“What’s our best customer?”
“Which warehouse performs best on Wednesdays?”
“Are sales reps actually driving revenue after visits?”
These aren’t technical queries; they’re critical business questions. And they’re being asked every day by CEOs and leaders across sectors, from manufacturing and supply chain to logistics and distribution.
While you might think that most in-house copilot environments can deliver clear answers — they can’t, unfortunately. And that’s not because the technology isn’t capable. You see, quite often, the semantic model, logic definitions, and AI prompting are not built with decision-making or natural language in mind.
60 % of self-service BI initiatives fail to meet usability or ROI expectations.
— Gartner
That’s exactly the challenge one of our clients faced at the executive level. Below, we’ll break down the situation and explore how companies can overcome similar barriers to insight.
The Problem: When IT Can’t Keep Up with Executive Vision
So, our client’s internal IT team had set up a robust business intelligence solution — Power BI Copilot. However, the CEO’s expectations far outpaced what the tool could deliver. The questions being asked required:
- Cross-report context like “best month for a product”
- Correlations and behavior patterns such as “Do Wednesday shipments follow bulk entry Tuesdays?”
- Clean, business-friendly definitions: top customer, gross margin, etc.
- Natural language interpretation that works for sales reps, not just analysts
Copilot fell short for a few key reasons. First of all, the underlying semantic model was cluttered and inconsistent, with logic that was hard to follow. Field names lacked clarity, and important synonyms were either missing or poorly defined. On top of that, Copilot wasn’t properly configured to leverage generative AI or interpret queries in a broader business context.
The result? Frustrated users, a return to manual Excel workarounds, and ultimately, a gradual erosion of trust in the tool. As you can imagine, the sales team became disconnected while the CEO was left questioning the value of the entire investment. If this were the final chapter of the story, it might be titled The Broken Promise of Democratized Insights. But fortunately, it wasn’t the end. Velvetech stepped in with a solution.
The Solution: Realigned Data, Rebuilt Trust, Reimagined Copilot
We knew that simply tweaking a dashboard wouldn’t work. That’s why our approach was about rethinking entirely how insights are delivered across the organization.
Our team started by redesigning the semantic model into a business-aligned star schema so that data behaves the way real questions are asked. To ensure Power BI Copilot understands context correctly, it was essential to fine-tune the prompts and behavior: the tool now knows when to respect filters and when to override them. Next, core metrics were redefined, too, with the language mirroring how leadership and frontline teams think — not how databases are structured.
On the backend part, we configured Azure OpenAI and Microsoft Fabric to support advanced analysis driven by generative AI. And perhaps most critically, we focused on usability from the start so that sales teams ditch Excel, executives get real answers in real time, and trust in the platform is fully restored.
From Nationwide Distribution to Industry-Wide Value
The questions asked by our client are not unique, of course. They’re quite universal, as every CEO typically wants to know:
- Who’s buying?
- What’s selling?
- What’s changing — and why?
That’s why Velvetech’s copilot implementation isn’t just a service. We build a scalable solution ready to be deployed across the manufacturing, supply chain, distribution, and other sectors.
Ready to stop building dashboards and start delivering decisions? Let our team transform your Power BI from a frustration factory into an insight engine. Contact us for a discovery session and a custom quote.
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