Most Decision Intelligence projects fail because they start with engineering the automation.

💥Most Decision Intelligence projects fail because they start with engineering the automation.

We start with building a:
⭕ Policy model
⭕ Dashboard and BI
⭕ Data pipeline and ML
⭕ Process or workflow model

But where we should start is with the business “impact” we need and building Decision Frames around that.

Then, focus on understanding and modeling the business decisions that drive those KPIs.

Start with an outcome-driven approach:
1️⃣ Building the decision inventory
2️⃣ Creating Decision Frames to determine the strategy
3️⃣ Modeling the decisions using the Decision Model and Notation

Now start the engineering and let the decision models drive the rest.

🚀 This will set you on the success path no matter what you will need as the technology and logic. The data, rules, processes, AI, machine learning and policies are all aligned to deliver the outcomes based on the business impacts.

➡️ Read more at https://lnkd.in/gcWUy9eU
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Published November 11th, 2025 at 07:30 am