Article
The gap between a successful AI project and a failed one is almost never the technology
What really separates an AI project that reaches production from one that stays a slide deck.
When an enterprise AI initiative stalls, the post-mortem usually points at the model: the wrong provider, not enough data, a prompt that needed more work. In our experience that diagnosis is almost always wrong.
The projects that make it into production and the ones that quietly disappear tend to use the same models. What differs is everything around the model.
Failure rarely looks like a technical failure
A pilot that works in a demo and dies on the way to production usually fails for organisational reasons: no clear owner, no definition of what "good enough" means, no process that actually changes when the AI is switched on.
The technology was never the bottleneck. The decision-making around it was.
What the successful projects have in common
- A named business owner who is accountable for the outcome, not for the pilot.
- One measurable metric agreed before the build starts — cycle time, approval rate, cost per case.
- A redesigned process, not an AI feature bolted onto the old one.
- Governance from day one: who can use it, on which data, with what audit trail.
- A production path defined at kickoff, including integration, monitoring and support.
The uncomfortable implication
If success depends mostly on ownership, process and governance, then buying better technology will not save a badly framed project. And a well-framed project can succeed with fairly ordinary technology.
That is good news: the hard part is inside your control.
Before the next AI initiative, ask who owns the outcome, which number is expected to move, and what changes in the process on the day it goes live. If those three answers are missing, the model choice is irrelevant.
This article is the English version of a post originally published in Hebrew on our LinkedIn page.