THE AI ADOPTION GAP | ORGANIZATIONAL CHANGE MANAGEMENT

Your AI pilot proved the model works. That was the easy part.

Most AI programs are judged on whether the pilot delivered. The ones that create value are judged on whether anyone is still using the system 90 days after go-live. This white paper covers the four gaps between those two points, and the sequence for closing them.

The pilot isn’t the proof

A working proof of concept feels like the hard part is over. It isn’t. MIT followed more than 300 enterprise AI initiatives and found that 20% reach pilot, but only 5% reach production with measurable impact. Gartner now estimates half of generative AI projects are abandoned after proof of concept.

A pilot runs on a clean dataset with a technical champion in the room. Production means hundreds of employees who were never trained on the tool, pulling from data nobody has validated in years, with no one accountable for whether it gets used. This paper, from Infoverity’s AI Literacy & Value Realization Practice, sets out what to put in place before that point, not after.

What’s inside

The paper works through what separates a pilot from a production system that pays back:

• Why AI initiatives stall, and why the pattern repeats across industries
• The four-gap diagnostic: literacy, readiness, adoption, value realization
• Why organizational change management is the variable most AI programs leave out
• A five-stage methodology from Discover to Realize, and what each stage owes the next
• What “AI-ready data” actually requires, beyond a cleanup before launch
• Five KPIs that measure outcome, not activity, including sustained adoption at 90 days, time-to-value, champion density, and a data trust score that flags adoption decay before usage numbers do

That last section is the one most teams are missing. Licenses provisioned and first-month logins are the metrics available by default, and none of them tell a CFO whether the investment worked.

Who this is for

If you’re a CDO, CIO, or Head of Data with AI spend approved and a pilot that hasn’t scaled yet, this gives your team a sequenced plan to work from, not another framework to translate into action yourself.

About the author

Shannon Long, Managing Consultant and Change Management Lead at Infoverity, wrote this paper from direct experience guiding enterprise teams through AI and data programs, where adoption, not the technology, is usually what decides whether the investment pays back.

Get the guide  Four gaps. Five stages. One PDF your team can act on.