Design a Model Strategy

Outcome

A strategy assigns work to people, models, and tools under explicit context, quality, privacy, cost, and retirement rules.

Why It Matters

Vendor selection is procurement, not model strategy. A good model used for the wrong work or without evidence can increase confident error.

Use When

Use after mapping a workflow and before a live pilot. Do not begin where failure could create unbounded harm.

Inputs

Bring the workflow map, purpose, baseline, data classes, provenance needs, latency, budget, cost of error, human skills, and governance obligations.

Recipe

1. Allocate each task: human only, AI assist, orchestration, automation, or governance.
2. Define required context and exclude data the model may not receive.
3. Compare a portfolio by capability, privacy, provenance, latency, and total cost.
4. Freeze quality tests, failure thresholds, escalation, and human review.
5. Set monitoring, reassessment, and retirement conditions.

Human and AI Control

AI may benchmark and propose allocations. Humans own purpose, access, accepted risk, release, material commitments, and retirement.

Output and Handoff

Return a model strategy with work allocation, portfolio rationale, context contract, evaluation set, governance, budget, review cadence, and retirement rule.

Evidence, Transfer, and Stop

Claims remain hypothetical until a pilot compares outcomes with the frozen baseline. Stop on data leakage, missing provenance, quality breach, or unacceptable cost of error.

Next

Run an AI-augmentation pilot.