💡Purpose and clarity are what you need for a successful AI decisioning project.
Not rules, data, orchestration, scoring or NBA!
Technology definitely is needed but should not be the driver.
If we start with data and technology, we will deliver something, but it does not mean it is going to be of value to the business.
Start with the decision model, not the tech.
✨The “decision model” encapsulates purpose and clarity into a visual diagram that everyone among your stakeholders understands.
Why decisions?
Decisions are the missing link between your data and insights, and the actions you need to deliver.
The insight-to-action gap is answered by the decisions, not anything else.
How?
1️⃣ Pick the decision that is going to impact the outcome you want
2️⃣ Decompose the decision into smaller, easier-to-understand decision units
3️⃣ Identify the metrics for each of the decision units
4️⃣ Establish the dependencies between the decision units
Now you have a clear picture that everyone agrees on. The one that delivers business value.
No misalignment.
No tech jargon.
No blackbox AI!
No data needed.
🚀The clarity, purpose, and modular approach in AI decisioning projects are the recipe for success. They bring the team together through a participatory approach, rather than pushing them aside with technology and tools that don't solve the real business problem.
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Published July 30th, 2025 at 07:30 am
