AI Strategy for the Next Decade
Why most companies are thinking about AI wrong
Most companies approach AI strategy as a technology decision. It is not. It is a business decision with technology implications, and getting this distinction wrong is why most AI initiatives fail to deliver meaningful results.
After a decade of working with startups and established companies on their AI strategies, I have developed a framework that cuts through the noise. It starts not with what AI can do, but with what your business needs to be true.
The Problem with Current Thinking
The typical approach to AI strategy starts with the technology and works backward to the business case. A team discovers a new model or capability, gets excited, and then hunts for a problem it might solve. This is exactly wrong.
The best AI strategies I have seen start with a ruthless assessment of the business constraint, not the technology frontier.
When you start with the technology, you end up with impressive demos that never make it to production. When you start with the constraint, you build things that matter.
A Better Framework
Start with the binding constraint on your business today. Not the aspirational one — the real one. The thing that, if you could relax it by an order of magnitude, would change everything.
The constraint test: Ask yourself — if I could do this ten times faster, ten times cheaper, or ten times more accurately, which would change my business the most?
Once you have your constraint, the AI strategy becomes straightforward. You are looking for the intersection of three things: where the constraint binds hardest, where data is available or acquirable, and where AI capabilities are mature enough to be reliable.
Implementation Principles
With the constraint identified, implementation follows a set of principles that are counterintuitive to most technology teams.
First, start with the simplest possible approach. If a rules-based system gets you 80% of the way there, build that first. You will learn more from a working system than from a perfect architecture diagram.
Second, measure what matters to the business, not what matters to the model. Accuracy on a test set is irrelevant if the system does not move the metric your CEO cares about.
Third, plan for the human in the loop. The most successful AI systems I have seen are not fully autonomous — they are tools that make skilled people dramatically more effective.