Why AI pilots stall before the model is even the problem (BARC Report)

Most AI pilots never get cancelled. They demo well in the spring, pick up a second round of scope over the summer, and by autumn the two people who built the thing have been pulled onto something with a firmer deadline, without anyone writing the word "failed" anywhere. The question of why AI pilots fail…

Prompt engineering for data analysts has one failure mode that general AI writing advice never touches: the model returns SQL that parses cleanly, runs without error, and produces a number that's wrong in a way no one catches until somebody questions the dashboard three weeks later. The request was usually underspecified rather than the model…

Give a competent engineering team a week and they'll have something working. A GPT endpoint wired up, a charting library on the front end, a handful of SQL queries hitting the production database, and there it is, the new "AI-powered insights" feature ready for the demo deck.

In most mid-market companies, the BI half of the picture is the part that's settled: the dashboards refresh overnight, the Monday reports land in inboxes, and there's a tidy stack of SQL behind every figure on screen. What's new is the pressure coming from above to bolt AI onto all of it, and somewhere between…

You're managing 400 SKUs. You treat every product the same, same reorder triggers, same attention, same safety stock logic. Then one of your top-10 revenue products runs out on a Tuesday, and the slow-moving items nobody orders pile up in the warehouse. That's the problem ABC analysis solves.

Somewhere in the growth of most B2B SaaS products, customers start asking for the same thing: a way to see and report on their own data without leaving the app. Embedded analytics for SaaS exists to answer exactly that, putting a branded dashboard inside your product instead of pointing each client toward a separate reporting…

Intro Your team spent weeks building a dashboard. You shared the link. Nobody opened it. Dashboard abandonment is one of the most expensive and least-discussed problems in business intelligence — not because the data is wrong, but because adoption was never treated as part of the build. Every dashboard your team creates but no one…

Every hour your team spends manually checking dashboards is an hour not spent fixing what those dashboards reveal. By the time someone spots a problem (a campaign bleeding budget over the weekend, a location with a sudden cost spike, a trial account going quiet), the window for action has already narrowed. Automated dashboard alerts change…

If your marketing team, IT department, or operations lead has started building a BI or reporting solution using Claude or another AI tool, the first few steps likely exceeded expectations. A few prompts, a working data connection, a chart that renders with real numbers. It's genuinely impressive — and it's easy to see why the expensive platform…

Restaurant analytics means connecting the operational data your restaurant already produces, from POS and inventory to labour, reservations, online ordering, and marketing, and turning it into decisions your team can act on. Two parts of that definition matter more than the words themselves. It matters more in 2026 than it ever has. The National Restaurant…