AI for Engineering Leaders: Course Direction Survey

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Section 1: About You

A few quick questions so we can tailor the cohort to the room.

What best describes your current role?

Engineering Manager (managing ICs)

Senior EM / Director (managing managers)

VP of Engineering or above

Tech Lead / Staff+ with leadership scope

Clear selection

How large is the engineering organization you oversee?

1–10 engineers

11–30 engineers

31–100 engineers

100+ engineers

Clear selection

What stage is your company at?

Early-stage startup (pre-Series B)

Growth-stage startup (Series B+)

Mid-size company (500–5,000 employees)

Large enterprise (5,000+ employees)

Clear selection

How would you describe your current level of AI usage? Select the option that best reflects your day-to-day experience.

Just getting started - I've tried tools like ChatGPT or Copilot a few times but don't use them regularly

Casual user - I use AI regularly for everyday tasks — summarizing emails, drafting content, or editing.

Active experimenter -I'm exploring AI beyond basics — testing prompts, using AI in workflows, or piloting tools with my team.

Practitioner -I integrate AI into my engineering processes — code generation, reviews, internal tooling, or automation.

Advanced / Builder- I design or deploy AI-powered systems — fine-tuning models, building agents, or architecting LLM pipelines.

Clear selection

Section 2: What Should We Cover?

AI is changing everything from how we hire to how we ship. We can’t go deep on all of it in a short cohort — these questions help us figure out where to invest.

Rank these four topic areas by how urgent they are for you right now (1 = most urgent).

1 - Most Urgent

4 - Least Urgent

People & Talent (Evaluating engineers when AI writes half the code; rethinking hiring to detect real skill vs. AI-assisted performance; coaching teams on productive AI adoption.)

Delivery & Quality (Maintaining the quality bar when velocity spikes; redefining “good output”; managing the paradox that AI makes teams faster but incidents are rising.)

Team Operations (Using AI for planning, tracking, bug triage, tech debt management, and communication — the day-to-day workflows AI can actually improve right now.)

Strategy & Culture (Building an AI-forward team culture intentionally; deciding where AI creates real leverage vs. noise; driving adoption without mandate fatigue.)

1 - Most Urgent

4 - Least Urgent

People & Talent (Evaluating engineers when AI writes half the code; rethinking hiring to detect real skill vs. AI-assisted performance; coaching teams on productive AI adoption.)

Delivery & Quality (Maintaining the quality bar when velocity spikes; redefining “good output”; managing the paradox that AI makes teams faster but incidents are rising.)

Team Operations (Using AI for planning, tracking, bug triage, tech debt management, and communication — the day-to-day workflows AI can actually improve right now.)

Strategy & Culture (Building an AI-forward team culture intentionally; deciding where AI creates real leverage vs. noise; driving adoption without mandate fatigue.)

Clear selection

Within People & Talent, which challenge would you most want the cohort to tackle?

AI-based engineer evaluation — How do you assess performance when AI writes half the code?

Rethinking hiring — How do you design interviews that reveal real capability when candidates have AI assistance?

Coaching your team through the transition — How do you guide engineers to adopt AI without creating dependency or resistance?

Clear selection

Within Delivery & Quality, which tension feels most relevant to you?

Speed vs. quality — Teams ship faster with AI, but change failure rates and incidents are climbing.

Redefining what to measure — PR count, velocity, and traditional metrics don’t capture what matters anymore.

Making timelines transparent — AI compresses execution but not alignment. How do you shift focus to requirements, design, and coordination?

Clear selection

Within Team Operations, where do you see the biggest opportunity?

Planning & status tracking — AI to support sprint planning, surface risks early, and flag blockers automatically.

Code health & tech debt — Smarter bug triage, pattern detection in incidents, and prioritizing maintenance work.

Communication & context sharing — Improving information flow across teams with AI-assisted summaries and updates.

Clear selection

Section 3: Topic Deep-Dive

Pick the specific topics you’d most like us to spend time on.

Which topics are you most interested in covering? Pick your top 5.

Evaluating and adopting AI coding assistants (Copilot, Cursor, etc.)

Measuring the real productivity impact of AI on engineering teams

Building an AI adoption strategy and rollout plan for your org

LLM fundamentals every engineering leader should understand

Building AI-powered features into your product

Managing and upskilling your team in the age of AI

AI’s impact on hiring, team structure, and career ladders

Security, compliance, and risk management with AI tools

AI agents and agentic workflows

Cost management and budgeting for AI tooling

Vendor evaluation: how to choose between AI platforms and providers

Leading through uncertainty — communicating AI strategy to execs and ICs

What is the single biggest challenge you’re facing with AI in your org right now?

Section 4: Depth & Format

Help us get the balance right between big-picture strategy and hands-on tactics.

Where on this spectrum should the cohort sit?

Mostly strategic — Where AI is headed, how to position my team, what bets to make. I can figure out the tactics.

Mostly tactical — Concrete approaches: how to restructure my perf review process, what metrics to track, how to run an AI adoption pilot.

Start strategic, end tactical — Give me the big picture first, then show me how to act on it.

Clear selection

What types of live sessions would be most valuable?

Frameworks & mental models — Structured tools for thinking (e.g., evaluation rubric, adoption readiness matrix)

Hands-on workshops — Work through real scenarios live (e.g., audit your workflow for AI opportunities)

Case studies — Real examples from engineering orgs: what worked, what didn’t, and why

Peer breakout sessions — Small-group discussions to work through your specific situation

What would make this course worth your time? Select all that apply.

Actionable frameworks I can apply at work immediately

Real case studies from engineering orgs similar to mine

Hands-on exercises and live demos

Networking with other engineering leaders facing similar challenges

A take-home playbook or toolkit

Direct Q&A time with the instructor

Peer breakout sessions to discuss my specific situation

How much time can you realistically commit to this course each week over the next one or two weeks? This includes live sessions, reading, and hands on exercises.

1–2 hours — I can attend live sessions but have minimal time for additional materials.

3–5 hours — I can attend sessions plus engage with readings and short exercises.

5–7 hours — I'm comfortable with sessions, assignments, and hands-on practice.

7+ hours — I'm fully committed and want to go deep with projects and experimentation.

Clear selection

Is there anything else you’d like us to know as we design this cohort?

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