Choosing the Right Use Case: Starting Small for Big Impact
Introduction
Introducing private AI to your organization is an exciting step, but it can also feel overwhelming. With so many possibilities, how do you decide where to start? The key is to begin with a focused, manageable use case that delivers real value. Choosing the right use case helps build momentum, gain buy-in, and set the stage for broader AI adoption.
Why Starting Small is Important
AI implementation isn’t just a technical challenge—it’s a cultural one. A small, successful project demonstrates value to stakeholders, builds confidence in the technology, and minimizes risk. Think of it like planting a seed: nurturing a small, well-chosen use case gives it the chance to grow into something much bigger.
Criteria for Choosing a Use Case
When evaluating potential use cases, consider the following criteria:
Impact: Look for processes where AI can deliver measurable results, such as reducing time, cutting costs, or improving accuracy.
Feasibility: Start with processes that are well-documented and have clean, accessible data.
Simplicity: Avoid overly complex tasks for your first project. Choose something manageable to build confidence.
Relevance: Pick a use case that aligns with your organization’s strategic goals to ensure buy-in from leadership.
Examples of Good Starting Use Cases
Customer Support Automation: Implementing an AI-powered chatbot to handle FAQs can free up staff to focus on more complex issues. Personally, I have found this to be an excellent place to start. Turnover for employees in this area is often high, interactions require a command of a lot of written information, and answers need to be quick for customer interactions.
Compliance Reporting: Use AI to automate the preparation of compliance documents, reducing errors and saving time. Developing a template and system prompts are key for this use case.
Data Classification: Train AI to categorize and tag documents for easier retrieval, streamlining knowledge management. I suggest creating a metadata template that is well thought through for the AI to mimic.
Steps to Identify the Right Use Case
Engage Stakeholders: Talk to teams across your organization to identify pain points and opportunities where AI can help. Establishing a bi-weekly project call works well.
Analyze Existing Processes: Look for inefficiencies in workflows that AI could improve. Focus on repetitive, time-consuming tasks.
Define Success Metrics: Decide upfront how you’ll measure success—whether it’s time saved, error reduction, or increased satisfaction. One learning I have here is that often, your success metric is not 100% accuracy. For example, in the Customer Support use case, call center reps also make mistakes. This is your true benchmark.
Start Small, Think Big: Choose a use case that’s small enough to handle easily but scalable for future projects.
Challenges to Watch Out For
Even with a small use case, challenges can arise. For example, stakeholders might resist change, or the data needed for the AI model might not be as clean as expected. Address these challenges early by fostering collaboration and maintaining clear communication. One of the things I am noticing in practical application of projects is that data source quality often very poor. This data clean-up effort is time consuming and makes it difficult to demonstrate quick wins and forward progress. I don’t have a good answer for how to address this yet and would be interested in your comments.
Conclusion
Choosing the right use case is the foundation of a successful AI journey. By starting small with a high-impact, manageable project, you create a blueprint for future success. Remember, the goal is not just to implement AI but to demonstrate its value and build trust. The right first step can set the tone for an entire transfomation.