Enterprise AI
Why Enterprise AI Keeps Failing — And Why Nobody Talks About It
Every week, another press release. Another organisation announces a sweeping AI transformation. Thousands of licences. A keynote quote from the CEO. A logo on a vendor slide deck.
And then, mostly, silence.
Not failure exactly. Not success either. A kind of quiet, expensive stasis where the tool is technically deployed, the dashboard shows acceptable numbers, and the organisation continues to work almost exactly as it did before — slightly more tired from the effort of appearing to change.
I've spent years working inside large enterprises on technology adoption. I've watched implementations that looked inevitable on paper quietly dissolve into shelfware. I've sat in rooms where the gap between what was promised and what was delivered was visible to everyone and mentioned by nobody.
I'm not writing this series to attack AI, or the vendors who sell it, or the leaders who buy it. The technology is genuinely capable. The potential is real. I'm writing it because I think the conversation about why value isn't being realised is almost entirely absent — drowned out by the announcements, the case studies, and the conference keynotes that celebrate the minority of implementations that worked while the majority disappear without record.
The graveyard of failed or underwhelming enterprise AI implementations is enormous. It is also almost completely undocumented. Every new buyer walks in with the same optimism, makes variations of the same decisions, and encounters variations of the same problems — without the benefit of anyone having mapped the terrain honestly.
This series is an attempt to map it.
I'm going to use an unconventional lens: data fallacies, cognitive biases, and historical analogies.
[Draft ends here — continue or rewrite before publishing.]