Latency Is a Feature, Not Just a Metric

Most AI pilots look great in a demo but quietly collapse before reaching production. New research shows that nearly half of AI initiatives are abandoned, and even fewer ever deliver real ROI. The problem isn’t the technology; it’s weak foundations, unclear ROI, and poor adoption strategies. This article explores why so many AI projects fail, and what business leaders can do differently to turn demos into production-ready solutions that deliver real value.

Many companies think the fastest path to AI success is hiring more engineers. But talent alone won’t get projects into production or deliver ROI. The real missing piece is strategy – knowing which problems to solve, how to measure value, and how to guide teams with the right frameworks. In this article, we’ll look at why so many AI pilots stall, why strategy beats talent alone, and how bringing in the right guidance (even part-time) can save money and reduce risk.

A demo only needs to work once to impress. Production AI needs to work every time for every user. That leap from “sometimes” to “always” is where reliability costs explode—and if you don’t design for it early, you’ll end up rebuilding under pressure.

Your demo lives on the happy path. Production AI lives in the typos, slang, and weird edge cases. Handle them well, and you’ll keep user trust.

Nike’s “Just Do It” inspires action. But in AI projects, action without strategy often fails. The ReAct framework – Reason + Act – offers a better mindset: think first, act deliberately, then adjust. In this article, we explore why leaders should replace “Just Do It” with “Think Then Do It” when adopting AI.