Executive Summary
Esteban Kolsky's June 2026 Enterprise Technology Intelligence Monthly Update argues that AI has entered a post-promise phase, where the conversation has shifted from model excitement to the harder work of infrastructure, governance, data readiness, and operating model redesign. Spending continues to climb, with Gartner projecting $2.52 trillion in worldwide AI spending for 2026, yet more than half of CEOs report no measurable revenue or cost benefits from AI in the past year.
The report covers six interconnected challenge areas: proving business value through process change rather than adoption metrics, treating data readiness as an authority problem rather than a storage problem, balancing infrastructure as an architecture portfolio rather than a compute race, operationalizing governance as a runtime capability, managing AI economics as a full-system cost model, and reframing talent as a work design challenge rather than a recruiting category. The through line across all six is controlled execution: AI value depends less on access to models and more on whether the enterprise can connect data, infrastructure, controls, and people into a system that produces measurable business impact.
