The Internal AI Adoption program is for companies that have bought the licenses and aren't seeing the usage. It's the most common gap we see at the 50–1000 person scale, and it's not a tooling problem. It's a workflow integration problem.
Three weeks. Pilot cohort, role-specific playbooks, champion network, measurement. You finish with sustained adoption, not just trained employees.
What this is not
It's not a training program. We do training, but training alone has been shown over and over to fail at producing real adoption.
It's not a tool selection engagement. If you haven't picked tools yet, look at Best AI Setup for Your Company first.
It's not a transformation program. We don't redesign jobs. We integrate AI tools into the jobs people already do.
Why this works
The two non-obvious mechanisms:
Pilot cohorts beat all-hands rollouts. A small group of willing early adopters produces internal proof faster than an org-wide announcement. The adoption story spreads through people who've actually used the tools and seen results.
Workflow integration beats tool training. People don't adopt tools in the abstract. They adopt tools because their actual jobs got easier. The playbooks we build attach AI use cases to specific recurring tasks, not abstract capabilities.
Engagement constraint
One of these per quarter, max. The program requires real engagement during the three weeks and we don't run multiple in parallel.
Who this is for
- Companies that bought AI tool licenses and aren't seeing usage
- Leadership teams who want AI productivity uplift but haven't operationalized it
- Heads of People / Operations leaders running internal enablement
- Companies in 50–1000 person range — too big for ad-hoc, too small for full transformation programs
What's included
- Adoption baseline: what tools are licensed, what's being used, by whom
- Pilot cohort selection and onboarding
- Workflow integration: bringing AI tools into the actual jobs people do
- Custom training tailored to roles, not just tools
- Internal champion network setup
- Adoption metrics dashboard
- Executive readout at end of engagement
- 60-day post-engagement check-in
Process
- 01 Baseline (Days 1–3)
Audit current AI tool licenses and actual usage. Interview a sample of employees across roles to understand barriers. Identify the pilot cohort (typically 8–15 people across 2–3 functions).
- 02 Pilot setup (Days 4–7)
Onboard the pilot cohort with role-specific use cases. Each person leaves the onboarding with 2–3 specific workflows they're going to try AI on this week.
- 03 Active pilot (Days 8–14)
Daily-or-weekly check-ins with the pilot cohort. Collect what's working, what's broken, what's missing. Adjust playbooks. Document successes.
- 04 Scale-out (Days 15–18)
Identify internal champions from the pilot. Run train-the-trainer sessions. Roll out playbooks and workflows to the broader team in waves.
- 05 Measure & report (Days 19–21)
Adoption metrics, qualitative wins, leadership readout with recommendations for sustained enablement. Then a 60-day check-in to confirm adoption holds.
Deliverables
- Adoption baseline assessment
- Role-specific playbooks for AI tool use
- Pilot cohort outcomes report
- Internal champion network with named members
- Adoption metrics dashboard
- Executive readout deck and meeting
- 60-day check-in
FAQ
- How is this different from just running training sessions?
- Training sessions teach tools. This program drives usage. The difference is the pilot cohort, the workflow integration, the champion network, and the measurement. Training without a system around it produces almost no sustained adoption — we've seen the data.
- What if leadership wants AI adoption but the team is skeptical?
- This is the single most common case. The pilot cohort approach is designed for it: rather than mandating, we work with willing volunteers, build internal proof, then let the champions pull the rest of the team along. Forcing adoption from above does not work.
- What does 'success' look like?
- We define success metrics with you in week one. Common ones: percentage of pilot cohort with sustained weekly use, hours saved per role per week, qualitative satisfaction scores. We track them. The exec readout at the end shows real numbers, not vibes.
- Can you handle regulated industries?
- Yes. We've run this for legal, financial services, and healthcare clients. The playbooks adapt to regulatory constraints — typically biased toward self-hosted models and tighter data-handling rules. The shape of the program is the same.