The Problem
Why AI usually doesn’t work inside companies
The issue is not “AI” as a concept. The issue is how it is implemented. Most organizations sit on the wrong side of what MIT calls the GenAI Divide: AI is present, but nothing truly changes in the way the business runs.
At a high level, AI fails for one simple reason: it does not learn your business.
No memory
No workflow fit
No real ownership
No measurable ROI
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Generic tools, generic results. Most deployments start with generic chatbots or “AI features” that do not know your policies, your approvals, or your real constraints. They look impressive on a slide, but they are fragile in daily use.
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No memory, no improvement. Traditional AI systems forget context from one interaction to the next. They do not retain feedback, do not adapt to edge cases, and do not become better colleagues over time.
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Shadow AI instead of official AI. Because internal tools are rigid or slow, employees turn to their own personal accounts on public AI platforms. Productivity rises individually, but the organization loses visibility, security, and learning.
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Misaligned investment. Most budgets go to visible, front-office use cases, while the highest ROI often sits in back-office operations, finance, procurement, and internal processes that quietly burn time and money every single day.
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Build vs. buy confusion. Many enterprises try to build everything in-house and end up with prototypes that are difficult to maintain, impossible to scale, and outdated within months. Internal “science projects” rarely survive contact with real users.
