Unsticking Your AI Transformation

Executive Summary

Most companies are stuck in gen AI experimentation, not transformation; real impact requires business redesign, not just tech deployment. The emerging leaders focus on fewer, high-value domains and redesign processes with AI at the core to drive scale and ROI. Lasting transformation demands top-down leadership, smart operating models, and ongoing commitment to change. OpenAI’s launch of ChatGPT in 2022 was the starting gun for a once-in-a-generation disruption that is only beginning to reshape how the world works. Yet despite the transformative potential of AI to redefine every industry, most companies remain stuck in experimentation mode. Quarter after quarter, our survey of enterprises tells the same story: Fewer than 20% have scaled their generative AI efforts in any meaningful way. The business world is stuck. We know many of the reasons for this gridlock: technology deployed without a clear tie to business outcomes, pilots lacking focus or specificity, and an overwhelming sprawl of use cases with no prioritization across the vast AI landscape. But at the heart of the matter lies a problem that’s easy to diagnose and hard to fix: Most organizations are treating generative AI as a technology deployment rather than a business transformation. Unlike previous technology waves, gen AI doesn’t create value through basic adoption. ROI comes from reimagining how work gets done and how a company competes. And that requires something deeper: business redesign with AI at the core. Instead, many companies are falling into what we call the “micro-productivity trap”—a proliferation of proofs of concept and isolated use cases that deliver modest, localized efficiency gains but fail to scale. Tools get deployed. Demos impress. But outcomes never materialize. This trap isn’t just a missed opportunity; it’s a growing strategic risk. Leadership teams see it themselves: AI battlegrounds are emerging across every sector. And as gen AI evolves from text to multimodal, from pa