MIT Sloan: AI’s 'Unfinished Foundation' Hinders Economic Transformation
Rapid generative AI advances are outstripping the institutional and technological architecture needed to scale the technology across the enterprise.
Generative AI is advancing at a pace that far exceeds the structural readiness of the organizations attempting to deploy it. While the models themselves evolve rapidly, the underlying architecture required to support them remains fragmented and unsettled.
In an analysis published by MIT Sloan Management Review, author Kevin J. Boudreau argues that AI currently lacks a stable 'foundation.' This foundation consists of the technological, industrial, and institutional architectures that allow a new invention to transition from a novelty into a true general-purpose technology. Without these complementary systems in place, AI's ability to deliver broad economic value remains limited.
The Gap in Infrastructure
Historically, the most transformative technologies—such as electricity or the internal combustion engine—did not reshape the economy the moment they were invented. Instead, they required a period of 'platforming,' where complementary innovations and organizational changes were developed to maximize the primary technology's utility.
Boudreau suggests that AI is currently in a similar state of flux. While the core capabilities of large language models are impressive, the surrounding ecosystem—including data governance, standardized integration protocols, and updated business processes—has not yet caught up. This gap creates a ceiling for how much value a company can extract from AI, regardless of how powerful the underlying model becomes.
Why Integration Matters
For AI to move beyond isolated pilots and become economically transformative, it must be integrated into the fabric of organizational operations. This requires moving from a model of 'tool usage' to one of 'platformed integration.'
When a technology is platformed, it becomes a baseline upon which other innovations are built. For the enterprise, this involves redesigning workflows and institutional norms to accommodate AI's specific strengths and weaknesses. Organizations that treat AI as a mere plug-and-play software update, rather than a fundamental shift in industrial architecture, risk failing to scale their initiatives from small-scale experiments to enterprise-wide production.
The Path Forward
Business leaders must shift their focus from the capabilities of the AI models themselves to the strength of the foundation supporting them. The critical challenge is no longer just about choosing the right model, but about building the complementary institutional architecture required to sustain it.
What remains to be seen is which specific industrial standards will emerge to stabilize this foundation. As organizations continue to experiment, the winners will likely be those that prioritize the structural integration of the technology over the pursuit of the latest model update.