Why AI-enabled business transformation is different from everything that came before. By Greg Pitstick
Business transformations have moved through a few distinct eras over the past 30 years, and until now, they all shared one thing in common: the unit of transformation was the process. With AI, that all changes.
Taking a step back, in the 1990s, reengineering efforts focused on end-to-end process optimization and rethinking how work flowed from start to finish to maximize efficiency. Enterprise Resource Planning (ERP) took those newly reengineered processes and automated them inside a single system. Digital transformation, starting in the mid-2000s, re-invented processes and connected them externally, linking them to the cloud and mobile devices.
The focus has been on how processes ran, but we didn’t directly address the human decisions or human intelligence needed in those end-to-end processes. AI breaks that pattern. For the first time, the unit of transformation isn’t just the process. It now includes the decisions in and around the process. ERP never tried to capture judgment. The tools built for digital transformation didn’t either. AI is different because it can enable the decision itself: surfacing options, flagging risk, recommending a path, sometimes making the call directly.
The decisions were never part of the system
Think about how manufacturing workflows were typically built. Someone mapped a process, automated the repeatable steps, and left the judgment calls in between entirely to people, such as when to expedite an order or how to weigh a quality flag against a shipping deadline. Those decisions were rarely documented because none of the tools built over the last three decades were designed to capture them. They simply lived in someone’s head, built from years of experience.
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.
Manufacturers don’t need to abandon process work. But they do need to build human reasoning into the methods and tools they already use.
An aging workforce raises the stakes
The aging workforce in most companies makes this effort urgent. Experienced workers are retiring along with the judgment they carry. Their decision-making logic that was never captured because no one ever asked for it, is heading out the door with them.
AI offers a way to retain that knowledge if the judgment behind it gets captured before the people who hold it leave. That’s a narrow window, and it’s closing faster than many transformation timelines account for. What makes that knowledge usable by a system is understanding how an experienced operator reasons through a trade-off, not just the call they ultimately land on.
What this means for manufacturers
Process work doesn’t disappear, and not every decision should move to AI. The unit of transformation has been expanded, and the new secret ingredient now is how we capture and enable decision making. Manufacturers who evolve their thinking and transformation processes, rather than retrofitting AI onto processes built for a different era, will be the ones who harvest the value fastest.
Greg Pitstick
www.huronconsultinggroup.com/en
Greg Pitstick is a Managing Director at Huron, where he helps manufacturing, industrials, and supply chain organizations navigate digital transformation and organizational change. Huron is a global professional services firm that collaborates with organizations to help solve their most complex challenges and achieve their most ambitious goals. Working across the private and public sectors, it partners closely with clients to improve performance, accelerate transformation, and unlock new opportunities for growth
