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AI_PM_UPLEVEL_IDEAS.md

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AI PM — keep-up stack & craft reps (backlog)

Context: Enterprise AI PM (fintech / capital markets) shipping assistants, agents, workflow automation.

Already in place

  • Daily AI news digest (ai-compass)
  • Daily X Following recap (ai-compass)
  • Weekly: AI Platform Deltas brief (ai-compass) — scheduled

Backlog ideas (to implement later)

1) Weekly “Model + Platform Delta” review → plus your POV notes

Purpose: turn news into shipping-relevant decisions.

Automation (ai-compass brief) can provide:

  • Deltas that matter
  • Implications for enterprise AI PMs
  • Suggested experiments
  • Watchlist

Optional add-on (private):

  • “Reflection prompts” section (or a separate private note) so you can write:
    • What I believe now
    • What I’ll try next week

2) Tiny eval habit (1–2 hrs/week)

Purpose: build opinions backed by measurement.

Implementation options:

  • Create a small eval harness repo (or folder inside ai-compass) with:
    • 30–100 representative tasks (domain-specific)
    • scoring rubric
    • baseline prompts
    • model/provider configuration
  • Weekly routine:
    • test one change (prompt/model/tool/UI)
    • log results + failure modes

Potential automation:

  • Generate an “Eval Run Log” MDX (or JSON) weekly with:
    • what changed
    • metrics snapshot
    • notable failures

3) AI product / UX pattern library (and anti-patterns)

Purpose: codify craft so you can reuse it across products.

Suggested structure:

  • Pattern name
  • Problem
  • When to use
  • Implementation notes
  • Risks / failure modes
  • Enterprise/fintech notes (audit, controls, data handling)

Examples to cover:

  • Onboarding to first successful outcome
  • Uncertainty UX & calibrating confidence
  • Human-in-the-loop approvals
  • Safe tool execution defaults
  • Cost/latency budgeting and user-visible controls
  • Incident handling (bad answer recovery)

4) One deep thread per week (depth over feeds)

Purpose: develop non-consensus POV.

Rotation candidates:

  • Agents/tool execution + permissions
  • Reliability engineering for LLM systems
  • RAG/knowledge systems + freshness
  • Data flywheels/feedback loops
  • Enterprise procurement/security/compliance
  • Regulation, privacy, and auditability

Output template:

  • 1-page synthesis:
    • core idea
    • what changed my mind
    • implications for our product choices

5) Weekly teardown of 1 AI product (20–30 min)

Purpose: reps on product judgment.

Teardown template:

  • User & JTBD
  • Where AI creates value vs risk
  • Trust + cost + latency tradeoffs
  • Wedge + why now
  • What I’d copy / avoid

Potential automation:

  • Maintain a content/teardowns/ section in ai-compass.

6) Operator signal (talk to people running AI in prod)

Purpose: see what breaks in real deployments.

Track:

  • reliability incidents
  • cost spikes
  • tool execution failures
  • governance/audit issues
  • model/vendor regressions

Potential automation:

  • A “Reliability & Ops Watch” monthly roundup (public sources only).

7) Monthly POV memo (1–2 pages)

Purpose: crystallize your point of view and improve communication.

Prompts:

  • What’s overhyped and why?
  • What’s underappreciated and why?
  • What’s inevitable next?
  • What would I build in 30 days and why?

Potential automation:

  • Draft-only memo scaffold with placeholders + links; you fill in the POV.

Questions to decide later

  • Preferred cadence for POV memo (monthly vs biweekly)?
  • Should teardowns be public in ai-compass or private?
  • Which eval harness format (simple rubric spreadsheet vs scripted harness)?
  • Key enterprise constraints to bake into every brief (SOC2, data retention, model residency, audit trails)?