AI for Engineering Managers
The course your leadership team needs before they make decisions about AI rollout, measurement, and policy. No coding required.
Who this is for
Target audience
- Engineering managers, directors, VPs, and CTOs
- Heads of L&D and Heads of AI/ML
- Anyone making rollout, policy, or measurement decisions for AI tooling
Prerequisites
- No coding or technical background required
- Helpful but not necessary: experience managing engineering teams
- Some exposure to AI tooling conversations in your organization
What you'll learn
10 lessons, each built around the same structure: show, tell, do, break it, check. No lesson has more than 15 minutes of passive content before a hands-on moment.
- 1
Literacy for leaders
What these tools actually do, the three mental models, where the real risks sit, honest productivity numbers vs. vendor claims.
- 2
Rollout strategy
The pilot/expand/normalize arc, picking pilot teams, executive sponsorship, communication plans, and common failure modes with real examples.
- 3
Measuring adoption honestly
Vanity metrics you'll be tempted by, metrics that correlate with real productivity, self-report vs. observed, board-level storytelling.
- 4
Policy and governance
The minimum viable policy in 5 documents, data classification, tool approval matrix, vendor risk, incident response for AI events.
- 5
Risk register
Confidentiality, IP, liability, over-reliance, supply-chain risk, hallucination, bias, injection. How to track these in your existing risk process.
- 6
Talent, hiring, and career impact
What to change in job descriptions, interview loops, mentoring risks, performance review signals, why your best engineers quit when tools are done badly.
- 7
Workshop: draft a rollout plan
Draft a rollout plan for a hypothetical 300-engineer org. Reviewed by peers and a practitioner.
- 8
Workshop: build a measurement dashboard
Build a dashboard spec for your own org with leading and lagging indicators that your board will actually find useful.
- 9
Workshop: redline an AI usage policy
Take a sample AI usage policy and redline it for your context. Training, attestation, and record-keeping included.
- 10
Capstone: 12-month state-of-AI memo
Write your own 12-month state-of-AI-in-engineering memo, as if delivered a year from now. Reviewed by a practitioner for certification.
What you'll build
Every track includes graded hands-on labs on realistic codebases. No toy examples.
Rollout plan workshop
Draft a phased rollout plan for a 300-person engineering org. Pilot team selection, communication plan, success criteria, and rollback triggers.
Measurement dashboard spec
Design an AI adoption dashboard with metrics that matter. Leading indicators, lagging indicators, and the vanity metrics to explicitly exclude.
AI usage policy review
Redline a sample AI usage policy. Identify gaps in data classification, tool approval, incident response, and attestation.
Sample lesson preview
Lesson preview
Measuring adoption honestly: vanity metrics vs. real ones
- Why 'number of completions accepted' tells you almost nothing about productivity
- The four metrics that actually correlate with engineering output improvement
- Self-report surveys vs. observed metrics: when each one lies and when each one is honest
- Hands-on: build a dashboard spec for your own org using a provided template
Certified AI Engineering Leader
Complete this track to earn your CAEL badge. Certifications are earned through practical assessment — a written exam plus a hands-on practical — not just quiz scores. Exportable as Open Badges 2.0 and verifiable by URL.
Badges are valid for 18 months, renewable with a short refresh assessment.
Start your team's training
Per-seat annual plans start at $300/user. Enterprise pricing available for teams over 200.
Not sure where to start?
Take our free 3-minute AI maturity assessment and get a personalized recommendation for which tracks fit your team.