Explore real world tradeoffs of AI driven reporting and insights from evaluation Microsoft products.
AI is showing up in reporting conversations everywhere right now, and for good reason. The promise is compelling: ask a question in plain English and get an answer instantly. But as organizations move from curiosity to real adoption, the conversation quickly shifts from “Can it do it?” to more practical questions like “Can we trust it? Can we run it day-to-day? And what does it really cost?”
Over the past several months, our team has been evaluating different ways to apply AI against a structured Azure SQL data source, with a focus on real reporting outcomes and what it actually takes to build and support them. We didn’t set out to pick a single “best” solution. Instead, we evaluated different approaches to understand where each one fits (and doesn’t fit) across a range of client scenarios.
Our discovery focused on three approaches to AI-driven reporting:
- Microsoft 365 Copilot – A low-friction option for finding and retrieving information when data is already well indexed and organized for discovery.
- Copilot Studio – A conversational reporting approach that allows business users to ask questions directly against reporting-ready datasets.
- Microsoft Foundry – A code-first approach that supports fully customized reporting experiences when deeper control, tailoring, and extensibility are required.
We found that all three approaches can work, but none is a universal answer. What stood out most is that the right choice is rarely about AI capability itself. In practice, business value is far more often determined by familiar software decision factors and tradeoffs, including:
- Ongoing cost and predictability. Some options are primarily license-based, while others scale with how often they are used and how complex the interactions become.
- Data quality and readiness. AI reporting outcomes are only as reliable as the underlying data quality. Clean, well-designed reporting views and consistent business definitions matter more than most people expect.
- Governance and change management. As schemas, security rules, and business definitions evolve, the AI layer must evolve with them. Governance is not optional; it’s part of keeping results accurate and trusted over time.
- Build-out effort and ownership model. There’s a wide range between light configuration and fully engineered solutions. Where an organization lands depends on how much control, flexibility, and ongoing ownership it’s prepared to take on.
- Trust, auditability, and risk. In reporting scenarios, being able to explain why an answer is correct (and where it came from) can be just as important as the answer itself.
We’ll discuss these findings along with practical decision guidance and examples during our upcoming session at the Technology Vendor Summit 2026 on April 9, 2026 at the Old National Events Plaza. If you’re exploring AI for reporting and want a clearer view of the tradeoffs before investing, we’d love to connect and share what we learned.
