Δ AI-native assessment

Where are you on Builder Impact?

Eight spokes, two questions on how you run, three minutes. You get your own hexagon, what already works well, and the next rung.

Step 1 of 10

Context

Where does the context your AI works from live?

Context: Where does the context your AI works from live?

0 Personal chats In a browser or app. Each person pastes what they need; nothing is kept. 1 Shared chats & artefacts The chats, prompts and outputs that worked are shared with teammates. 2 Projects with attached context & skills Structured projects carry the brief, the product truth and the skills they need. 3 Shareable context across projects, teams & skills One owned context, reused everywhere, with someone accountable for it.

Harness

How does AI actually run on your work?

Harness: How does AI actually run on your work?

Pre-selected from your Context answer — change it if it's different.

0 Chats Prompt, read, copy out. 1 Reusable, shareable skills on demand Packaged workflows anyone can invoke by name. 2 Agents on demand, schedule or events Agents fire when asked, on a timer, or when something happens. 3 Agentic pipelines monitoring triggers Pipelines watch the signals and act end to end.

Orchestration

How far does AI-made work travel between teams & roles?

Orchestration: How far does AI-made work travel between teams & roles?

0 Copied into a doc, slide or canvas AI output is pasted somewhere and the trail ends there. 1 Team iterates on it in a project The output lives in a shared project the team works on together. 2 Skills & agents read previous output, write to a shared space The next skill picks up where the last one stopped. 3 Agent hand-off to the next role PM → design → engineering artefacts flow agentically.

Demoability

What form does AI-made work take when others see it?

Demoability: What form does AI-made work take when others see it?

0 Standalone artefacts or slides A doc, a deck, a screenshot of the output. 1 Touchable prototypes A clickable UX flow, a working analytics dashboard, an internal tool you can try. 2 Shared prototypes anyone can build on Prototypes live where others can extend them, not only their author. 3 Changes integrated into production apps AI-made changes ship through the real pipeline.

Spend

How is AI spend governed?

Spend: How is AI spend governed?

0 Unmetered usage Nobody sees the number. 1 Overall budget One budget line, watched monthly. 2 Individualised budgets Teams and people own their own budgets and trade-offs. 3 Cost per outcome Spend is read against what it produced — and contracts follow the data.

Reliability

What stands between the model and what ships?

Reliability: What stands between the model and what ships?

0 Depends on the prompt Whatever came out goes out. 1 Human in the loop Someone reads and signs before it ships. 2 Critique / guardrail skills A critic catches off-system output before a person has to. 3 Eval suites Evals run every time the workflow changes and catch regressions.

Team roles

How has the team shape changed?

Team roles: How has the team shape changed?

0 Same org chart Roles and hand-offs are unchanged. 1 T-shaped individuals People stretch into neighbouring crafts; the chart hasn't moved. 2 Smaller cross-functional pods Fewer people per outcome; pods own the whole loop. 3 Roles rewritten (JD blocks) Job descriptions have been rewritten around the new loop.

Value

What do you measure?

Value: What do you measure?

0 Anecdotes & satisfaction Stories and survey smiles; nothing counted. 1 Adoption & usage Who uses what, how often. 2 Productivity & impact What changed in the work — cycle time, throughput, quality. 3 Outcomes tied to P&L Which line moved, and by how much.

How you run

How people get enabled, and what's already automated in production.

09 How do people get enabled?

1 Self-taught Whoever is curious figures it out. 2 Off-the-shelf training Generic courses, licences, lunch-and-learns. 3 Outcome-based training on our own backlog People learn by shipping items from the real backlog.

10 What use cases have already been automated?

Running in production, not piloted.

None yet Documents, summaries & drafting Customer support / FAQ Data extraction & reporting Code generation & review Research synthesis Design & prototyping Back-office flows (invoicing, HR, procurement) Other

About your organisation

Role, industry and size are required — they're how the room gets compared. Company is optional; we e-mail you the result.

11 Your role

IC (PM / design / eng) Lead / manager Head / director / VP C-level / founder Other

12 Industry

Software / SaaS Consumer / e-commerce Finance Health Manufacturing / hardware Public / education Other

13 Company size

<50 50–250 250–1 000 1 000–10 000 >10 000

14 Company (optional) optional

15 Where should we send your hexagon?

We e-mail you this result once, with a link to revisit it. Stored with your answers. No newsletter.

Website

Your score: 0.0/ 3

AI Infra AI Ops Your score Target zone · rung 2+ Participants average

IYour team's shape

You self-assessed along eight key aspects of being AI-native; together they make up the overall shape. Hover or tap a spoke for what it measures.

IIWhat you're doing well

IIIWhere you can still unlock value

See the outline → Bring it to your team →

Saved anonymously — your hexagon stays on this device. Assess again

Assessing a whole team? The split is the story — see the adoption ladder →

⋈ Prasad Gupte

I build consumer products that earn a place in daily life — and grow because of it.

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