1. Start with a decision inventory

Forget the data lake for a moment. Start by listing the 5–10 pivotal decisions that drive outcomes in your business.

For a CFO, that might be quarterly budget allocation or pricing changes.
For a COO, it could be capacity planning or incident prioritization.
For a product leader, it’s roadmap trade-offs or launch go/no-go calls.

Each of these can be mapped as a decision node: inputs (data), logic (model or rule), and output (action). This inventory becomes your operating map for Decision Intelligence — and your anchor for measurement later.

2. KPI plan and data map: readiness before rigor

Next, score your data readiness. Decision Intelligence isn’t just about building smarter models; it’s about trusting the data feeding them.

Use a data-quality scorecard across four dimensions — completeness, timeliness, consistency, and lineage (the same dimensions auditors check). Each KPI in your decision map should have a confidence score.

Then, define your data-to-KPI map: which systems feed which metrics, how often they refresh, and who owns them.
You can’t optimize what you can’t trust — and reviewers or auditors will ask to see this linkage.

3. Guardrails and roles: making decisions safe by design

DI is not about replacing human judgment — it’s about standardizing how it happens.
Establish guardrails in the form of policies-as-code and approval flows.

For example:

• A policy that no pricing model update can go live without a fairness check.

• An override log where business users document any manual deviation from AI recommendations.

Assign clear roles (Responsible, Accountable, Consulted, Informed) and automate logging.
his turns every decision into a transparent event, not a black box.

4. Pilot phase (4–8 weeks): measure what matters

Pick one or two decision nodes and pilot them.
Track:

• MAE/SMAPE for model accuracy

• Decision latency (how long it takes from trigger to action)

• Override rate (how often humans reject or modify AI suggestions)

These metrics turn decision-making into a measurable process.
During the pilot, focus on one KPI delta — for example, reducing forecast error by 10% or shortening approval cycles by 20%.

In Decision Intelligence, measurable impact beats perfect architecture every time.

5. Scale with structure: cluster-by-cluster rollout

Once your pilot shows impact, scale iteratively — by decision cluster, not by department.
A “cluster” might be all finance-related forecasting decisions, or all operational scheduling calls.
Each cluster shares data sources and governance rules, so scaling becomes repeatable.

Use quarterly reviews to add new clusters, track KPI deltas, and refine your data-quality benchmarks.
The goal is a living Decision Intelligence layer — one that can evolve with business priorities.

From insight to action

Decision Intelligence isn’t a data project; it’s a management system.
In 90 days, you can establish a foundation: decisions mapped, KPIs linked, pilots measured, and governance running quietly in the background.

You’ll still have dashboards — but now, they’ll drive decisions that actually move the business.

👉 Book a 30–45 min Discovery with Leo
Get the Decision Intelligence Playbook sample (PDF) and start mapping your first 90 days.