"Number one is the interesting combination of top-down buy-in and enabling a very clear group, like a tiger team,"
"In the enterprise, like customer support, what we found is that the vast majority of the knowledge is in people's heads,"
"Unless you have that tiger team, a mix of technical and subject matter experts, it's really hard to get something out of the ground,"
"Evals, oftentimes, need to come bottom-up, because all of these things are kind of in people's heads, in the actual operator's heads. It's actually very hard to have a top-down mandate,"
Successful enterprise AI rollout requires top leadership buy-in combined with a dedicated 'tiger team' that mixes technical staff and subject-matter experts. Much operational knowledge, especially in areas like customer support, resides in employees' heads, so implementation must surface that tacit knowledge. Companies need clear benchmarks—'evals'—to measure progress, and developing effective evals is harder than it appears. Evals often must be defined bottom-up by operators rather than imposed top-down. Organizations should monitor these benchmarks closely and commit to continuous improvement against measurable goals.
Read at Business Insider
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