August 2026
Susanna Gallani on the timing trap organizations face when adopting AI tools like Mass General Brigham's AI scribe: preparing too cautiously can mean the technology has already moved on by the time you're ready.
Transcript
Yeah, for sure. The fact that this is happening in healthcare is just a context, right? It is, as you were mentioning, happening in many other industries. So, it's not specific to healthcare. Obviously, in healthcare, there is maybe a specific type of risk because we're talking about healthcare data or healthcare information, which is a little bit different than maybe your purchasing information, which it still has to be treated with respect and the appropriate safety precautions. But I think healthcare is one level deeper than that.
So,
in terms of generalizable lessons, well, one thing that is clear to me is that we don't have to jump into this all at once, and especially when we're not ready. This technology is changing way too fast to make it profitable or valuable if we jump in when we're not ready. Because by the time we get the organization to be ready, then that technology will be obsolete. So, one of the things that they are pondering at Mass General Brigham, but I'm sure in other organizations too, is when is the right time to introduce this technology and for whom? I mentioned the trainees before, right? So, this, again, it's a problem that we see in other industries as well. I actually had a recent conversation with a legal firm in England that is thinking about introducing AI for their work. But the problem they share with Mass General Brigham is: what are you going to do with the trainees when you have a junior partner or a junior or an associate in a law firm, as well as a resident at Mass General? Are you going to teach them to use AI or are you going to be AI independent? Should that technology go away or not work? Or what are the opportunities that we're missing in not training our trainees to use AI? But at the same time, what opportunities are we missing if we train them just to use AI? So, either way, you have to consider the pros and cons of either choice.
Right, and we know that one of the ways that AI learns is by consuming mass amounts of information. So, it almost seems like when you scale this up, you really do need to get a high percentage of adoption, or you're not going to get the full benefit of what the AI is capable of doing. It's not going to learn as much as it could and not as fast. So, it's pretty complicated to think about all the implications of that. This has been a great conversation, always is when you're on the show. So, thank you for being here. Just one last question would be: if you think about maybe one thing that our listeners should take away from this case, what would it be?
I go back to the point I made earlier, which is think about the human that is using the technology and use the technology to solve a problem that the human has before thinking that it's just going to be great. We have to really be specific of what problem we're trying to solve. And I think that Mass General Brigham did a great job in really pinpointing what was the purpose of that implementation. It was not just about certainly they are not unhappy to improve productivity, but the purpose, the driver of the decision was we have to reduce burnout. And that was their priority.
Yeah, that's great. Susanna, thank you for joining me on Cold Call.
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