AI Adoption
Identifying Use Cases
This guide helps you understand what great use cases are and how to identify them for your organization.
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. We recommend starting with horizontal use cases that are relevant for many people across all teams, ideally to everyone. This approach has several advantages:- Everyone understands the problem and can relate to the situation. This increases willingness to learn how to build use cases and use AI in daily work.
- Horizontal use cases require less customization. When trying to cover deep vertical use cases, many integrations and custom steps are often needed, which increases effort.
- Deeper use cases are more difficult to build and maintain. In the beginning, collective AI knowledge isn’t as deep yet, so it makes sense to focus on educating users with simpler cases first before diving into more complex use cases.
How to find use cases
1. Experiment and understand AI capabilities
AI excels at performing specific tasks across different areas. Initially, let users experiment and learn about AI’s different capabilities. Pair this experimentation with showing example use cases and helping users organically develop their own use cases. Here are general AI 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 time-consuming.3. Collect activities
Collect activities from the entire group and cluster similar activities. If several people have the same or similar use case, it might make sense to exchange experiences or work together on them. You can use a whiteboard or digital whiteboard (e.g., Miro, Mural, Figjam) for this activity.4. Connect use cases with AI capabilities
Understand which use cases work with which AI capability from above. For example, a translation use case would require writing text, while a document summarizer needs text writing and text summarization capabilities.5. Prioritize what to work on first
Every organization has 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 build them step by step. In our experience, it makes sense to start with use cases that require little effort to build and have high impact for many people in the organization. You can use a 2x2 matrix to prioritize use cases. The different axes are feasibility and impact. Feasibility can be evaluated by:- Effort - Lower effort makes it more feasible
- Data and attachments readiness - If data needs to be cleaned up or collected first, additional time is needed and feasibility is reduced
- APIs, integrations, or code needed - Many use cases can be covered by uploading a file from your computer or using Langdock integrations. While Langdock offers APIs, actions, and customization options, this increases effort
- Time saved - How many hours can be saved per week/month for how many employees?
- Quality gains - How much better is the output quality? Are errors reduced?
- Customer satisfaction - Does this use case improve service quality or speed?
- Financial impact - Is there potential to save costs or increase revenue?
6. Document and execute
After finding and prioritizing use cases to work on, document your findings. Create a table listing all use cases, what AI capabilities they utilize, how much effort and impact they have, who owns them, and what the next steps are.7. Build use cases in groups and individually
Now it’s 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, everyone has time to experiment and build their use cases individually or in smaller groups. A good timeframe for enough experimentation without losing momentum is 1-2 weeks. In the meantime, you can follow up with users individually to see if they’re stuck or need help. In the next group session, you can share how different use cases were built, what users learned, what worked, and what didn’t work. Keep in mind that not everything works immediately, and some use cases aren’t ideal for AI to perform. This is normal and part of the learning journey.The Langdock team is also available to support you here. Just reach out to your point of contact to discuss how we can help.