Why 95% of AI Initiatives Fail And Why Yours Doesn’t Have To
You've probably come across the striking headline that "95% of enterprise generative-AI pilots" fail, with failure defined as "no measurable P&L (profit and loss) impact".
Read the full article here if you're curious about the research methodology and exact findings. Here, instead, let us focus on takeaways.
What goes wrong
There are a lot of reasons mentioned in the report. A few standout ones:
Poor integration into actual business workflows
Unclear success metrics
Top-line hype instead of concrete use-cases
Incidentally, we've written about all these before (check out our archive) or, in particular, these:
It's a nice validation of our thinking.
How to get it right
To distill the whole article—with the pitfalls and the things that those who succeed with AI are doing right—into a single sentence, I'd say:
Start one pilot focused on a single, measurable back-office process and define the P&L metric before building.
No sweeping, company-wide digital transformation, no press-release-driven bravado, no chasing after shiny objects. Just one area where your well-paid knowledge workers (engineers, lawyers, copywriters, you name it) waste time on a back-office process that's not part of their value creation chain. Declare what success looks like and then go build and iterate.
Finally, the researchers found that your success rate increases dramatically if you bring in a specialized partner who can help you bridge the tech-business gap, rather than going it alone. If that sounds intriguing, hit reply and let's start a conversation.
