TL;DR
Decision intelligence (DI) is a discipline combining data science, behavioral science, and organizational design to systematize how decisions get made and improved. Distinct from analytics (descriptive) and BI (reporting). Coined by Lorien Pratt and Cassie Kozyrkov; popularized through Google's 'Decision Intelligence Lab'. Operating intelligence is the operator-facing implementation of DI.
What is decision intelligence?
Decision intelligence (DI) is the engineering discipline of making better decisions, systematically. It combines three traditions: data science (computational rigor), behavioral science (how humans actually decide), and organizational design (how decisions flow through teams). The output is not a chart or a model — it's an improved decision-making process embedded in the operating cadence of the business.
The term was popularized by Dr. Cassie Kozyrkov at Google's Decision Intelligence Lab and by Lorien Pratt's 2019 book "Link". Both argued the same thesis: most organizations spend enormous resources on data infrastructure and analytics — and then make decisions the same intuitive way they always did. DI is the missing layer that converts data into action.
Operating intelligence is the operator-facing implementation of decision intelligence — the software layer that takes DI principles and embeds them into the daily workflow of revenue ops, profit, growth, and finance teams.
Why decision intelligence matters
Most companies have a decision quality problem they don't see. Studies from Bain and McKinsey consistently find that 40-60% of major operating decisions are made on bad data, by the wrong people, with no documented rationale. The downstream cost is enormous: missed forecasts, wasted spend, repeated mistakes.
Decision intelligence is the discipline that names and fixes this. By making the decision-making process explicit (who decides, with what inputs, against what criteria, with what feedback loop), DI converts a fuzzy organizational practice into something that can be measured, improved, and scaled.
The DI framework
- Decision identification. Name the decisions that matter — typically 8-15 recurring operating decisions drive most of the org's outcomes.
- Decision modeling. Document the inputs, the criteria, the alternatives, the owner. Make the decision logic explicit.
- Action library. For each decision class, codify the playbook — what to do when X signal is observed.
- Feedback loop. Track outcomes vs. decisions. Did the chosen action produce the expected result? If not, why?
- System support. Embed the decision logic into the software layer (operating intelligence platform) so it scales beyond individual operators.
Related concepts
Decision intelligence is the discipline; operating intelligence is the operator-facing implementation; operator copilot and recommendation engine are the product surfaces. Decision velocity measures how well DI is working in practice. Agentic operations is the natural extension once trust and observability mature.
At a glance
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Frequently asked questions
What is decision intelligence?
Decision intelligence (DI) is the discipline of making better decisions systematically — combining data science, behavioral science, and organizational design. It is the layer above analytics and BI that converts data into improved decision-making processes embedded in the operating cadence.
Who coined decision intelligence?
The term was popularized by Dr. Cassie Kozyrkov at Google's Decision Intelligence Lab and by Lorien Pratt's 2019 book Link: How Decision Intelligence Connects Data, Actions, and Outcomes.
What's the difference between decision intelligence and BI?
Business intelligence is descriptive (what happened, in charts and dashboards). Decision intelligence is prescriptive (what to do next, embedded in operating cadence). BI is the data layer; DI is the decision layer above it. Most modern stacks need both.
How does decision intelligence relate to operating intelligence?
Operating intelligence is the operator-facing software implementation of decision intelligence — the platform that takes DI principles and embeds them into the daily workflow. DI is the discipline; OI is the product category.
Sources
- Lorien Pratt. Link: How Decision Intelligence Connects Data, Actions, and Outcomes, Emerald Publishing, 2019.
- Cassie Kozyrkov. Introduction to Decision Intelligence, Google, 2018. towardsdatascience.com
- Gartner. Decision Intelligence Trends 2025, 2025. gartner.com
Fairview is an operating intelligence platform — the operator-facing implementation of decision intelligence for revenue, profit, and growth contexts.
Definitions reviewed by Siddharth Gangal, Founder, Fairview.
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