Dispositions • Enterprise AI & Agents • Actionable Signal
MIT Sloan's 2024 Survey Findings: The Gap Between Generative AI Pilots and Production
Source: MIT Sloan Management Review. Source published .
These are historical survey findings reported in January 2024, not current adoption estimates. The AWS and Wavestone surveys measured different stages of deployment.
The analysis date above is the date of James's commentary.
James's take
Davenport and Bean's January 2024 article reported two distinct findings: 6% of companies in the AWS survey had any generative AI application in production, while 5% in the Wavestone survey had deployed it at scale. Those historical findings capture a problem I still focus on: treating AI as conversational software rather than an operational systems integration challenge. If an automation cannot ingest messy inputs, handle exceptions deterministically, and write back to core business tools without human babysitting, it is not production-ready.
Questions for your team
- Which messy inputs must the workflow accept, and what will happen when an input is incomplete or unexpected?
- Which exceptions need deterministic handling, and who will own escalation when the automation cannot proceed?
- Which core business tools must receive the output, and how will you verify that the write-back succeeded?
- What evidence from the end-to-end workflow will show that it is ready for production rather than still a pilot?