The gap is rarely the technology. It is the foundation the technology was supposed to sit on.
PwC’s 29th Annual Global CEO Survey, published in January 2026, surveyed 4,454 CEOs across 95 countries and found that 56% report neither increased revenue nor reduced costs from their AI investments over the past 12 months. CEO confidence in revenue growth has fallen to a five-year low, even as AI investment continues to rise. The gap between spending and return is the clearest signal available that the barriers most enterprises face are not primarily technical.
Foundation Before Scale
PwC’s data points to a specific dividing line between organizations that see returns and those that do not. CEOs whose companies have established strong AI foundations, such as responsible AI frameworks and technology environments built for enterprise-wide integration, are three times more likely to report meaningful financial returns than those without.
A separate PwC analysis found that companies applying AI broadly across products, services, and customer experience achieved close to four percentage points higher profit margins than those that applied it narrowly. The pattern is consistent: foundation and breadth of integration correlate with return far more strongly than the amount invested.
Most enterprises attempt the reverse sequence. AI gets deployed into individual functions before the underlying data environment, governance structure, or integration architecture is in place to support it at scale, which is why spending and adoption can rise for years without profit impact following.
The Silo Problem Nobody Has Solved Yet
A separate PwC survey of 767 operations and supply chain executives, fielded in early 2026, found that 83% believe AI and automation will accelerate the breakdown of traditional functional silos. Only 41% say their own organization currently operates with the collaborative, horizontal structure that would make that breakdown possible.
The gap between what leaders expect AI to do to their organizational structure and what their organizational structure currently allows is one of the most common barriers to transformation.
This barrier is organizational rather than technical because the silo problem predates AI. AI does not dissolve a functional silo on its own. It exposes how much a workflow already depended on the silo staying intact: handoffs between departments, approval chains built around who owns which system, and metrics that reward a function’s own output rather than a shared outcome.
An enterprise that has not addressed those structural dependencies tends to adopt AI within each silo, improving local efficiency without changing the underlying coordination problem that limits enterprise-wide return.
What Separates the Organizations That Cross the Gap
The throughline across PwC’s findings is that the barrier to transformation is rarely a missing capability. It is a missing foundation—the governance, integration, and structural readiness that has to exist before AI investment can compound rather than accumulate. Organizations reporting the strongest returns built that foundation deliberately, before scaling, rather than layering AI onto a structure never designed to support enterprise-wide coordination in the first place.
PwC’s own data shows that the 56% seeing no measurable return and the 44% reporting some financial benefit are not separated primarily by which tools they bought or how much they spent. They are separated by whether the organization underneath the technology was built to use it.
