Enterprise AI Solutions

AI Adoption Programs

After a system is deployed and usage is lower than expected — which is the normal outcome without deliberate adoption work, not a sign the system is bad.

Most AI failures after launch are adoption failures. The system works, and people continue doing the task the way they always have, because the new way is unfamiliar and the old way is known to work.

Adoption work covers enablement, internal champions, and the feedback loop that turns a deployed system into a used one — including acting on what users report, which is what makes them report again.

What the work includes

  • Enablement designed around real tasks rather than feature tours
  • Identification and support of internal champions within each team
  • Feedback channels, and a commitment to act on what comes back
  • Usage measurement that distinguishes trying something from relying on it
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Common questions about AI Adoption Programs.

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Because deployment and adoption are different problems. A system that works still has to fit how people actually do their jobs, earn enough trust to be relied on, and be easier than the workaround it replaces. Low usage after launch is the normal outcome without deliberate adoption work.

Enablement for the people expected to use the system, internal champions who can answer questions locally, and a feedback loop that carries real usage problems back to whoever can fix them. The loop matters most, since early friction is what determines whether use persists.

Before launch. The people who will use a system are the best source of information about whether it fits their work, and involving them late converts them from participants into recipients — which is where most resistance originates.

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