47%
increase in AI tool adoption
3.2x
faster developer onboarding
0
prompt injection incidents
62%
reduction in internal support tickets
The challenge
After purchasing enterprise licenses for Claude Code and GitHub Copilot, the engineering leadership team expected rapid, organic adoption. Instead, they hit the pattern most large organizations encounter.
A handful of early adopters became the de-facto support desk, fielding dozens of internal questions per week. The security team needed an AI usage policy but had no precedent to build from. Usage data was scattered across vendor dashboards with no single view of who was using what, how often, or how effectively. Two teams had already built internal agents with no review process, no guardrails, and no documentation.
The VP of Engineering described the situation: adoption was uneven, measurement was non-existent, and the security team was increasingly uncomfortable with the lack of visibility.
What we delivered
We scoped a 90-day engagement combining platform access, embedded coaching, and policy deliverables.
Platform rollout (2,000 seats)
All five Foundations tracks deployed on day one. Engineers progressed at their own pace through prompt engineering, Claude Code fluency, security best practices, and agent fundamentals. Spaced-repetition review kept retention high.
AI usage dashboards
OpenTelemetry-based telemetry ingest gave engineering leads a single pane of glass: tool adoption rates, session frequency, feature utilization. No prompt or response bodies stored -- privacy by design.
Embedded trainer (10 days)
A senior practitioner embedded with two pilot teams for hands-on pairing, code review in AI-assisted PRs, and live office hours open to the full org. The goal: build internal champions, not external dependency.
AI usage policy
A tailored policy covering data classification, approved tool matrix, prompt hygiene, incident response addendum, and training attestation. Delivered in three weeks, adopted by legal in four.
Results after 90 days
Adoption moved from 18% of engineers actively using AI tools to 65% -- a 47-percentage-point increase measured through the telemetry dashboards. The internal experts who had been buried in support requests saw a 62% drop in ad-hoc questions as the knowledge base and learning tracks absorbed the load.
New-hire onboarding to AI tool proficiency dropped from an average of 6 weeks of informal learning to under 2 weeks on the structured tracks -- a 3.2x improvement. The security team reported zero prompt injection incidents during the engagement, attributing this to the mandatory security track and the clear policy guardrails.
Internal champions trained during the embedded period now run their own monthly office hours, making the program self-sustaining.
“We spent six months hoping adoption would happen organically. Context Courses got us further in 90 days than we got in those six months -- and now we can actually measure it.”