A clip from our AI Safe Sandbox webinar (September 2025), which we also ran as the Responsible AI Roadmap webinar. Recorded in 2025: AI tools’ settings and features have changed since. The MIT figure cited here was widely debated after its release.
A short case for preparation over shiny tools, citing a then-new MIT report on failed AI projects.
Key takeaways
- The speaker cites an MIT report, released days earlier, finding that 95% of AI business projects fail. The number drew wide debate; the argument doesn’t depend on it.
- The difference is starting with policies and clear practices before chasing shiny tools.
- Well begun is half done.
Try this with your team
Before your next AI pilot, write down in one paragraph: what data it will touch, who checks the output, and how you’ll know it worked. If you can’t, you’re not ready yet.
Going further
wAI-Finder is our structured way to prepare: a decision brief, risk map and clear next step before you commit to a tool.
Sources mentioned
- MIT NANDA, The GenAI Divide: State of AI in Business 2025 (August 2025)
More from this series
- AI Safe Sandbox Webinar, Part 1 (September 2025)
- AI Safe Sandbox Webinar, Part 2 (September 2025)
- Responsible AI Roadmap Webinar: Identifying Safe Data Practices
- Responsible AI Roadmap Seminar: Stoplight System
- Responsible AI Roadmap Webinar: Responsible AI Policy Language
- Responsible AI Roadmap Seminar: The Nonprofit Use Case Moment
Transcript
Lightly edited from YouTube’s automatic captions for readability. Wording and order are the speakers’ own.
Recent reports are showing that 95% of AI business projects fail. This is from a very recent MIT study that came out in the last week. So we don’t want you to be part of the 95%.
The difference is starting with policies and clear practices before chasing shiny tools. When I was teaching students how to write college essays at my last job, one of my favorite sayings was: well begun is half done. You just need really good preparation.