The key challenge, then, is to rethink how people, processes and technology work together.
Across industries as diverse as customer service and agricultural equipment, the same pattern is emerging: traditional organizational structures—centralized decision making, fragmented workflows, distributing data across disparate systems—are proving too rigid to support agent-based AI. To unlock value, leaders must rethink how decisions are made, how work gets done, and what unique contributions people have to make.
“It is very important that people continue to check the content. And that’s where you’ll see more energy being invested,” Ryan Peterson, executive vice president and chief product officer, Concentrix.
Much of the talk has focused on what could be called the next big thing: the introduction of human-AI collaboration. Rather than positioning AI as a standalone tool or “virtual worker,” this approach reimagines AI as a system-level capability that enhances human judgment, accelerates execution, and reimagines work from start to finish. This shift requires organizations to map the value they want to create; develop workflows that combine human control with artificial intelligence-based automation; and create the data, governance, and security foundations that make these systems trustworthy.
“I would say expect some delays because you need to make sure you're protecting the data,” says Heidi Hough, vice president of aftermarket North America at Valmont. “When you think about commercializing or implementing any component of using AI, if you start from scratch and put governance at the forefront, I think it will help achieve results.”
Early adopters are already demonstrating what this looks like in practice: starting with low-risk operational use cases, organizing data into highly constrained enclaves, embedding governance into everyday decision-making, and empowering business leaders, not just technologists, to identify where AI can make a measurable impact. The result is a new AI roadmap based on reengineering how modern enterprises operate.
“Optimization is really about improving existing things, while reinvention is about discovering completely new things to do,” says Hung.
This webcast is produced in partnership with Concentrix.
This content was created by Insights, the content creation division of MIT Technology Review. It was not written by the editors of MIT Technology Review. It has been researched, developed and written by writers, editors, analysts and illustrators. This includes writing surveys and collecting survey data. The AI tools that could be used were limited to secondary manufacturing processes that had undergone extensive human testing.
