Bissell's data team cut through months of AI theorizing by locking themselves in a room for 48 hours and building five functional AI workflows. Working with Domo's platform, cross-functional teams from customer service, product design, and sales tackled real business problems that were eating hundreds of manual hours annually. The results were immediate and measurable: their post-launch analytics AI agent now spots product trends in 5-10 minutes instead of 8-10 hours, identifying issues like a 20% spike in users needing product guidance and 15% more troubleshooting requests.
This approach directly counters the endless AI pilot purgatory plaguing most enterprises. Brandon Walsh, Bissell's Director of Data & Analytics, noticed his team's AI skepticism stemmed from playing with free tools in their spare time rather than building production solutions. By securing executive buy-in for dedicated focus time and picking actual pain points—not theoretical use cases—they delivered working automation instead of PowerPoint promises.
The story matters because it's replicable. Bissell didn't need custom models or massive infrastructure—they used an existing analytics platform with AI capabilities to solve specific workflow bottlenecks. While we only have Bissell's side of the story (presented at Domo's own conference, naturally), the approach of time-boxing real problems with dedicated resources is sound engineering practice.
For teams drowning in AI strategy discussions, Bissell's playbook is straightforward: identify manual work that's actually painful, lock in two days of focus time, and build something that works. The 48-hour constraint forces you to solve real problems instead of engineering perfect solutions that never ship.