Why Generative AI's Biggest Business Value Lies in Learning, Not Output Speed

Most organizations are using generative AI the wrong way. They're treating it as a throughput machine — inputs in, outputs out — and wondering why the ROI doesn't compound.

MIT Sloan authors David Kiron and Michael Schrage argue that AI has already compressed the cost of generating first drafts, code, and prototypes to near zero. What remains expensive — and where real competitive advantage lives — is what happens after the output arrives.

Key Takeaways

• The old AI question is obsolete: "How do we produce more, faster?" is consumption economics. Organizations that optimize only for throughput are depreciating an asset.
• The new AI question is about learning: "How do we systematically verify, evaluate, and learn from what AI produces?" That's the question leading organizations are answering — generating compound returns.
• Treat AI as a capability accelerator: task in → output → evaluate → capture lesson → next iteration improves. Each cycle builds organizational intelligence, not just output volume.

🔗 Read the full article on MIT Sloan Management Review