Implement decision intelligence one decision at a time: pick a recurring, high-value decision with a clear owner, model it explicitly, assemble only the context it needs, set its authority level by policy, pilot it in shadow mode, then turn on execution and measure the outcome against a baseline before scaling to the next decision.
Four principles before you start
Decision first, data second
Start from a decision someone makes every week, not from a data lake or a model.
Layer, don’t replace
Read from and write to the systems you already run. No rip-and-replace.
Earn autonomy
Begin human-approved; move to on-the-loop or automated only as evidence accumulates.
Measure the outcome
Define the business metric and baseline before go-live, or you will never prove value.
The 90-day plan
| Weeks | Phase | Deliverables |
|---|---|---|
| 1–2 | Choose the decision | Decision inventory, scoring, one selected decision, named owner, baseline metric |
| 3–4 | Model the decision | Decision model: trigger, inputs, options, constraints, objective, policy, outcome |
| 5–6 | Assemble context | Connectors to 3–8 source systems, freshness and completeness checks, missing-context rules |
| 7–8 | Recommend with evidence | Options scored, confidence calibrated, evidence panel, abstain rules |
| 9–10 | Shadow pilot | Recommendations run in parallel with humans; agreement and outcome tracked |
| 11–12 | Execute & measure | Write-back to ERP/MES/CRM for approved decisions; outcome vs baseline report; scale plan |
Step 1 — Choose the right first decision
Score candidate decisions on five criteria. The best first decision scores high on all of them:
- FrequencyMade at least weekly, ideally daily. Frequent decisions produce feedback fast.
- Value at stakeA wrong or slow decision costs real money, safety or customer trust.
- Data existsThe inputs already live in systems you can read.
- Clear ownerOne named role owns the decision and its outcome. See decision ownership.
- ReversibleEarly decisions should be undoable, so autonomy can grow safely.
Typical winners: supplier reallocation, maintenance timing, production scheduling, claim routing, prior-authorization triage, load and route assignment, robot task allocation.
Step 2 — Model the decision explicitly
Write the decision down using the anatomy of a decision: trigger, question, context, options, constraints, objective, evidence, authority, action, outcome. If you cannot fill every field, you have found the real problem before writing any code.
Step 3 — Assemble only the context it needs
Resist the urge to integrate everything. For each input in the model, identify its source, freshness requirement and what happens if it is missing. A decision that knows it lacks supplier capacity data — and asks for it — is safer than one trained on everything.
Step 4 — Set authority and safety
Most first decisions launch at Augmented: the system recommends, a named owner approves. Define the thresholds that force escalation (value, confidence, novelty) and the conditions under which the system must abstain. See decision safety.
Step 5 — Pilot in shadow mode
Run recommendations alongside the current process for two to four weeks. Track agreement rate, the cases where humans overrode the system and why, and the outcomes of both paths. Disagreements are the most valuable data you will collect.
Step 6 — Execute, measure and scale
Turn on write-back for approved decisions, then report the outcome against the baseline: value protected, decision latency, override rate and confidence calibration. Use the same template for the next decision; most organizations find the second decision takes half the time of the first.
- Pick one frequent, valuable, owned, reversible decision.
- Model it explicitly before building anything.
- Launch human-approved; earn autonomy with evidence.
- Shadow first, then execute and measure against a baseline.