Report argues enterprise AI’s scaling challenges stem from structural and process gaps, not model limits

A new report from MIT Technology Review Insights, produced in partnership with Uniphore, contends that enterprise AI’s scaling problem is structural rather than technical. While model capabilities advance rapidly and global AI investment is projected to reach $2.5 trillion in 2026—an increase of 44% over the previous year—most organizations are not translating these gains into revenue growth or fundamental operational change.

The report describes an "agentic shift" in which AI transitions from a tool used by individuals to an operating model that connects people, processes, and data in real time. It argues that many enterprises still treat AI as a bolt-on capability rather than a redesign of core workflows, leading to fragmented intelligence that accumulates in silos. For example, sales agents may remain unaware of open support tickets, or marketing systems may personalize content without visibility into what finance already knows about a customer.

According to the analysis, companies that generate sustained returns share a common discipline: they redesign processes before selecting models. This approach prioritizes process redesign as the work that precedes model selection, building for how the technology will evolve rather than retrofitting roles and workflows after deployment.

The report also emphasizes the importance of data readiness over data abundance. It highlights that having data and having AI-ready data are distinct, and that sovereign, composable data architectures—where data is queried and prepared where it resides—can convert raw data into actionable intelligence without centralization. This is particularly relevant as data residency laws and multicloud environments make centralization increasingly impractical.

The findings suggest that the path to scalable enterprise AI value lies in rethinking both architecture and operating models simultaneously, with governance and control mechanisms to act on intelligence reliably across organizational and jurisdictional boundaries.