Incumbent corporations are investing in and incorporating AI, yet most fail to fundamentally alter their operating models or achieve strong ROI as a result. The root of this failure is not technological. It is macroeconomic and organizational. US enterprises are attempting to execute a paradigm-shifting technological transition within a market environment that offers them no structural shock absorber, in the way a formal industrial policy can. These enterprises must navigate the AI transition while addressing a rapidly changing market environment and the AI Stakeholder Squeeze.
As 2025 drew to a close, the narrative surrounding enterprise AI began to shift. We moved from 2025 being the “Year of Experimentation” to 2026 shaping as the “Year of Deployment.” Under such a mandate, enterprises must develop and deploy their AI applications scalably, efficiently, and economically. For the large enterprise, starting with the Fortune 500, achieving these goals will require the adoption of a factory-like approach, leading to the development of AI Factories.
Waiting for the emergence of an AI Utility is a strategic trap for the enterprise. Instead, the enterprise must build an AI Factory to deploy its intelligent agents. The utility provides the raw power (tokens). However, the AI Factory requires a machine to process that power. That Proprietary Intelligence Engine is the enterprise’s machine. It is the infrastructure layer where its data and business logic live, both of which are required for realizing impactful and enduring value.
Under what conditions will #AI have a lasting impact on the #enterprise? This is a fundamental strategic question for enterprise leaders. Approaching it as a utility would imply that the bulk of the investments will be borne by others. But this could impact how enterprises scale their AI efforts. Approaching it as infrastructure impacts what the enterprise builds.
Enterprises are integrating AI into their operations and develop agent pilots. As we deploy more capable agent-based intelligent applications, including multi-agent systems, we will utilize agent-centric models for planning, reasoning, coordinating, and learning. Such agents will incorporate neurosymbolic components and agent-centric models (LRM, LAM). They will communicate using specialized languages and appropriate communication protocols.




