no human control. | Internal processes, limited customer impact, human-in-the-loop. | Fully internal, no personal data, purely supportive. | x 1.5 |
Calculation example: A use case with Value (4x2=8), Feasibility (5x1=5), Data Readiness (3x2=6), and Risk (4x1.5=6) achieves a total score of 25 points.
Conclusion and next steps
Prioritizing AI use cases is not an exercise in technological optimism, but in sober business management. By countering wishlist inflation and systematically scoring projects on value, feasibility, data readiness, and risk, you significantly increase the chance of a successful first pilot. Remember that the most boring, high-volume processes are often the best candidates for AI automation.
Have you selected your first project using the scoring matrix? Then it is time to make the transition from theory to execution. The next crucial step is preparing to bring your selected pilot to production, anchoring the insights gained into your daily operations.