Identifying Use Cases
This guide helps you to understand what great use cases are and how you identify them.
What are great use cases of AI?
Great use cases are situations and prompts/assistants that increase quality of your work or your product and/or reduce effort and time to get to a result.
In the beginning, we recommend to start with horizontal use cases, that are relevant for many people across all teams, ideally to everyone. This has the following advantages:
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Everybody understands the problem and can relate to the situation. This increases the willingness to learn how to build your own use cases and use AI in daily work.
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Horizontal use cases do not require as much customization. Often times, when trying to cover a deep vertical use case, many integrations and custom steps are needed, which is more effort.
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Deeper use cases are often more difficult to build and maintain. In the beginning, the collective knowledge of AI is not as deep yet, so instead it makes sense to focus on educating users with simpler cases first before diving deeper into more complexuse cases.
In the end, the email assistant, the document summarizer or the translator do not seem as exciting as a fully automated CRM agent. But these use cases are relevant in almost any organization and already help users a lot in their daily work.
How to find use cases
1. Experiment and understand the capabilities of AI
AI is good in performing specific tasks in different areas. In the beginning, it makes sense to let users experiment and learn about the different capabilities about AI. This experimentation should be paired with showing them example use cases as well as helping users organically coming up with their own use cases (see the following steps). Here is a collection of general capabilities:
| Text | Images | Audio (coming soon) | Data Analysis |
|---|---|---|---|
| Write | Create | Transcribe | Extract data |
| Summarize | Analyze | Speak | Perform analyses and calculations |
| Analyze | Describe | Identify patterns | |
| Answer questions | Extract text | Create tables and diagrams |
2. List daily activities
After understanding how AI generally works, ask users to list 5 activities that are repetitive and take a lot of time.
3. Collect activities
Collect activities in the entire group and cluster similar activities. If several people have the same or a similar use case, it might make sense to exchange experiences or work together on them.
You can use a whiteboard or a digital whiteboard (e.g. Miro, Mural, Figjam) for this activity.
4. Bring use cases and AI capabilities together.
Understand which use cases work with which AI capability from above. For example a translation use case would require writing text, a document summarizer needs text writing and text summarization capabilities.
5. Prioritize on what to work on first.
In every organization there are hundreds of use cases, where AI can help. Trying to start with all use cases at once often overwhelms users and in the end no use case is properly covered. The key is to focus on a few and to build them step by step.
In our experience, it makes sense to start with use cases where not much effort is needed to build them and that have a high impact for many people in the organization (see first section on this page).
You can use a 2x2 matrix to prioritize use cases. The different axes are feasibility and impact. Feasibility can be evaluated by:
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Effort - a lower effort makes it more feasible
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Data and attachements are ready. If data needs to be cleaned up or collected first, additional time is needed and feasibility is reduced.
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APIs, Integrations or code needed - many use cases are coverable by uploading a file from your computer or by using Langdock integrations. While Langdock also offers a range of APIs, actions and customization options, this increases effort.
Impact can be evaluated by:
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Time saved - how many hours can be saved by week/month for how many employees?
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Quality gains - how much better is the quality of the output? Are errors reduced?
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Customer satisfaction - does this use case improve service quality or speed?
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Financial impact - is there potential to save costs or increase revenue?
After prioritizing the use cases, start with the high-impact use cases that require little effort. Afterwards work on high-impact use cases that require more effort. Postpone or not work on low-impacting tasks (there are probably a lot more use cases with a high impact).
At this point, each user has one use case to work on that helps them in their daily work.
6.Document and execute
After finding and prioritize the use cases to work on, you should document your findings. Create a table where all use cases are listed, what AI capabilities they utilize, how much effort and impact they have, who is owning them and what the next steps are.
7. Build use cases in groups and individually
Now it is time to build the use cases. You can build a few use cases in a group together, so people get a feeling for how it works. Afterward, everybody has time to experiment and build their use cases individually or in smaller groups. A good time frame for enough experimentation but not losing the momentum is 1-2 weeks.
In the meantime, you can follow up with the users individually to see if they are stuck or need help.
In the next group session, you can share how the different use cases were built, what users learned, what worked and what did not work. Keep in mind that not everything works immediately and some use cases are not ideal to be performed by an AI. This is normal and part of the learning journey.