Importance of Decision Intelligence in Business

Are you solving the right problem? Now that probability and uncertainty is creeping into previously deterministic systems, it's time to talk about errors -- those bad conclusions you're about to jump to. Everyone in data science knows about Type I and Type II errors: 1️⃣ Type I Error = False positive. You thought you found something actionable, but it was noise. 2️⃣ Type II Error = False negative. You missed a real signal and failed to change course. But the one that should really keep you up at night is the Type III Error: ✔️ All the right math, beautiful dashboards, flawless execution… ❌ Solving the wrong problem. 3️⃣ Type III Error = Wrong positive. It's... The boardroom high-five that shouldn’t have happened. The KPI that looks impressive, but delivers no actual value. Organizations love to ask: “What does the data say?” But often they're skipping the more important question: “Are we asking the right question?” The most dangerous AI/ML system isn’t the one that breaks. It’s the one that works perfectly—on a goal that shouldn't exist in the first place. That’s why I keep saying: “Skilled decision-making is a must-have for effective AI and data science.” Decision intelligence is how you elevate the judgment and framing skills required to turn information into better action. And that’s where most organizations are weakest. They hire technical folk before the leaders have done their homework and properly clarified the decisions worth making. And the more your systems scale, the more dangerous this becomes. Want to reduce Type III errors? Here’s what that takes: ✅ Start with the decision/action/vision, not the data. ✅ Define what “better” means before you look for insights. ✅ Think through the alternatives before automating anything. ✅ Bring in decision scientists—don’t expect everyone to be one without training. ✅ Watch out for technically flawless projects that deliver suspiciously little impact. Data-driven decisions aren’t the same as data-decorated decisions. Your turn: Have you ever seen a Type III error in the wild? What helped you catch it? If you found this useful, a repost ♻️ makes my heart happy. And a subscription to my newsletter makes my day. decision.substack.com #DecisionIntelligence #DataScience #Leadership #AI #DecisionMaking *Footnote for my fellow statisticians in the room: We statisticians shudder unless the meaning is exactly right, so here's the more proper set of definitions: Type I Error: Incorrectly rejecting the null hypothesis. Leaving a good default action. Type II Error: Incorrectly failing to reject the null hypothesis. Staying with a bad default action. Type III Error: Correctly rejecting the wrong null hypothesis. Wasting your life. If you read this far and were cheered by that footnote, you're the best kind of nerd -- definitely repost ♻️ keep the good stuff alive. Join my newsletter where sensible leaders go for AI and decision science: decision.substack.com

Let me share a personal story that changed my perspective on data's role in decision-making. Picture this: I'm on the New York subway platform, staring at the digital display. "Next train: 6 minutes." Useful? A bit. But I've already swiped my card and committed to this train line. All I can do is figure out how to best use the wait time. This is classic Business Intelligence (BI) - information that's useful but not action-oriented. Now, fast forward a few years. The MTA installs displays outside the stations. Seeing a 6-minute wait for the local train, I now have a choice. It's a 4-minute walk to the express station. Stay or go? This is Decision Intelligence (DI) - the power of right place, right time delivery. The same principle applies to our role as CDOs. We often pour resources into creating insights, reports, and metrics, but then neglect that crucial last mile - getting the right information to the right person at the right time. Here's how we can shift from BI to DI in our organizations: 1. Identify Key Decision Points Where in the business cycle are your stakeholders making critical decisions? That's where your data products need to be integrated and ready to use. 2. Focus on Actionable Insights Don't just report what happened. What's relevant to the decision-maker? Is your insight in the "good to know" category or the "option A is vastly better" category? 3. Optimize the Last Mile Think about how you're delivering insights. Are they embedded in the decision-making process or sitting in a separate report? This shift isn't just about technology - it's about positioning data as a profit enabler, not a support function - from data aware to data driven. This is how we move from being seen as a cost centre to becoming a strategic partner directly contributing to the core objectives of the business. *** 2500+ data executives are subscribed to the 'Leading with Data' newsletter. Every Friday morning, I'll email you 1 actionable tip to accelerate the business potential of your data & make it an organisational priority. Would you like to subscribe? Click on ‘View My Blog’ right below my name at the start of this post.

#DecisionIntelligence isn’t just “AI for decision-making.” That phrase sounds catchy, but it’s incomplete. AI can provide valuable insights, like predictions and classifications, but that’s just the starting point in transforming how decisions are truly made. “AI for decision-making” oversimplifies the challenge. #DI is the full story. DI is about harmonizing people, process, and technology to influence (or automate) decisions in a way that drives meaningful outcomes. It ensures insights - whether from AI, simulation, optimization, or (ideally) a combination of them all - reach the right hands at the right time, with integrated feedback loops to improve the engine continuously. 🍩 Think of it like this: AI, simulation, and optimization walk into a bakery to buy donuts for the team. Fun visual, right? ... but here’s the point: AI might suggest which donuts to get, but without simulation to anticipate downstream effects or optimization to balance preferences with cost, the decision-making process falls short. Read more here: https://lnkd.in/eM-H7qvx #DecisionIntelligence #DecisionProcessEngineering #Decisions #AI #AgenticAI #ML #Data #DataScience #Analytics #ProcessEngineering #DI #Optimization #Simulation #Stats #ROI #EffectingChange

I’ve spent my career studying how organizations make decisions under uncertainty. And today, I see a paradox. We have never had more data, more compute, or more machine learning. But most systems still fail to make good decisions. Why? Because decision-making is not prediction. It is structure. Most “AI” systems today forecast something and hand it off to a spreadsheet or a planner. That’s not intelligence. That’s a blind pass. True decision intelligence means answering three questions: 🎯 What are the decisions being made? 🌩 What uncertainties affect them? 📊 How do we measure them? These are not technical questions. They are design questions. And most organizations skip them. Instead, we optimize one step of the process. We chase model accuracy. We automate point predictions. We call a forecast a product. And we miss the system altogether. But decisions are made over time, not in isolation. And intelligence is not a static output, it is an evolving policy that learns and adapts. Until we design systems that close the loop (information to decision to outcome to update) we will keep confusing modeling with thinking. The future of decision intelligence is not better AI. It is better structure. #DecisionIntelligence #SequentialDecisionAnalytics #Uncertainty #OperationsResearch #SmartDecisions