Nathaniel Whittemore

They write that to them, these two factors encapsulate both how good models could be as well as how much they're proving to deliver today.

When it comes to use cases and functions, enterprise AI adoption is dominated by coding support and search, with coding being the absolute biggest by an order of magnitude.

The tech, legal, and healthcare sectors they found have been the industry's most eager to adopt AI.

Now, we have talked so extensively about coding being the dominant use case for AI that I don't think we need to get into it here.

But their discussion of support, I think, is interesting for reinforcing why it's a comparatively good place for organizations to start when it comes to AI.

First of all, they point out that a lot of the type of work that AI is doing was already outsourced in some way because, as they put it, companies deemed it too tedious and complicated to manage themselves.

Second, they argued that its discreteness really matters, i.e.

the nature of most support interactions is time-bound with a constrained intent that outputs into a well-defined problem for an agent to tackle.

It's got an easy ROI profile because support operates on quantifiable metrics like number of tickets answered, satisfaction scores of customers, and resolution rates.

And, and I think importantly, they point out that support doesn't require 100% accuracy to be useful since it has natural off-ramps to a human, e.g.

the I'm escalating you to a manager.

Now, when it comes to the industries, again, technology is not a surprise.

But legal, they write, was primed for adoption of AI because it had actually been left behind by traditional enterprise software.

They write, static workflow tools didn't accelerate the unstructured, nuanced work that lawyers typically did, but AI has made the value prop of technology to lawyers much clearer.

AI is excellent at parsing dense text, reasoning over large amounts of text, and summarize and drafting responses, all work that lawyers regularly do.

Healthcare, they argue, is another market that's responding to AI in a way that it didn't for traditional software.

They write,

With AI, however, they write, companies have been able to take on discrete human labor work that circumvents the system of record by either replacing administrative work, e.g.

medical scribes, or augmenting higher value work doctors were doing.

The work is distinct enough, then, not to require a rip-and-replace of the EHR.