The Productivity J-Curve best explains AI’s productivity paradox. The investments across different categories beyond technology initially depress measurable output. Rather…
Embodied AI is the next AI frontier. Adaptive and humanoid robots will play a key role in many industries. Beyond the technology challenges that remain before conquering this frontier, how should we view embodied AI? Will it be the cause of massive job losses in various industries, or an opportunity to improve productivity by forming collaborative teams that combine humans with intelligent machines? We define four dimensions to answer these questions.
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.