AI Fluency Rubric

A framework for measuring where we are and where we're heading.

This rubric helps you figure out where you are today and what the next step looks like.

This is our first pass. It'll evolve as we learn. But we need a starting point so each functional lead can honestly assess their team and figure out where to focus.

We think about AI proficiency as a power curve with four levels:

0×

Disengaged

Uses AI occasionally but nothing about your work has actually changed. Same process, same output, just with a chatbot open on the side. If you're here and not actively moving up, you're below the bar.

1×

Competent

You're meaningfully better with AI than without it. You use it daily across your core work. You can show specific examples of how it's made you faster or your output higher quality. You've built repeatable prompts and workflows you actually rely on. This is the minimum for everyone within 90 days.

5×

Operator

You've gone beyond your own desk. You're building workflows, automations, or tools that other people use. You think in systems, not one-off prompts. You can point to something you've built that's changed how your team works. This is where functional leads need to be.

10×

Builder

You've re-engineered how the work gets done so it looks fundamentally different. Categories of work have been eliminated or run without humans. You're building the infrastructure that levels up your whole function. This is where we're heading.

We assess across four dimensions: whether you've got the right mindset (AI as infrastructure, not a toy), whether you've got a strategy (you can explain how AI changes your function), whether you're actually building (repeatable workflows, not one-off prompts), and whether you take accountability (you own the output, not just the input).

What each level looks like per function

Product

Engineering & Design

0×Disengaged

Uses AI for snippets and debugging but hasn't changed how they design, build, test, or ship. Can't explain their tool choices or how their usage has evolved.

1×Competent

Uses AI regularly across implementation, debugging, testing, and docs. Shows real tool literacy and has a structured approach for generating specs and prototypes they reuse across projects.

5×Operator

AI fundamentally changes how they engineer. Defaults to AI-first where appropriate. Built workflows and tooling that improve output beyond just themselves. Strong judgment on tradeoffs and failure modes.

–Can point to new skill sets developed through AI: writing SQL, data analysis, building dashboards
–Rapidly generates working solutions users can try. Fidelity and speed both up

10×Builder

Re-engineers how software gets built so AI is part of the operating model. Code production, review, testing, and delivery meaningfully restructured. Building towards the self-healing, self-evolving product described above.

–AI triages incidents, runs test suites, resolves common issues autonomously
–Engineers steer with judgment and taste instead of grinding boilerplate

Customer Success

0×Disengaged

Uses AI to draft follow-up emails or summarise calls but the core work is unchanged. Account plans, renewal prep, health tracking still done manually the same way.

1×Competent

Has repeatable prompts for account research, meeting prep, and renewal drafting. Feeds real context into AI before customer conversations. Uses AI to do things they wouldn't have had time for before: deeper account analysis, structured pre-reads, stronger prep.

5×Operator

Clear before/after story. Built account plan templates, health score frameworks, or engagement workflows that the whole team uses. AI surfaces risk signals and expansion opportunities before conversations, not after.

–Automated renewal prep and account review generation
–Building AI-driven account intelligence that flags churn risk and expansion triggers

10×Builder

The function is proactive by default. Categories of manual work eliminated. CSMs walk into every conversation with a complete picture generated automatically.

–Account health, risk, and expansion running as live systems, not quarterly exercises
–CSMs operating as strategic partners, not administrators of the relationship

Customer Support

0×Disengaged

Asks AI one-off questions then goes back to handling tickets the same way. Uses it to draft Slack messages but hasn't applied it to real work. No evidence of impact.

1×Competent

Has repeatable prompts for core parts of their job. Feeds ticket context, product docs, and customer history into AI before responding. Uses AI to do things they wouldn't have had time for before: thorough investigation, structured summaries, second-pass quality checks.

5×Operator

Clear before/after story spanning months. Produces tools, templates, or processes others on the team use. Actively iterates on their AI workflows like a product.

–Has built or is building bespoke AI agents personalised per customer or account
–AI drafts first responses, classifies tickets, and surfaces knowledge articles before the rep opens the ticket

10×Builder

Changed what the team does, not just how fast. Categories of work either no longer exist or run without human involvement. Building towards the dedicated AI partner per customer described above.

–Knowledge base writes and updates itself as the product ships
–Support reps operate as proactive technical experts, not ticket coordinators

Sales

0×Disengaged

Uses AI call summaries and drafts outreach emails. Can't describe how AI has changed win rate, deal velocity, or pipeline quality. Before and after looks the same.

1×Competent

Uses AI products daily across core workflows: research, HubSpot analysis, call prep. Built a personal prospecting workflow that saves significant time. Self-serves pipeline analysis without waiting on ops.

5×Operator

Chains multiple AI tools into repeatable workflows. AI research flows into account plans into personalised outreach. Shares what's working and has become a go-to resource.

–Uses AI to surface signals they wouldn't have found manually: expansion triggers from usage data, competitive displacement opportunities
–Every salesperson operating as a forward-deployed sales engineer

10×Builder

Redesigned core sales motions around AI-native logic. AI owns the operational layer: CRM updates, meeting notes, pipeline analysis, enrichment, prospecting. Reps focus on judgment and relationships.

–Preparation is automatic. Follow-ups draft themselves
–The salesperson is fully present, fully prepared, and fully human in every conversation

Marketing & Content

Growth & Content

0×Disengaged

Uses AI for first drafts only. Output reads like unedited AI. No real workflow. Can't explain how AI has changed their process or output quality.

1×Competent

Uses AI regularly across content, SEO, and performance review. Volume and quality both up with specific examples. Built a reusable prompt library the team pulls from.

5×Operator

Running AI-driven experiments with measurable results. Built a content system that drafts, formats, and schedules across channels. Always-on agentic workflows running without human involvement.

–Content pipelines, monitoring, and campaign ops operating 24/7
–A/B testing at scale with AI-generated variants

10×Builder

Content connected directly to the codebase. A feature ships, a page appears. Video walkthroughs that re-record themselves when the product changes. The marketing team's highest contribution is strategy, positioning, and creative direction. AI handles the rest.

–Personalisation engine at scale tied to pipeline
–Automated entire job categories with measurable revenue impact

Exec Ops

Operations & Founders

0×Disengaged

Uses AI to tighten emails or summarise meetings. Priority routing and judgment work unchanged. No repeatable workflow.

1×Competent

AI aggregates calendar, context, and prior threads into a weekly brief. Structured flow where AI does first pass on emails and comms. Can cite time saved with specific examples.

5×Operator

Connected calendar, CRM, email, Slack, and notes into one system that produces briefings, flags conflicts, and routes follow-ups. Others reuse what they've built.

10×Builder

Replaced recurring reporting with live dashboards and AI briefs. Freed capacity for strategic work. Redefined responsibilities. Stakeholders changed how they consume information.

People

People Experience

0×Disengaged

Manually does work AI could meaningfully assist with: comp modelling, scenario planning, workforce analysis. Hasn't tested whether AI would improve it. Blocks team from experimenting.

1×Competent

Uses AI daily with repeatable prompt templates refined each cycle. Connected AI tools into recurring workflows. Sets direction for their team's AI experimentation.

5×Operator

Automated a core People process end-to-end with AI-native logic: agentic screening, AI-generated deliverables, quality checks. Replaced recurring manual work with live, AI-populated systems.

–Every person has their own AI agent managing calendars, surfacing priorities, drafting communications
–Office manager agent per region with full memory of how things operate

10×Builder

Rebuilt the function around AI-first delivery. Agents generate personalised outputs from role data, deliver just-in-time content, auto-assess completion. Redefined team roles. The operational overhead of running a company this size shrinks dramatically.

	0× Disengaged	1× Competent	5× Operator	10× Builder

Product Engineering & Design	Uses AI for snippets and debugging but hasn't changed how they design, build, test, or ship. Can't explain their tool choices or how their usage has evolved.	Uses AI regularly across implementation, debugging, testing, and docs. Shows real tool literacy and has a structured approach for generating specs and prototypes they reuse across projects.	AI fundamentally changes how they engineer. Defaults to AI-first where appropriate. Built workflows and tooling that improve output beyond just themselves. Strong judgment on tradeoffs and failure modes. –Can point to new skill sets developed through AI: writing SQL, data analysis, building dashboards –Rapidly generates working solutions users can try. Fidelity and speed both up	Re-engineers how software gets built so AI is part of the operating model. Code production, review, testing, and delivery meaningfully restructured. Building towards the self-healing, self-evolving product described above. –AI triages incidents, runs test suites, resolves common issues autonomously –Engineers steer with judgment and taste instead of grinding boilerplate

Customer Success	Uses AI to draft follow-up emails or summarise calls but the core work is unchanged. Account plans, renewal prep, health tracking still done manually the same way.	Has repeatable prompts for account research, meeting prep, and renewal drafting. Feeds real context into AI before customer conversations. Uses AI to do things they wouldn't have had time for before: deeper account analysis, structured pre-reads, stronger prep.	Clear before/after story. Built account plan templates, health score frameworks, or engagement workflows that the whole team uses. AI surfaces risk signals and expansion opportunities before conversations, not after. –Automated renewal prep and account review generation –Building AI-driven account intelligence that flags churn risk and expansion triggers	The function is proactive by default. Categories of manual work eliminated. CSMs walk into every conversation with a complete picture generated automatically. –Account health, risk, and expansion running as live systems, not quarterly exercises –CSMs operating as strategic partners, not administrators of the relationship

Customer Support	Asks AI one-off questions then goes back to handling tickets the same way. Uses it to draft Slack messages but hasn't applied it to real work. No evidence of impact.	Has repeatable prompts for core parts of their job. Feeds ticket context, product docs, and customer history into AI before responding. Uses AI to do things they wouldn't have had time for before: thorough investigation, structured summaries, second-pass quality checks.	Clear before/after story spanning months. Produces tools, templates, or processes others on the team use. Actively iterates on their AI workflows like a product. –Has built or is building bespoke AI agents personalised per customer or account –AI drafts first responses, classifies tickets, and surfaces knowledge articles before the rep opens the ticket	Changed what the team does, not just how fast. Categories of work either no longer exist or run without human involvement. Building towards the dedicated AI partner per customer described above. –Knowledge base writes and updates itself as the product ships –Support reps operate as proactive technical experts, not ticket coordinators

Sales	Uses AI call summaries and drafts outreach emails. Can't describe how AI has changed win rate, deal velocity, or pipeline quality. Before and after looks the same.	Uses AI products daily across core workflows: research, HubSpot analysis, call prep. Built a personal prospecting workflow that saves significant time. Self-serves pipeline analysis without waiting on ops.	Chains multiple AI tools into repeatable workflows. AI research flows into account plans into personalised outreach. Shares what's working and has become a go-to resource. –Uses AI to surface signals they wouldn't have found manually: expansion triggers from usage data, competitive displacement opportunities –Every salesperson operating as a forward-deployed sales engineer	Redesigned core sales motions around AI-native logic. AI owns the operational layer: CRM updates, meeting notes, pipeline analysis, enrichment, prospecting. Reps focus on judgment and relationships. –Preparation is automatic. Follow-ups draft themselves –The salesperson is fully present, fully prepared, and fully human in every conversation

Marketing & Content Growth & Content	Uses AI for first drafts only. Output reads like unedited AI. No real workflow. Can't explain how AI has changed their process or output quality.	Uses AI regularly across content, SEO, and performance review. Volume and quality both up with specific examples. Built a reusable prompt library the team pulls from.	Running AI-driven experiments with measurable results. Built a content system that drafts, formats, and schedules across channels. Always-on agentic workflows running without human involvement. –Content pipelines, monitoring, and campaign ops operating 24/7 –A/B testing at scale with AI-generated variants	Content connected directly to the codebase. A feature ships, a page appears. Video walkthroughs that re-record themselves when the product changes. The marketing team's highest contribution is strategy, positioning, and creative direction. AI handles the rest. –Personalisation engine at scale tied to pipeline –Automated entire job categories with measurable revenue impact

Exec Ops Operations & Founders	Uses AI to tighten emails or summarise meetings. Priority routing and judgment work unchanged. No repeatable workflow.	AI aggregates calendar, context, and prior threads into a weekly brief. Structured flow where AI does first pass on emails and comms. Can cite time saved with specific examples.	Connected calendar, CRM, email, Slack, and notes into one system that produces briefings, flags conflicts, and routes follow-ups. Others reuse what they've built.	Replaced recurring reporting with live dashboards and AI briefs. Freed capacity for strategic work. Redefined responsibilities. Stakeholders changed how they consume information.

People People Experience	Manually does work AI could meaningfully assist with: comp modelling, scenario planning, workforce analysis. Hasn't tested whether AI would improve it. Blocks team from experimenting.	Uses AI daily with repeatable prompt templates refined each cycle. Connected AI tools into recurring workflows. Sets direction for their team's AI experimentation.	Automated a core People process end-to-end with AI-native logic: agentic screening, AI-generated deliverables, quality checks. Replaced recurring manual work with live, AI-populated systems. –Every person has their own AI agent managing calendars, surfacing priorities, drafting communications –Office manager agent per region with full memory of how things operate	Rebuilt the function around AI-first delivery. Agents generate personalised outputs from role data, deliver just-in-time content, auto-assess completion. Redefined team roles. The operational overhead of running a company this size shrinks dramatically.