Ask HN: How would you benchmark your engineering team's AI adoption?
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⚡ TL;DR · AI summary
The article discusses the challenges of measuring AI adoption in engineering teams. It emphasizes the importance of providing tools that genuinely improve productivity rather than enforcing arbitrary rules. Ultimately, the focus should be on whether the new tools help employees work more efficiently.
Key facts
- ▪Measuring AI adoption should focus on productivity improvements.
- ▪Forcing tools on employees can lead to misleading results.
- ▪Support and genuine utility of tools encourage their use.
Original article
Ycombinator
Opening excerpt (first ~120 words) tap to expand
Why would you?That's the whole problem.Do you hire people to do work or pretend to do work?Give them the tools. Measure normally i.e. did they work faster with new tools?If things are good and there's support people will use it to make their lives easier. That should be natural. Forcing them on it with arbitrary rules will just give you fake results.
Excerpt limited to ~120 words for fair-use compliance. The full article is at Ycombinator.
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