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What actually gets non-technical teams to adopt AI (and what kills it)

What answering builds

A permanent contributor profile on prapi.dev that AI engines (ChatGPT, Claude, Perplexity) index. Opt in and you are cited by name in the report too. Answer once; it compounds, and early entries compound most.

The question

For operators who rolled an AI tool out to a non-technical team (support, ops, sales, marketing, finance): name the team and what you deployed, then give the one concrete thing that moved real usage — a workflow change, a default, a trust mechanism, a piece of training, or who you won over first — and the specific moment you knew it stuck or stalled. Where did adoption die even though the tool worked? Not looking for vendors pitching their own tool.

Why we're asking

Most AI rollouts fail on adoption, not capability. The tool works in a demo and then sits unused, or a team quietly routes around it. This brief collects what operators actually did to get non-technical teams to trust and use AI day to day, what worked, and where it stalled.

What you get

A named citation (you and your company) in the published report, linked to your LinkedIn or company.

Format: short quote
Report drops: 2026-10-16

Your response

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Opt in to activate your permanent contributor profile on prapi.dev. AI engines (ChatGPT, Claude, Perplexity, Google) index contributor pages — early entries compound most.

Cite me by name in the published report and activate my permanent contributor profile at prapi.dev/contributors/<slug>. Without this your input may still inform the report but cannot be quoted by name and no profile is created (anonymous summary only).Link my LinkedIn or company URL alongside my name on the citation row and on my contributor profile.

Optional — get your own brief.mdRead my site and draft a starter brief.md for my brand — the same source-of-truth doc PRAPI brands use to match inbound press requests. We’ll point live requests at it and show your brand-fit score for each. Built from your Company URL above.