Testing Copilot Studio: AI Is Only as Smart as Your Data
Testing Copilot Studio: AI Is Only as Smart as Your Data
The promise of AI-driven copilots is compelling: streamlined information retrieval, automated workflows, and enhanced efficiency. But as my recent test of Microsoft Copilot Studio demonstrated, the effectiveness of AI is only as good as the data it relies on.
The Challenge: Finding the Right Answers
Our goal was to evaluate how well the Copilot could retrieve precise answers from the database. The results were mixed. The AI struggled to provide exact matches, primarily due to limitations in data quality and structure.
Here’s what we discovered:
- The database consisted of .docx documents structured around email-style Q&A exchanges.
- The search function relied on exact matching of specific content rather than semantic variations of questions.
- There was no way to test alternative phrasing or conversational queries.
- Overlapping and inconsistent answers in the dataset made it difficult for the AI to return a definitive response.
- The business requirements did not allow for AI-generated responses. Only pre-existing text could be retrieved.
- Data integrity was maintained, as business stakeholders controlled ownership, but this also meant we couldn’t manipulate data to test ideal scenarios.
Key Takeaways: Fixing the Foundation
To improve future AI deployments, we need to rethink our approach:
- More realistic queries: Business users must provide better-structured questions to test the AI’s interpretation skills.
- Improved data quality: Using platforms like SharePoint to enhance metadata tagging can refine search accuracy.
- Reducing ambiguity: Clearer, more distinct responses will help the AI deliver precise answers rather than conflicting outputs. Go in interaction with the data owners to modify the data where possible, under their control.
Real-World Implications
This isn’t just a technical issue—it’s an operational one. In industries where compliance and accuracy are non-negotiable. AI can’t provide trustworthy automation if it’s built on inconsistent, unstructured data. Our test reinforced a fundamental truth: even the most advanced AI can’t compensate for poor data quality. If the input is flawed, the output will be unreliable.
Looking Forward
While our test exposed limitations, it also revealed opportunities. With better data structuring, metadata tagging, and an improved approach to question formulation, AI copilots can be a powerful tool. But first, we must solve the age-old problem of garbage in, garbage out.
My focus is on structuring, automating and managing business processes using Agile and DevOps best practices. This creates working environments where business continuity, transparency and human capital come first. Reach out to me on LinkedIn or check out my github or blog for more tips and tricks.
The ideas and underlying essence are original and generated by a human author. The organization, grammar, and presentation may have been enhanced by the use of AI.