One year later: Raising the AI fluency bar for every Zapier hire 

Unacceptable (if this is the extent of what they do)

• Uses AI as a lightweight assist inside a mostly unchanged workflow; helps with snippets, debugging, or summarization but does not materially change how they design, build, test, or ship.

• Cannot clearly explain tool or model choices, limitations, or how their usage has evolved; little evidence of intentional experimentation or repeatable workflows.

Capable

"I use AI to operate at a meaningfully higher level."

• Uses AI regularly across implementation, debugging, testing, and documentation, with concrete examples of better quality, speed, or leverage.

• Shows real tool and model literacy: can explain why they use different tools for different tasks, where those tools break down, and how they have refined their workflows over time.

Adoptive

"I orchestrate AI and build systems that elevate how I work."

• AI fundamentally changes how they engineer: they default to AI-first approaches where appropriate and have built workflows, tooling, or practices that improve output beyond just themselves.

• Demonstrates strong judgment about tradeoffs and failure modes by choosing tools intentionally, validating outputs, and building review, testing, or mitigation into the workflow.

Transformative

"I re-engineer how work happens."

• Re-engineers how software gets built so AI becomes part of the operating model, not just an individual productivity boost; code production, review, testing, and delivery are meaningfully restructured around it.

• Raises the bar for others by setting standards, building shared systems, and enabling teams to move materially faster without lowering quality, reliability, or safety.

Unacceptable (if this is the extent of what they do)

• Uses AI for simple tasks like summarizing, writing, or looking up information, but output reads like obvious AI slop.

• Cannot point to clear evidence that work is faster or higher quality. The work they do before AI and after AI looks largely the same.

Capable

"I use AI to operate at a meaningfully higher level."

• Has a structured approach for generating specs and prototypes that they reuse and refine across projects. The clarity of product direction has improved significantly.

• Uses AI to tap into user insights (quant and qual) previously inaccessible due to technical limitations or data scale.

• Rapidly generates working solutions users can try and give feedback on. Fidelity of what ships to users is both faster and higher quality.

Adoptive

"I orchestrate AI and build systems that elevate how I work."

• Can point to entirely new skill sets developed through AI: writing SQL, doing data analysis, building dashboards.

• Can speak to how they handle quality control and prevent AI from making mistakes.

• Building systems, not one-off features: pipelines of agents that take in customer feedback, write specs, prototype solutions, and ship small features.

• Has built always-on AI systems others rely on (e.g., automated monitoring of support tickets, NPS, etc).

Transformative

"I re-engineer how work happens."

• The PM/Design role on their squad looks materially different than six months ago, with clear evidence that AI is changing the role and improving outcomes.

• Can show examples of redesigning how product ships: abandoning the previous status quo and reinventing the process.

• Leading new ways of building product, such as unlocking PMs or Designers shipping code in production. Can speak to the tradeoffs.

Unacceptable (if this is the extent of what they do)

• Asks AI one-off questions to look something up, then goes back to doing work the same way. AI is a slightly faster Google, nothing more.

• Treats AI as a drafting shortcut for low-stakes written communication (Slack messages, email replies) but hasn't applied it to substantive work in their role.

• Cannot point to clear evidence that work is faster or higher quality. No examples, no before/after, no signal of impact.

Capable

"I use AI to operate at a meaningfully higher level."

• Has repeatable prompts for core parts of their job. Can describe what they put in, what they get back, and how it's made that task faster or better.

• Feeds relevant context into AI before complex work rather than asking isolated questions. Understands that output quality reflects input quality.

• Uses AI to do things they wouldn't have had time to do before: more thorough prep, a second pass on quality, a structured summary for a stakeholder.

Adoptive

"I orchestrate AI and build systems that elevate how I work."

• Their use of AI has a clear before/after story that spans months, not tasks. Six months ago they were doing X manually; now a connected set of tools handles X, and they've picked up additional high leverage work.

• Produces work that others on the team use: a tool, a template, a process. Their AI capability isn't just personal productivity; it's raised the floor for people around them.

• Actively iterates on their AI workflows. They treat their AI setup like a product: it has versions, it gets updated when the work changes, and they can describe what they'd build next.

Transformative

"I re-engineer how work happens."

• Has changed what work their team does, not just how fast they do it. Categories of work either no longer exist or run without human involvement.

• Has influenced how the broader org works: frameworks introduced, processes replaced, ways of working others have adopted.

• Thinks about AI in terms of operating models and role design. Can articulate what roles should look like in 12 months, what will be automated, and what new skills will matter. Outcomes are materially different, not just faster.

Unacceptable (if this is the extent of what they do)

• Uses AI for first drafts only. Output reads like unedited AI; hasn't developed a process for improving quality or adapting tone.

• Uses AI for campaign ideas but has no real workflow. Cannot explain how AI has changed their process, speed, or output quality. Before AI and after AI looks the same.

Capable

"I use AI to operate at a meaningfully higher level."

• Uses AI regularly across content, SEO analysis, and performance review. Output volume and quality are both up, and they can point to specific examples.

• Built a reusable prompt library for top content formats that the team now pulls from. Can explain how they've iterated on it over time and why certain approaches work better than others.

Adoptive

"I orchestrate AI and build systems that elevate how I work."

• Has run AI-driven experiments with measurable results (e.g., A/B testing copy that increased CTR by 18%) and now defaults to this approach across campaigns.

• Built a content system that drafts, formats, and schedules posts across channels. The team stopped doing that manually.

• Has built always-on agentic workflows that run without human involvement: content pipelines, monitoring, or campaign ops that operate 24/7.

Transformative

"I re-engineer how work happens."

• Built a personalization engine that serves AI-generated campaign variants at scale, tied directly to pipeline.

• Restructured how the marketing team works: what gets automated, what gets owned, how success gets measured.

• Has automated entire job categories and is driving measurable impact on pipeline and revenue. The team's output is qualitatively different from what was possible before AI.

Unacceptable (if this is the extent of what they do)

• Manually does work AI could meaningfully assist with (comp modeling, scenario planning, workforce analysis) and hasn't tested whether AI would improve it. Skepticism is untested, not informed.

• Actively blocks their team from experimenting with AI. No AI Builder setup, no enablement participation. Becomes a bottleneck for transformation work.

Capable

"I use AI to operate at a meaningfully higher level."

• Uses AI daily across multiple parts of their role with repeatable prompt templates they refine each cycle. Can name the growth arc: started with one-off prompts, now uses AI for every major touchpoint. Quality and speed both improved.

• Has connected AI tools into recurring workflows (e.g., auto-summaries into team status updates, AI-drafted content edited for voice). Sets direction for their team's AI experimentation; carved out time and created safe-to-fail norms.

Adoptive

"I orchestrate AI and build systems that elevate how I work."

• Has orchestrated end-to-end automation of a core People process (onboarding, hiring pipeline, reporting) with measurable results. Piloted against the manual process, tracked outcomes, and updated the team's standard workflow based on evidence.

• Redesigned how a core function works around AI-native logic: agentic screening, AI-generated deliverables, and built-in quality checks that flag issues before they reach decision-makers.

• Replaced recurring manual work (e.g., weekly reports) with live, AI-populated systems. Their role shifted from data compilation to strategic pattern recognition and predictive modeling.

Transformative

"I re-engineer how work happens."

• Stopped running a legacy program entirely and rebuilt the function around AI-first delivery. Agents generate personalized outputs from role data, deliver just-in-time content, and auto-assess completion. 

• Redefined team roles around the new operating model; team members now own agentic platforms rather than manual workflows. Successfully upskilled the broader People team and leaders to self-serve within the new paradigm. Produces measurable improvements in performance and effectiveness.

Unacceptable (if this is the extent of what they do)

• Uses AI for background research before drafting but the core review process is unchanged.

• Cannot point to evidence that reviews are faster, higher quality, or more consistent as a result of AI usage.

Capable

"I use AI to operate at a meaningfully higher level."

• Uses AI across every contract: redlining, risk flagging, drafting, negotiation prep. It's standard practice, not occasional.

• Built a reusable library in Claude, iterated on it over months, and reviews are faster and more consistent.

• Built a partner terms review agent that cut a 3-day process down to hours.

Adoptive

"I orchestrate AI and build systems that elevate how I work."

• Converted the enterprise negotiation playbook into an AI-assisted workflow other teams self-serve.

• Built an AI legal response and ticketing system that provides initial response based on the legal team curated knowledge source.

• Built a legal team dashboard that tracks status of projects, SLAs, and team capacity/workload, connecting Jira, Slack, Zip, Ironclad and other systems so that there's one view.

Transformative

"I re-engineer how work happens."

• Rebuilt legal ops so that intake, triage, and routing happen largely automatically. New requests get classified against the playbook and either handled autonomously or scoped for an attorney with context pre-assembled.

• Attorneys work from exceptions, not the full queue. Volume work largely disappears. Every judgment call gets captured as a skill refinement, so the system learns when to act and when to escalate.

Unacceptable (if this is the extent of what they do)

• Uses AI-generated call summaries after meetings.

• Uses AI to draft outreach emails or clean up follow-up notes.

• Asks ChatGPT or Claude one-off questions to research an account or prep for a call.

• Can't describe how AI has changed their win rate, deal velocity, or pipeline quality—usage is convenient but not strategic.

• The work they do before AI and after AI looks largely the same.

Capable

"I use AI to operate at a meaningfully higher level."

• Runs AI-powered tools daily across core sales workflows—account research, usage analysis, call prep—and can describe how each has made them materially faster or more effective.

• Built a repeatable pre-call research workflow (company signals, stakeholder mapping, competitive context, product usage data) they run before every meeting and have iterated over time—not just one-off prompts.

• Uses AI to self-serve quota attainment analysis, pipeline feasibility, and unworked lead prioritization without waiting on ops or analysts.

• Can demo their personal AI workflow live and explain where they trust it and where they don't.

• Positions AI transformation strategies with customers at both the tactical and outcomes level—can credibly walk a prospect through what AI-powered automation looks like in their business.

Adoptive

"I orchestrate AI and build systems that elevate how I work."

• Chains multiple AI tools into connected workflows that go beyond what any single tool does alone—e.g., AI-driven account research feeds into a personalized outreach sequence, which feeds into a deal channel with auto-generated deal qualification analysis and stakeholder heatmaps, and the whole system runs without manual handoffs between steps.

• Built or adopted tools the team now relies on—like a multithreading analysis that pulls contact engagement data, classifies by seniority and function, and generates a visual coverage heatmap so reps spot executive gaps before deals stall. Or a usage monitoring workflow that visualizes customer consumption against their plan limit so upgrade conversations are timed to data, not gut feel.

• Has measurable before/after impact on a specific motion (e.g., "prospecting pipeline went from X to Y after building this workflow" or "cut deal prep from 2 hours to 15 minutes and quality improved — I can show you the output").

• Proactively shares what's working—has become a go-to resource, run enablement sessions, or contributed reusable workflows others pull from.

Transformative

"I re-engineer how work happens."

• Redesigned core sales motions around AI-native workflows—not legacy processes with AI layered on top. For example, renewal flows where an AI agent analyzes account history, usage, and engagement, routes ownership, and generates a renewal brief so reps start at strategy, not data gathering.

• Built a partner ecosystem flywheel: automated portfolio research produces partner reports, meeting follow-ups drafted from call notes, and CRM cross-referencing surfaces warm intro opportunities—workflows that compound and improve with use.

• Delivered measurable team-level impact, improving metrics like pipeline coverage, stage velocity, forecast accuracy, and rep ramp time.

• Actively enables others by sharing playbooks, running sessions, and building reusable workflows that raise the team’s baseline.

• Reimagines the rep role: AI owns the operational layer (data entry, CRM hygiene, prep, follow-ups, reporting), while reps focus on judgment, negotiation, and strategic deal shaping.

Unacceptable (if this is the extent of what they do)

• Uses AI occasionally for note cleanup, summaries, and light analysis, but doesn't rely on it. Low impact on the actual work.

• Cannot point to evidence that reporting is faster, analysis is deeper, or decisions are better as a result.

Capable

"I use AI to operate at a meaningfully higher level."

• Uses AI across reporting, analysis, and synthesis every week. Output is faster and clearer.

• Built repeatable workflows for recurring analyses, iterated on them over time, and they're now embedded in how the work gets done.

• Can explain where AI improves quality, not just speed.

Adoptive

"I orchestrate AI and build systems that elevate how I work."

• Connected data sources, reporting tools, and AI into one system. Insights surface without manual pulls and the team runs it.

• Built automated reporting workflows that eliminated recurring manual work at scale.

• Tracks where outputs are weak and iterates; embedded validation before anything goes to leadership.

Transformative

"I re-engineer how work happens."

• Rebuilt planning, reporting, and decision systems around AI; not the old process with AI layered on.

• Stopped legacy static reporting entirely; reallocated that capacity to forward-looking analysis and strategic work.

• Redefined what ops does: judgment and prioritization is what's left. AI handles the operational and analytical layer.

Unacceptable (if this is the extent of what they do)

• Uses AI for basic research only, like to summarize companies or markets.

• Tries AI for diligence but doesn't rely on it.

• Cannot point to evidence that diligence is faster, analysis is deeper, or deal quality has improved.

Capable

"I use AI to operate at a meaningfully higher level."

• Uses AI across deal evaluation every week: market mapping, comps, CIM synthesis, and IC memo drafting.

• Built a repeatable diligence workflow, iterated on it across multiple deals, and output quality has improved materially.

• Can explain where AI accelerates judgment and where they still own the call.

Adoptive

"I orchestrate AI and build systems that elevate how I work."

• Connected deal sourcing, research tools, and AI into one pipeline; cut time-to-IC-memo meaningfully and the team runs it.

• Built a target screening workflow that scores inbound opportunities automatically and surfaces the ones worth pursuing.

• Tracks where the system misses and iterates; has embedded validation steps before anything goes to leadership.

Transformative

"I re-engineer how work happens."

• Rebuilt how corp dev works: sourcing, screening, diligence, and memo generation are AI-first. Team focuses on deal judgment and relationship work.

• Stopped legacy research and manual comp processes entirely; reallocated that capacity to higher-value deal activity.

• Agents handle first-pass evaluation; humans engage at conviction and negotiation.

Unacceptable (if this is the extent of what they do)

• Uses AI occasionally for partner comms.

• Uses AI to draft partner emails or materials.

• Tries AI for research but not embedded in workflow.

Capable

"I use AI to operate at a meaningfully higher level."

• Uses AI across partner workflows every week (research, outreach, co-sell prep, and enablement materials).

• Built a repeatable workflow for partner QBR prep or integration prioritization, iterated on it, and it's changed how the work gets done.

• Output quality has improved across the role, not just speed.

Adoptive

"I orchestrate AI and build systems that elevate how I work."

• Connected CRM, partner data, and AI into one system. Partner tiering, outreach, and performance tracking run without manual pulls.

• Built an enablement workflow the whole team uses. Partners onboard faster and the team stopped building materials from scratch.

• Tracks where the system produces weak output and fixes it; embedded review steps before anything goes to partners.

Transformative

"I re-engineer how work happens."

• Rebuilt how the ecosystem function operates. Everything about how partners are sourced, tiered, enabled, and measured is AI-native.

• Stopped legacy manual tracking and one-off outreach; reallocated capacity to strategic partnership development.

• Redefined what the role does. Relationship and strategy work is what's left; AI handles the operational layer.

Unacceptable (if this is the extent of what they do)

• Uses AI for basic support tasks.

• Uses AI to summarize reports or explain concepts.

• Tries AI for analysis but doesn't rely on it.

Capable

"I use AI to operate at a meaningfully higher level."

• Uses AI for forecasting, variance analysis, and reporting.

• Uses AI for tax research, provision prep, and filing review.

• Uses AI for close checklists, flux analysis, and journal entry review.

• Builds repeatable workflows across FP&A, tax, and accounting.

Adoptive

"I orchestrate AI and build systems that elevate how I work."

• Automates scenario modeling, reporting, and insight generation.

• Built a workflow for tax memo drafting, jurisdiction risk flagging, and quarterly review.

• Automated reconciliations and journal entry exceptions. Surfaces issues without manual hunting.

Transformative

"I re-engineer how work happens."

• Shifts from backward-looking reporting to forward-looking AI models.

• Rebuilt tax ops around AI: research, structuring, and audit prep are AI-first.

• Redesigned the close process with AI; cut close time materially with better documentation.

Unacceptable (if this is the extent of what they do)

• Uses AI only for one-off tasks, not as part of regular workflows.

• Applies AI narrowly (e.g., isolated help with queries) without broader impact.

• Experiments with tools but does not develop repeatable patterns or ways of working.

• Does not integrate AI into how work is planned, executed, or delivered.

Capable

"I use AI to operate at a meaningfully higher level."

• Uses AI consistently across core workflows (e.g., analysis, modeling, engineering, experimentation).

• Improves speed and quality of outputs (e.g., queries, models, code, insights) while maintaining rigor and reliability.

• Develops repeatable ways of working with AI in recurring tasks.

• Validates outputs and ensures work meets standards for accuracy, reproducibility, and trust.

Adoptive

"I orchestrate AI and build systems that elevate how I work."

• Builds repeatable AI workflows that significantly reduce manual work in analysis, reporting, or data development.

• Integrates AI into shared processes so work is more scalable, consistent, or self-serve.

• Uses AI to connect tools and workflows where it meaningfully improves execution or reduces friction.

• Enables others to move faster by codifying and championing adoption of AI patterns, workflows, or systems.

Transformative

"I re-engineer how work happens."

• Enables teams across Zapier to independently ingest and use data through AI-powered systems, with appropriate guardrails for accuracy, trust, and safety.

• Replaces significant manual or heuristic work streams with durable AI systems that automate ingestion, transformation, and insight generation.

• Establishes AI platforms and workflows where insights are surfaced proactively and teams operate with materially more speed, independence, and leverage.

Unacceptable (if this is the extent of what they do)

• Uses AI to tighten emails or summarize meetings; priority routing and judgment work are unchanged and there's no repeatable workflow or measurement.

• Tries ChatGPT for travel ideas or one-off summaries; cannot describe iteration or where AI must not be used for exec ops work.

Capable

"I use AI to operate at a meaningfully higher level."

• Every week: AI aggregates calendar, attendee context, and prior threads into a fixed brief; they iterate prompts, spot hallucinations against source material, and can cite time saved and quality improvements.

• A structured flow where AI does a first structured pass of emails (classify, draft in exec voice), and the EA owns what ships with explicit rules for sensitive topics.

Adoptive

"I orchestrate AI and build systems that elevate how I work."

• Connected calendar, CRM, email/Slack, and note tools so one system produces briefings, flags conflicts, and routes follow-ups; others reuse it; impact is described (e.g., prep time, misses avoided).

• Ran a time-boxed experiment (e.g., AI-first pass on all weekly stakeholder updates); measured outcome; kept workflows that improved quality and dropped others; updated team process.

Transformative

"I re-engineer how work happens."

• Replaced a weekly long-form update with a live dashboard and short AI brief; freed capacity for forward-looking work; stakeholders changed how they consume information or complete work.

• Redefined responsibilities (e.g., EA owns exec AI infrastructure and guardrails), trained teams, or scaled patterns to multiple execs—governance and role change, not only personal productivity.