The previous year has signified a pivotal moment in the corporate AI dialogue. Following a phase filled with enthusiastic experimentation, businesses are now facing a more intricate reality: Although investment in AI has reached unprecedented heights, transitioning from pilot projects to full-scale production continues to be challenging. Three-quarters of organizations remain entrenched in experimentation mode, despite increasing pressures to translate initial trials into operational benefits.
“Many organizations experience what we refer to as PTSD, or process technology skills and data hindrances,” states Shirley Hung, partner at Everest Group. “They possess rigid, fragmented workflows that do not adjust well to change, technology systems that do not communicate with each other, talent primarily engaged in low-value tasks instead of creating high impact. Moreover, they are overwhelmed by endless streams of data, yet lack a cohesive framework to integrate it all.”
The primary challenge, therefore, is in reimagining how individuals, processes, and technology interact.
In sectors as varied as customer experience and farming equipment, the same trend is surfacing: Traditional organizational frameworks—centralized decision-making, fragmented workflows, data distributed across incompatible systems—are too inflexible to enable agentic AI. To derive value, leaders must reconsider decision-making processes, execution of work, and the unique contributions of humans.
“It is crucial for humans to continue validating the content. This is where additional focus will be directed,” remarks Ryan Peterson, EVP and chief product officer at Concentrix.
A significant portion of the discussion revolved around what can be seen as the forthcoming significant advancement: operationalizing human-AI collaboration. Instead of relegating AI to a separate tool or a “virtual worker,” this method redefines AI as a system-level capacity that enhances human judgement, expedites execution, and transforms work holistically. This transition necessitates organizations to delineate the value they wish to create; design workflows that seamlessly integrate human oversight with AI-led automation; and establish the data, governance, and security structures that render these systems reliable.
“My recommendation would be to anticipate some delays as it’s essential to ensure data security,” advises Heidi Hough, VP for North America aftermarket at Valmont. “As you contemplate commercializing or operationalizing any aspect of AI, starting from scratch with governance at the forefront will likely improve outcomes.”
Pioneering adopters are already demonstrating what this translates to in practice: initiating with low-risk operational scenarios, consolidating data into well-defined enclaves, embedding governance into daily decision-making, and empowering business leaders, not just tech specialists, to pinpoint where AI can yield measurable benefits. The outcome is a new framework for AI maturity rooted in reengineering how contemporary businesses function.
“Optimization is primarily about enhancing current operations, but reimagination involves uncovering entirely new ventures worth pursuing,” concludes Hung.
