stop treating AI like a Project
A team at a large global food company was asked to deliver over 100 AI use cases by November. The request came from senior sales leadership. It was July. Nobody on the team could explain why November. Nobody could name the specific leader who set the deadline or articulate what business outcome it was tied to. But the deadline was on paper, so everyone started running toward it. Within weeks, vendors in the room were already proposing shortcuts. Structure the data this way, use this workaround, and we can make the timeline.
The team was taking the bait. An artificial deadline was actively producing bad architecture.
A Director of Strategic Growth Management at a major global food company was brought in to help. Her diagnosis was immediate: this is a Project mindset, and it is the single most destructive pattern in enterprise AI. Fund a project, rush to a deadline, take the shortcuts that make the timeline, ship something, and move on. The technical debt accumulates. The connections that were never built because there wasn’t time become the reason the next initiative has to start over from scratch.
A Product mindset asks a different set of questions before a single line of code is written. What is the ideal end state? What does this need to connect to? How do we build the first iteration in a way that doesn’t foreclose the second? Where are we intentionally accruing tech debt, and when do we plan to retire it? Those questions take time. They feel like they slow things down. They are the only thing that makes the work last.
A Director of Supply Chain Strategy at a large CPG organization draws a historical parallel: the dot-com boom was full of organizations that built beautiful front ends with nothing behind them. They rushed to ship, skipped the infrastructure, and collapsed when the shortcuts caught up. He sees the same pattern in enterprise AI today. Fast starts, fragile foundations, and a reckoning that arrives later than the deadline everyone was racing toward. A Data Science Manager at a major global CPG company puts the endpoint plainly: tools built this way go to the closet. The project gets closed out. The problem it was meant to solve is still there, now with less budget and less organizational patience to try again.
She frames it simply: nobody is funding a project to build throwaway work. But that is exactly what the Project mindset produces. The timeline becomes the objective. Shipping becomes the definition of done. And the organization wonders why it keeps starting over.
AI is not a project with a delivery date. It is a capability that has to be built, maintained, and evolved. Treat it like the former and you will keep paying to rebuild it.