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Leveraging AI as a Manager

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You do not need a computer-science background to get real leverage from AI as a manager. The gap of not being technical enough is exactly what this closes. The course is built around a manager's actual week: planning, writing the doc, prepping the review, reading the numbers, giving performance feedback, and making the call. You work in the tools that strong operators and FAANG teams actually use, meaning Claude, Codex and Cursor, rather than the automation builders that do not transfer. And if AI is reshaping who stays on your team, this is how you become the manager who multiplies output rather than the one being reorganized around.

The outcome

How does this prepare you for a job?

Managers are hired on judgment and leverage rather than on tooling trivia. Skill demonstrates itself, and where there is a mountain of skill there is no need for a pep talk.

  • Ran performance calibration and wrote promotion packets using AI-assisted synthesis of evidence from 1:1 notes, project docs and peer feedback, with every claim verified against its source.
  • Delivered structured performance feedback, using AI to draft it, pressure-test it for vagueness and bias, and tighten its specificity before the conversation.
  • Introduced AI-assisted planning, turning quarterly goals into roadmaps, PRDs and a status cadence.
  • Applied the AARRR framework to analyze activation and retention, using AI to segment cohorts and surface the drivers.
  • Built an AI-assisted hiring loop with structured rubrics, synthesis of interview notes, and calibration across interviewers, while keeping decision authority with humans.
  • Set team norms for responsible AI use, covering privacy boundaries, verification gates and disclosure.
Tools

AI tools learnt

Tools you can use on Monday, none of which require you to write production code.

Hands-on skills

  • Turn a quarterly goal into a PRD, a roadmap and a status cadence, with AI drafting and you editing
  • Synthesize 1:1 notes, peer feedback and shipped work into performance evidence ready for calibration, then verify every claim against its source
  • Draft performance feedback that is specific and behavioral, and pressure-test it for vagueness and bias before you deliver it
  • Run AARRR against a real funnel, segmenting cohorts, surfacing what drives activation, and separating correlation from cause
  • Pressure-test a decision by making the model argue the opposite case, then decide anyway
  • Prototype an idea in Lovable and Figma, so that a roadmap debate happens against an artifact rather than a paragraph
  • Read your team's pull requests in Cursor well enough to ask the right question, without writing the code yourself
  • Set the verification rule for AI output, covering what you check, what you cite, and what never goes into a prompt
Syllabus

Modules

Level: Basic

Where AI earns its place (and where it doesn't)

  • Map your week, separating the artifacts AI drafts from the ones it only reviews and the ones it never touches
  • The hallucination trap, and the verification rule you will adopt because of it
  • Privacy boundaries, meaning what never goes into a prompt

The recurring artifacts

  • Draft status reports, 1:1 agendas and PRDs with AI, then edit them yourself
  • Prepare reviews and meetings through summaries, agendas and decision memos
  • Build a reusable prompt library for the documents you write every month

Performance feedback, done well

  • Synthesize the evidence from 1:1 notes, peer input and shipped work
  • Draft feedback that is specific and behavioral, then strip out the vagueness and the bias
  • Rehearse the conversation, while the judgment stays yours

Reading the numbers

  • Analyze a funnel with AARRR, and segment the cohorts with AI
  • Separate signal from noise, and state the decision the data actually supports

Level: Advanced

Calibration and promotion at FAANG scale

  • Build promotion packets that survive a calibration room, covering evidence, scope and demonstrated impact
  • Use AI to find the gaps a calibration committee will attack, before they find them
  • Handle the distribution problem, covering ratings, pressure to stack-rank, and fairness

Decisions FAANG teams actually fight about

  • Netflix, and the trade-off between ads and engagement: model the objective and defend the metric you would optimize
  • Shipping against holding when there is a latency or cost regression
  • Write the one-page decision memo, and defend it in a review

An AI-assisted hiring loop

  • Structured rubrics, and synthesis of interview notes
  • Bias checks, and calibration across interviewers
  • Keep decision authority with humans, and document why

Team operating norms and leverage

  • Set the privacy, verification and disclosure norms for how your team uses AI
  • Measure the leverage through cycle time, document turnaround and decision latency
  • Make the case for your team's AI investment to a skeptical executive

Students can take the full course (both levels) or either level on its own.

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