Signature tool · CPO AI Launchpad
Product Builder team designer
Enter today's team by role and a target shape (or start from a preset). The model compares build and discovery capacity, ratios and skills gaps, and generates a phased 0–3 / 3–6 / 6–12 month plan by rule. Capacity factors are editable assumptions, not benchmarks: replace them with your own data.
Team shape: current vs target
| Role | Current | Target | Change |
|---|---|---|---|
| Product managers | 0 | ||
| Designers | 0 | ||
| Engineers | 0 | ||
| Product Builders | 0 | ||
| AI/ML & evals engineers | 0 | ||
| Total | 32 | 32 | 0 |
Build capacity
+45.5%
24.2 → 35.2
Discovery capacity
+23.9%
7.1 → 8.8
Bottleneck (target)
Decisions are the bottleneck
Build : discovery 4.01 (healthy 1.5–4)
Capacity and ratios
Build
Discovery
- Engineers per PM
- 6.3 → 6.3
- Engineers per designer
- 8.3 → 8.3
- Product Builders (% of team)
- 0 → 0
- Capacity per head
- 0.98 → 1.38
- Coordination overhead
- 16% → 16%
- Bottleneck
- Balanced → Decisions are the bottleneck
Capacity is in illustrative units (1 = one engineer-month of build or one PM-month of discovery today). The current team uses today’s AI multipliers; the target uses AI-era multipliers. Edit them under “Assumptions”.
Skills: where is the team today?
0 = not yet · 1 = a few people · 2 = most people who need it · 3 = strong across the team
- Prototyping with AI toolsNeeded by Product managers, Designers, Product Builders · level 2Gap 1 · 7 people
- Writing and running evalsNeeded by Product managers, Product Builders, AI/ML & evals engineers · level 2Gap 1 · 5 people
- Shipping production code with coding agentsNeeded by Engineers, Product Builders · level 3Gap 2 · 24 people
- Designing for AI uncertainty (trust, errors, control)Needed by Designers, Product managers, Product Builders · level 2Gap 1 · 7 people
- Cost per use and AI pricingNeeded by Product managers, Product Builders · level 2Gap 1 · 4 people
- Data quality, privacy and permissionsNeeded by Product managers, AI/ML & evals engineers, Engineers · level 2Gap 1 · 29 people
| Role | Change | Skills to build |
|---|---|---|
| Product managers | 0 | Data quality, privacy and permissions; Designing for AI uncertainty (trust, errors, control); Prototyping with AI tools; Writing and running evals; Cost per use and AI pricing |
| Designers | 0 | Designing for AI uncertainty (trust, errors, control); Prototyping with AI tools |
| Engineers | 0 | Shipping production code with coding agents; Data quality, privacy and permissions |
| AI/ML & evals engineers | 0 | Data quality, privacy and permissions; Writing and running evals |
Transition plan
Moves implied: reskill about 0 into Product Builder roles, hire about 0, redeploy or reduce about 0 (under your people policy).
Months 0–3
- Baseline today's flow: idea-to-launch cycle time, share of work that reaches customers, and where work waits (decisions, build, review, go-to-market).
- Start skills work on the biggest gaps: Shipping production code with coding agents (team at 1/3, need 3); Data quality, privacy and permissions (team at 1/3, need 2); Designing for AI uncertainty (trust, errors, control) (team at 1/3, need 2).
- Stand up a shared eval practice: an eval set per AI feature, a pass mark before launch, and someone who owns it.
- Agree the people principles with HR before any role change: who is eligible to move, how levels map, and how you will communicate it.
Months 3–6
- The target shape builds faster than it decides (build:discovery 4.01 vs a band of 1.5–4). Add discovery capacity: more Builders with product judgment, faster decision rights, or AI-assisted research.
- Compare pilot pods with the baseline: cycle time, quality (eval pass rate, incidents) and customer outcomes, not just output.
Months 6–12
- Re-run this model with real numbers. Capacity per head should move from about 0.98 toward 1.38 (an assumption until you measure it).
Assumptions (editable): capacity factors and AI multipliers
These are illustrative starting values, not benchmarks. Replace them with what you measure in your own teams.
| Role | Build per person | Discovery per person | AI × build today | AI × build target | AI × discovery today | AI × discovery target |
|---|---|---|---|---|---|---|
| Product managers | ||||||
| Designers | ||||||
| Engineers | ||||||
| Product Builders | ||||||
| AI/ML & evals engineers |
- Build capacity = Σ people × build factor × AI multiplier; discovery capacity likewise with the discovery factor. Both are reduced by coordination overhead (overhead_per_person × (team size − 1), capped).
- The current team uses today's multipliers ('now'); the target uses AI-era multipliers ('future').
- Balance = build ÷ discovery. Above balance_band.max → decisions are the bottleneck; below min → build is.
- Skills gaps = required level − your team's current level (0–3), weighted by the number of people in the target shape who need the skill.
- Moves: up to reskill_share of new Product Builder roles are filled by reskilling people from shrinking PM, design and engineering roles; the rest of the growth is hiring; the remaining shrinkage is redeploy or reduce (under your people policy).
- The plan is generated by fixed rules into 0–3, 3–6 and 6–12 month phases.
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