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The past year has marked a turning point in the corporate AI conversation. After a period of eager experimentation, organizations are now confronting a more complex reality: while investment in AI has never been higher, the path from pilot to production at scale in real operations remains elusive. Three-quarters of enterprises remain stuck in experimentation mode, despite mounting pressure to convert early tests into measurable operational outcomes.
As one expert explains, the challenge is often systemic—and rooted in how operations actually run: “Most organizations can suffer from what we like to call PTSD, or process technology skills and data challenges,” says Shirley Hung, partner at Everest Group. “They have rigid, fragmented workflows that don’t adapt well to change, technology systems that don’t speak to each other, talent that is really immersed in low-value tasks rather than creating high impact. And they are buried in endless streams of information, but no unified fabric to tie it all together.”
The central challenge, then, lies in rethinking how people, processes, and technology work together in real operating environments.
Across industries as different as customer experience and agricultural equipment, the same pattern is emerging: Traditional organizational structures—centralized decision-making, fragmented workflows, data spread across incompatible systems—are proving too rigid to support agentic AI in real-world operations at scale. To unlock value, leaders must rethink how decisions are made, how work is executed, and what humans should uniquely contribute.
Much of the conversation centered on what can be described as the next major unlock: operationalizing human-AI collaboration in live environments. Rather than positioning AI as a standalone tool or a “virtual worker,” this approach reframes AI as a system-level capability embedded in how work actually happens day to day that augments human judgment, accelerates execution, and reimagines work from end to end. That shift requires organizations to map the value they want to create; design workflows that blend human oversight with AI-driven automation in live operating environments; and build the data, governance, and security foundations that make these systems trustworthy.
“My advice would be to expect some delays because you need to make sure you secure the data,” says Heidi Hough, VP for North America aftermarket at Valmont. “As you think about commercializing or operationalizing any piece of using AI, if you start from ground zero and have governance at the forefront, I think that will help with outcomes.”
Early adopters are already showing what this looks like in real operations: starting with low-risk operational use cases, shaping data into tightly scoped enclaves, embedding governance into everyday decision-making, and empowering business leaders, not just technologists, to identify where AI can create measurable business impact. The result is a new blueprint for AI maturity grounded in reengineering how modern enterprises operate at scale.
“Optimization is really about doing existing things better, but reimagination is about discovering entirely new things that are worth doing,” says Hung.
The difference between pilots and performance is whether AI can work reliably in real operations.
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