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You bought the licences. Now get the adoption
Uptut runs enterprise AI adoption end to end: role-based enablement, hands-on engineering programmes, coding assistant rollout, prompt playbooks and the workflow redesign that makes any of it stick. Delivered by practitioners who use these tools in production, and measured on sustained usage rather than course completion.
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Why AI licences sit unused
Enablement built around each team's actual workflows, so people leave with their own work already changed.
Delivered by engineers who ship with these tools daily, not trainers reading a vendor deck.
Champions identified and equipped inside each team, because adoption spreads sideways and not from a mandate.
Measured on usage six weeks later, with a baseline captured before anyone is trained.
Your AI adoption roadmap
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Each project personalised for your business
Unique processes: each service customised to serve your projectChoose one-time projects or ongoing managed consulting
- Global support: work with consultants in your timezones
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- Expert consultants with average 10 years of experience across major industries
Best industry practices and proven frameworks for your domain
Next-gen technology including AI/ML and state-of-the-art tooling
Get more than just a generic report
Proactive discovery workshops to surface hidden risks and opportunities
Multi-domain expertise covering cloud, platforms, applications and enterprise systems
Summary reporting for key stakeholders and detailed assessments for your technical team
Frequently Asked Questions
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Getting people to actually use the AI tools you have licensed. That covers a usage baseline, role-based enablement built on each team's real workflows, hands-on sessions in your own environment, prompt playbooks, a champion network, and the workflow redesign that makes new practice stick. It is an adoption programme with training inside it, not a course catalogue.
Vendor training teaches the product and stops at the feature list. It is useful and usually free, so take it. What it does not do is rebuild your code review process around an assistant, decide what your engineers should stop doing, or address the teams quietly refusing to use it. We work on the practice and the workflow, which is where adoption actually fails.
A pilot team, from baseline to measured result, runs six to eight weeks. Rollout across an engineering organisation is typically three to six months in waves, because adoption spreads faster from teams that already succeeded than from a single org-wide launch. A focused assistant rollout for one department can be delivered in four weeks.
No. You get the facilitation playbook, the prompt library, the champion onboarding pack and the measurement setup, all written for your own enablement team to run. Most clients take the later rollout waves in house once the pilot and the champion network are established, and we stay involved only for new tools or new functions.
Common, and usually diagnosable. Broad fluency sessions raise awareness and change nothing, because nobody's Monday was different afterwards. We look at where usage stalled by team and role, keep what worked, and rebuild the middle layer for the roles that matter most. That is normally a smaller job than the original programme.
On sustained usage, not completion. We capture a baseline before enablement starts, then track active usage by team, which workflows actually changed, output quality where it can be assessed, and time on the tasks the tools were meant to help with, measured six to eight weeks after delivery. Where your tooling exposes usage data, we agree how to read it before the programme begins.
Turn AI licences into changed working practice
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