The gap between company strategy and AI strategy
The challenge is a gap that has opened between the company's strategy and the opportunities, and threats, that AI has created.
Based on conversations with friends over the summer, with clients, and on the LinkedIn feed, many seem to have realized the opportunities that come with AI.
The challenge is that a gap has opened between the company’s strategy and the opportunities, and threats, that AI has created. The strategy was written for another world - possibly with the addition of “AI Transformation” in a box.
Most AI initiatives I see are born the same way. A developer finds an area exciting. Or someone on the development team talks to a person in the business who has a problem, and they start building.
It sounds healthy. Close to the business, quick to action.
But that is opportunism, not strategy.
And the difference shows after a year. You have ten pilots, three demos that impressed the executive team and zero movement in the business. No one can answer the question:
Why exactly these projects?
It is also a large part of the explanation for why a majority of AI initiatives never make it all the way to impact.
A project without grounding in the business has no one fighting for it when things get hard - and things always get hard somewhere between demo and operations. Without that grounding, you simply do not get through.
Where the gap appears
The problem is rarely the technology.
The problem is that a gap has opened between the company’s strategy and the opportunities, and threats, that AI has created. The strategy was written for another world and, from that perspective, needs to be reworked entirely from this new view of the world.
As long as that gap stands open, it gets filled from below:
- by enthusiasm
- by whatever happens to be easy to build
- by whoever shouts the loudest
Individually, each project can make sense. Together they become a portfolio no one chose.
What an AI strategy actually is
Not a 40-page document.
Not a policy on which tools are allowed.
An AI strategy is the answer to a single question:
Where will we win with AI - and what does it take to win there?
For most companies this lands in two tracks that look different and should be governed differently.
1. Committed efforts where the potential is
A small number of areas where AI can move something that shows in the P&L - margin, lead time or customer experience.
Pilots are not enough here. This requires:
- real investment
- ownership in the executive team
- staying power through the classic questions: data, integrations and changed ways of working
Back to basics.
2. Exploration where the picture is unclear
The technology moves too fast for every decision to be made today. Some areas deserve small, cheap experiments with clear learning goals.
The difference from opportunism?
That the areas were chosen deliberately - and that you decided in advance what determines whether you scale or shut down.
Opportunistic projects can capture real opportunities, sometimes very good ones. But they should be the way into the strategy, not the replacement for it.
And it has to stay alive
This is where many go wrong in the other direction: they write an AI strategy.
When was your AI strategy made? Has anything happened in AI since then?
What was technically impossible this spring is standard today.
An AI strategy therefore needs a rhythm, not a deadline. It needs to be revisited regularly, with a mandate to move money between the tracks when reality has changed.
Fixed direction. Moving plan.
The test
Want to know where your organization stands?
Ask three people in the executive team one question:
In which two or three areas should AI give us the most over the coming 12-18 months?
If you get the same answer, you have a strategy.
If you get three different answers, you have a project list - and a clear gap.