AI implementation without the 60% failure rate
Most AI projects are abandoned. The difference between the ones that stick and the ones that don't is rarely the model — it's change management.
The numbers are brutal. Depending on which study you read, somewhere between half and two-thirds of AI initiatives are abandoned before they return anything. Teams blame the technology. The technology is almost never the reason.
The reason is adoption. A model that nobody trusts, integrated into a process nobody documented, owned by nobody in particular, will be quietly worked around within a quarter. The pilot demos beautifully and then dies in the gap between "it works" and "we work this way now."
We treat that gap as the actual product. Before we deploy anything, we score readiness across five dimensions — awareness, desire, knowledge, ability, and reinforcement. It is the ADKAR model, and it is unglamorous on purpose. A team can be technically ready and organizationally nowhere. Knowing which one you are is the whole game.
The pattern that works looks like this. Start where AI removes manual, repeatable work — not where it adds a flashy new surface. Sequence the rollout so the first win is small, reversible, and obviously useful to the people doing the work, not just the executive who approved it. Instrument adoption, not just deployment: who is using it, for what, and is the manual fallback shrinking. And keep a human in the loop on anything regulated or irreversible until the system has earned trust with a track record you can see.
This is also why we built the readiness assessment on this site. It will not tell you that you are doomed, and it will not tell you that you are five clicks from transformation. It gives you a band and a sequenced first step, in your own numbers. The teams that succeed with AI are not the ones with the best model. They are the ones who treated adoption as engineering.