as a tool
don't measure a strategy.
Why standard AI KPIs are insufficient for executives.
Standard AI metrics : adoption rate, productivity gain, processing time reduction, cost savings : answer an operational question: is AI being used and is it efficient? That's a legitimate question. But it is not a strategic question.
A strategic question would be: does our AI deployment build an advantage competitors cannot easily replicate? Are we creating dependencies that will limit our options in 3 years? Is our competitive position improving, or is our productivity improving while competitors do the same?
What strategic AI measurement must cover.
Does our AI deployment create an advantage competitors cannot easily replicate? This requires assessing whether AI exploits proprietary data or capabilities, or whether it uses generic tools accessible to everyone at the same price.
Are AI-assisted decisions better : not faster, better? This dimension is rarely measured because it is difficult to objectify, but it is precisely where AI can create the most strategic value.
What is the dependency surface created by our AI deployment? What is the estimated exit cost in 3 years? This metric works against standard metrics : it measures not what you gain but what you risk losing.
Does our current AI deployment keep our strategic options open for the next 5 years? Optionality is a strategic value that productivity metrics don't capture.
The question you don't ask enough.
If your AI budget disappeared tomorrow, what portion of your competitive advantage would disappear with it? If the answer is "little" or "none", your AI investments have not built an advantage : they have bought productivity, which your competitors are doing too.
If the answer is "a significant part of our ability to process X, decide Y, or anticipate Z" : you have built something defensible. It is not an easy metric to quantify. But it is the right question.