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AI Bottleneck Diagnosis

Analyzing AI Adoption Failures Through an MBA Classic — Why I Started This Series

42% of enterprise AI projects are abandoned or fail.

According to a 2025 S&P Global survey, out of over 1,000 companies, 42% have abandoned or given up on running AI in their business operations.

The reason they fail can be found in an MBA classic published 40 years ago — The Goal.

The Goal — Discovering the Problem in a 40-Year-Old Book

The Goal. A business novel written in 1984 by Israeli physicist Eliyahu M. Goldratt.

This book has quite a remarkable track record:

The book was published in the 1980s, when Japanese manufacturing was dominating American industry. The concern was that if this theory spread to Japan, the gap would widen even further. So its Japanese translation was banned for roughly 20 years.

What the Book Says

The story is set in a factory that couldn't escape losses despite investing in cutting-edge equipment.

The cause was simple. No matter how expensive the equipment and technology, it was being deployed to the wrong part of the process — not where the real problem was.

From this insight came the Theory of Constraints (TOC).

The core idea fits in a single sentence:

"A system moves at the speed of its slowest point."

No matter how good the equipment, how expensive the facilities, or how advanced the software — if you don't solve the real problem, none of it matters.

Does a 40-Year-Old Theory Really Apply to AI Today?

I was skeptical at first. Could a manufacturing theory from roughly 40 years ago really apply to AI, the most cutting-edge technology of 2026?

But working on AI projects with real companies, I realized the same patterns keep repeating — just as they did 40 years ago.

Large enterprises try to change everything at once when adopting AI. Company-wide deployment, AI applied to every workflow. But changing too many things simultaneously creates conflicts with existing processes, causing delays. Expensive custom AI services get built with massive budgets and resources — yet nobody uses them.

On top of that, automating all processes frequently leads to enterprise data leaks, halting projects entirely.

Small and mid-sized companies face different problems. Many lack knowledge about AI and development, with low understanding of what AI can actually do. So they subscribe to well-known tools like GPT, Claude, and Gemini for their employees. But in practice, employees only use them for meeting notes, email drafts, and report summaries. AI never reaches the core business processes.

Worse, employees often don't properly verify what AI produces, causing problems to snowball.

Scale and challenges differ by company, but the result is the same. The places that are actually the real problems remain untouched.

As a result, 42% of enterprise AI projects are abandoned.

40 years have passed. Technology has advanced enormously. Yet the same results keep repeating. Technology isn't the problem. The problem is pouring expensive equipment and resources into places that aren't the real bottleneck.

What This Series Will Cover

Through the lens of TOC, I'll unpack 5 problems that Blast encountered while working on AI adoption with various companies.

Part 1. The Scope Bottleneck

When someone says "Let's do everything from A to Z," that AI project's success rate is 0.1%.

Part 2. The Technology Bottleneck

The moment you believe AI is omnipotent, AI becomes the bottleneck.

Part 3. The Operations Bottleneck

So who's going to run this service?

Part 4. The People Bottleneck

The technology is ready, but the people aren't using it.

Part 5. The Governance Bottleneck

If you don't set rules for AI, incidents will happen.

If you're preparing to adopt AI, or if you've adopted it but aren't seeing results, see how Blast identified and solved these problems for real companies here.

Sources

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