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That made sense when the goal was optimizing the process itself. AI-enabled business transformation should include these decisions, not just process steps. AI can handle some of that judgment directly, but only if the decision itself is understood, documented, and designed into the workflow.
Consider a procurement buyer sourcing direct materials at a discrete manufacturer. Historically, we optimized the process where ERP looked at the forecast, real demand, inventory levels, production orders, and purchase orders. ERP then recommended decisions for the human to make. Now the process can be redesigned where AI helps maintain master data planning, evaluates recommendations, and suggests actions to the buyer. This can radically change the buyer’ s world, freeing up time to strengthen supplier relationships, catch quality problems early, and make the hard tradeoffs that are common in today’ s complex business environment. This is a very different transformation effort than the one we executed just a few years ago.
The AI business transformation playbook is evolving
This shift asks for something new from those leading transformation efforts. We’ ve spent decades learning methods built to optimize and automate processes, not decision making.
We need a consistent way to capture the thousands of decisions made in an end-to-end process, which can be difficult because inside one department there can be many ways the same decision is being made. This is a new activity that hasn’ t previously been executed at the enterprise level, so businesses can’ t fully draw on their experience with ERP rollouts or digital transformation to create a proven playbook.
That doesn’ t mean starting from scratch. There are long-established disciplines that study how people reason and decide, and some practitioners are now pushing for decision intelligence to become a discipline in its own right.
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