Getting started with AI: how to choose the first use case?

At Bright Cape, we believe that data is the engine behind operational success, but only in combination with the human side of the organization. Implementing AI and Agentic AI solutions can help companies improve efficiency, reduce costs, and get more work done in less time. However, AI is not a universal solution. Without a clear value strategy and the right investment in teams and infrastructure, AI remains a promise instead of becoming a catalyst for growth. 

That is why we do not focus only on the technology. We also share insights on how to develop realistic expectations of AI, ensure data quality, and maintain human control. 

Step by step, we guide you from the first introduction to AI within your organization to embedding a data-driven mindset more broadly. With AI, but without losing sight of the human component. 

In this blog, we zoom in on a question many organizations face when they want to start with AI: what is the right first use case? 

A good first AI use case should do three things: deliver value, be feasible, and create support within the organization. In practice, this means you do not start with technology, but with processes. After that, you determine which use cases have the most potential and assess whether the organization is ready to work with them. By following these steps in the right order, a realistic starting point for AI is created. 

Many organizations begin their AI journey with a tool. A new technology becomes available, and the question is: what can we do with it? 

In practice, it works better to first look at the processes themselves. Where is a lot of time being lost? Which tasks are performed manually every day? And where do errors or delays occur? 

By answering these questions, a clear picture emerges of where AI can truly add value. Often, these turn out to be processes that are repetitive and follow a fixed pattern. 

Only when you understand how processes currently work, you can determine where automation and AI can really make a difference. 

Once the main bottlenecks in processes are visible, it becomes easier to identify potential AI use cases. The temptation is then to immediately tackle a large problem. 

However, it often works better to start with a small and manageable application that has a clear impact. In practice, these opportunities are often found in back-office processes, such as: 

• processing invoices from emails  
• classifying or summarizing documents  
• collecting and structuring information from systems  

These types of tasks occur frequently, follow a clear pattern, and require a relatively high amount of manual work. This makes them well suited for AI to quickly deliver productivity gains. 

By starting with these kinds of use cases, an organization can show initial results relatively quickly. This helps to build trust in both the technology and the approach. 

Not every use case that looks interesting on paper is automatically the right first step. It is therefore important to also consider the organization’s ability to adapt. 

In many cases, the biggest challenge is not the technology, but adoption. Employees need to learn how new solutions fit into their daily work, and teams need to get used to a different way of working. 

That is exactly why an iterative approach works so well. By starting small, teams can gain experience, discover what works, and build confidence in the technology. 

It often helps to begin with teams that include early adopters—employees who are open to experimentation and new technologies. Their experiences make it easier later on to introduce AI more broadly across the organization. 

For organizations that want to get started with AI, choosing the right first use case is an important step. Not every application is suitable to begin with. 

By first analyzing processes, then selecting a small but valuable use case, and taking into account the maturity of the organization, a realistic and effective starting point is created. In this way, AI grows step by step—from experiment to structural value for the organization.