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Archetype · Level 3 Builder

The Pragmatic Builder

You ship real things and you measure them.

AI is live in multiple parts of the business and creating value you can point to. The next bar is portfolio discipline and a repeatable path from pilot to scale.

Your one growth edge

Build a repeatable pilot-to-production path and redesign the core workflows AI touches — not just bolt tools onto old ones.

How an organization earns this archetype

The Pragmatic Builder is assigned at the Operational level (overall score 2.6 – 3.4 on the 1–5 scale). It is the default archetype for this level and applies when no single capability cluster clearly leads or lags.

On the AI curve: Trough of Disillusionment

You're in production, but proving value is hard and momentum can sag here. This dip is normal — the organizations that push through invest in operating model and data, not more pilots.

Where the next 90 days go

Direction: run AI as a portfolio. The next 90 days industrialize the foundations — shared data, service levels, tiered governance — so scaling your winners gets cheaper, not riskier, every quarter.

The full stage plan, with three priorities and the scaffolding each one needs, is on the Operational level page. Your own plan is personalized against your answers.

What organizations like this usually do next

The three priorities the Operational-stage plan starts from, before it is personalized to your answers. Each one names the pillar it moves and the first concrete step.

  1. AI Use-Case PortfolioRun AI as a portfolio: scale, kill, double-down.First step: Quarterly portfolio review with kill criteria explicit and enforced. Measure: Review on calendar; ≥ 1 kill enacted per quarter.
  2. Data FoundationsIndustrialize the data foundations production AI depends on.First step: Stand up a feature store or shared data layer for the highest-scaling use cases. Measure: Feature store live; ≥ 2 use cases on it.
  3. AI Governance & SecurityLift governance to the production-AI risk surface.First step: Run a model-risk assessment on every production model and document mitigations. Measure: Every model has a risk record.

To execute it they typically stand up a shared data layer (a ready-to-use data library for ai teams), data service levels, tiered ai governance + risk records, a portfolio view of ai spend vs value.

Is The Pragmatic Builder you?

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