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.
And important step in an effective AI strategy is identifying where your team spends hours on manual work. These are the tasks AI can help with, freeing your employees to focus on what matters most.
AI strategy isn’t just about choosing a model or writing code. It has to begin before tech is selected, continue after deployment, and extend beyond the AI app itself into the wider technical ecosystem. In this article, we’ll look at how leaders can build strategies that deliver value across the full AI lifecycle.
Before you jump into AI tools, you need to understand what’s holding your business back today. Here’s how identifying bottlenecks is the first (and most important) step in building a successful AI strategy.
A new MIT study shows that 95% of enterprise AI projects fail to deliver ROI. But Morgan Stanley projects AI could generate $920 billion in annual benefits for S&P 500 companies. The difference? Strategy.
In a demo, 10 seconds feels magical. In production, it feels broken. Latency isn’t just performance; it’s user experience. Design for speed early, or watch users disappear.
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.
In production, a prompt isn’t “just a string”. It’s business logic. Track it, test it, and version it like code, or you’ll ship invisible bugs.
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.