AI Ambition to Reality Gaps

Why do big AI ambitions fail? In my experience, here are some common culprits.

The ambition is big, but not specific enough. Problem identification is critical to solutioning. Always has been. AI is no different. Although now we can have AI help identify root causes and frame experiments.

Existing structures, limited thinking, and fear undermine progress. Classic change management. Review the ADKAR model and involve others in learning and trying. Then after experiments run, use that confidence and experience to re-envision the workflows from the customer back through your value chain. Your assumptions and constraints may be holding back breakthroughs. Like Andy Grove did to focus Intel decades ago ask “What would the next guy in my seat do?” And do it first.

Security. It’s critical especially in an autonomous agentic landscape to which we are heading. Know your stance. Know the tools. Build in monitoring. Demand answers from your vendors and partners. The tools people are authorized to use don’t quite have the right capabilities. And the ones they aren’t allowed to use, they may be using creating exposure.

Current processes are not well understood and data is everywhere and not “clean” (in any definition). There is a real risk we automate waste and make things worse. Doing the right things well is always more important than just “doing things” well. AI is expensive. It is really expensive if you are burning tokens to erode free cash flow.

Some of these are AI-driven challenges, but most are grounded in the human experience. Mastering them has helped with other transitions and will be key to what’s next.