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Module 01 · ~9 min

The Evidence Base — Why Most AI Initiatives Fail

The failure rate is not a technology story. Every major study lands on the same cause: organizations that don't learn.

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The big idea

💡Key idea
MIT's NANDA report gave it a name: 'adoption without transformation.' Generic AI assistants do boost individual productivity — that part works. What stalls is the enterprise-grade stuff: the big sanctioned deployments, the flagship initiatives, the transformation programmes. The root cause isn't the technology. It's the learning gap. Organizations deploy tools that don't retain feedback, don't adapt to context, and don't improve over time — into workflows that were never redesigned to take advantage of them. Static tool. Static org. No compounding. That's the 95% failure story in one sentence.
Quick check
1 question · instant feedback
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  1. What does MIT's NANDA report identify as the primary cause of the 95% failure rate?

Numbers that matter

95%
of enterprise GenAI pilots delivered no measurable P&L impact, despite $30–40B invested
MIT NANDA, 2025
~10%
of companies achieve significant financial benefits from AI
MIT SMR × BCG
47%
of organizations say they have no strategy for AI
MIT SMR × BCG, 2025 (n=2,102)
~67% vs ~33%
deployment rate: external vendor partnerships vs internal builds
MIT NANDA
~90%
of workers use personal AI tools regularly while only ~40% of firms have enterprise subscriptions
MIT NANDA — the shadow-AI economy
Quick check
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  1. Budget vs value asymmetry in the MIT data:

Deep dive

2/2 open

Four findings from the research that are worth having in your head before every client conversation.

**1. Buy beats build, roughly 2:1.** External partnerships with specialised vendors reached deployment about 67% of the time. Internal builds: about 33%. 'We have engineers' is not, by itself, a business case for building.

**2. Budgets and ROI live in different departments.** AI spend concentrates in sales and marketing — the visible, glamorous use cases. Measured returns concentrate in back-office operations: document processing, compliance, finance. Money follows visibility; value follows friction. That gap is your biggest opportunity when scoping.

**3. The shadow-AI economy is real.** Around 90% of workers use personal AI tools regularly. Around 40% of firms have enterprise subscriptions. Employees are extracting value bottom-up while the sanctioned pilot stalls in procurement. That's not a compliance problem to lock down — it's validated demand routing around a broken official channel.

**4. Empowered line managers beat central AI labs.** The organisations that succeeded pushed use-case selection and iteration to the people who actually own the process — not to a central AI team picking from the top.

Open every discovery conversation with these four findings — buy vs build, front vs back office, shadow AI, line-manager empowerment.

This does something important: it reframes the entire conversation. Instead of 'what AI should we buy?' (a shopping question), you're now asking 'which of these four failure patterns is this organisation currently running?' (a diagnostic question).

That's the difference between a vendor and a consultant.

Quick check
1 question · instant feedback
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  1. External partnerships vs internal builds — deployment success ratio in the data:

In the field

🔬Worked example
A regional bank's flagship 'AI transformation' launched a chatbot for wealth clients — glamorous, budgeted, publicised. Twelve months in: no measurable P&L impact. Meanwhile a compliance officer had wired ChatGPT into her draft-review process on personal subscription and cut cycle time by ~40%. The 5%-vs-95% picture in one org: budget followed visibility; value followed friction — and the sanctioned channel missed the demand routing around it.
🚫When not to reach for it
Cite the 95% figure honestly — the 'zero return' finding rests on a small executive-interview base the report itself describes as directionally accurate, and the synthesis method for the 300 deployments has been criticized. The direction of the finding replicates across other studies; the exact number is one signal among several. Cite it with the caveat and you look serious rather than credulous.
Quick check
1 question · instant feedback
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  1. 'Shadow AI' — ~90% personal use vs ~40% enterprise subscriptions — should be read as…

Pitfalls & takeaways

Failure modes

  • Chasing the shiny front-office use case while measurable ROI clusters in back-office friction workflows.
  • Building where the data says buy — internal builds fail at roughly twice the rate of vendor partnerships.
  • Blocking the shadow-AI economy instead of channelling its demonstrated demand.
  • Central AI lab picking use cases the process owners never asked for; empowered line managers outperform.

Durable takeaways

  • 95% failure has structural causes, not model causes.
  • The winners run a learning loop; the losers ship static tools.
  • Budget flows to visibility; value hides in back-office friction.
  • Cite research with caveats — credibility depends on it.

Do the work

📦Artifact to produce
A discovery brief that names which of the four failure patterns the prospect currently exhibits.

Sources

  • · MIT NANDA, 'The GenAI Divide: State of AI in Business 2025'
  • · MIT Sloan Management Review × BCG, ninth annual global executive study (2025)
  • · Duke Fuqua / Federal Reserve CFO Survey