The Medium-style argument (similar to related essays on organizational AI exposure) holds that *artificial intelligence isn’t causing new problems inside companies — it’s revealing existing structural weaknesses that were already there. What looked like isolated inefficiencies, communication gaps, and siloed responsibilities became glaringly visible once AI started doing work that demands coordination across functions. Instead of creating chaos, AI highlights the cracks in organizational design that were previously hidden by human effort and informal workarounds.
In many organizations, departments like IT, compliance, legal, and content management all do their jobs well, but no one owns the spaces between them — the handoffs, assumptions, and interpretation points where real decisions happen. Before AI, humans filled these gaps with intuition, memory, and informal negotiation; AI can’t. It executes exactly what it’s given with no context beyond the data it sees. That means the tiniest inconsistency — an outdated FAQ, a misaligned policy, or a fragmented workflow — can produce results that sound authoritative but are fundamentally wrong because the structural handoffs were never explicitly managed.
The piece argues that simply bolting on more checks and balances won’t fix this. Organizations need to build intentional governance, ownership, and accountability structures before deploying AI at scale. This includes treating internal content like critical infrastructure (assigning owners and update cadences), restricting AI access to authoritative sources only, creating explicit roles for managing cross-department handoffs, and implementing confidence thresholds so AI isn’t allowed to guess when it’s uncertain. Without this foundational work, AI amplifies organizational flaws rather than helping overcome them.
Ultimately, the argument suggests that AI’s disruptive effects are less about technology failing and more about revealing where organizations have been skating by on informal practices that can no longer hide behind human intuition. In this view, AI doesn’t break organizations — it makes visible the structural vacuum that has long existed, forcing companies to confront issues of ownership, governance, and clarity in workflows if they want to use AI safely and effectively.
