Zapier AI fluency

ai-fluency.md

Role	Unacceptable	Capable	Adoptive	Transformative
Engineering	Calls AI coding assistants “too risky” Has never tested AI-generated code Relies only on Stack Overflow snippets	Uses ChatGPT / Copilot / etc for simple coding tasks (e.g. regex, unit-test stubs) Can explain how they prompt, review, and validate AI output	Chains LLM calls with fallback + retry logic Adds eval tests to flag hallucinations Knows Claude Code, Cursor, Windsurf, etc Can walk interviewers through prompt tweaks, token limits, code review	Ships LLM-powered features, monitors live metrics, and refines based on user feedback Builds an AI-first dev pipeline (guardrails, RAG docs, etc) that cuts down PR cycle time
Product	Dismisses AI as hype, showing no curiosity about user value PRDs and prototypes lack any AI concepts or experiments	Uses ChatGPT to draft PRDs, story maps, and synthesize user-interview notes Knows basics (LLMs, embeddings, latency vs cost) and can share example prompt patterns	Ships an AI-powered feature with a clear “human-in-the-loop” check Chooses models based on accuracy, latency, throughput, and context-window constraints Demonstrates ROI (e.g. cut time-to-insight from 5 days to 3)	Builds / drives product strategy and org-wide AI roadmap through eval-first product development Launches a proprietary fine-tuned LLM feature that opens up a new pricing tier
Support	Refuses to use AI for support workflows Has zero automation skills (no rules, macros, bots, etc) Manually handles every ticket	Summarizes tickets with ChatGPT and cites faster context shifts Knows and follows Security / TSO approval flow before trying new tools	Builds Zapier workflows that triage queues and auto-tag CRM records Tracks CX metrics, refines prompts when AI misreads tone, keeps a living doc of what works	Rolls out an org-wide AI triage bot that cuts first-response time by 25% Creates and presents ROI dashboards, balancing cost vs customer experience in quarterly planning
People / HR	Distrusts all AI hiring tools Screens each resume one-by-one Relies on manual scheduling and candidate follow-ups	Drafts interview guides and summarizes panels with ChatGPT, saving ~2 hours/week Can explain privacy limits (e.g. no PII in public models)	Automates onboarding docs; runs LLM resume-screen with bias checks, yielding 3× faster shortlists Measures time-to-hire gains and refines prompts for under-represented talent pools	Revamps recruiting funnel with AI to shorten time-to-hire by 30% Trains HRBPs on safe AI and shapes company policy on ethical hiring AI
Marketing	Runs campaigns without AI-driven A/B tests or content variants Ignores AI tools for analytics, personalization, or audience insights	Uses AI to summarize customer stories Drafts first drafts of social posts and headlines with AI, then edits by hand	Runs a basic AI stack and A/B-tests copy to increase CTR by 30% Audits biased language and optimizes prompt libraries	Builds an AI-driven campaign engine to personalize content at scale Leads quarterly AI trainings, sets tooling roadmap, and speaks at industry events on AI-powered growth

Source: https://x.com/wadefoster/status/1930680089651425452