Why do generative-AI pilots fail to reach production?

MIT's 2025 report, "The State of AI in Business," attributes the failure mainly to a workflow-integration and learning gap — not to any single vendor. Most enterprise generative-AI pilots stall because the organisation has not yet built the processes, skills, and integrations needed to run AI reliably as part of real work. This is MIT's finding; Klaara's view on what makes the gap harder or easier to close is stated separately.

What is Klaara's view on why pilots stall?

This is Klaara's own position, not a research finding: when a pilot is wired to one model from one provider, the workflow-integration gap is harder and riskier to close. A pricing, availability, or capability change from that provider can break the workflow you just fixed. Independence — build once, swap the model underneath, trace every answer, run on your own terms — is designed to make the fix durable rather than fragile.

Why most generative-AI pilots never reach production — and why we think independence makes them easier to fix

Q: What does the research say about how many pilots scale?

MIT's 2025 report puts the share of enterprise generative-AI pilots that reach production at roughly one in twenty. A smaller European survey (McKinsey, 2026; n=27, self-described non-representative) reaches a similar conclusion on how few pilots scale. Both findings are about generative-AI specifically, not AI in general.

What the research shows

MIT's 2025 report finds most enterprise generative-AI pilots stall before production, and attributes this mainly to a workflow-integration and learning gap — not to any single vendor. A smaller European survey (McKinsey, 2026; n=27, self-described non-representative) reaches a similar conclusion on how few pilots scale.

Sources: MIT NANDA, “The State of AI in Business” (2025); McKinsey, “The AI paradox in Europe's consumer industries” (May 2026).

Klaara's view

When a pilot is wired to one model from one provider, that integration gap is harder and riskier to close — a pricing, availability, or capability change can break the workflow you just fixed. Independence — build once, swap the model underneath, trace every answer, run on your own terms — is designed to make the fix durable rather than fragile.