A version of this piece was first published in Voluntary Benefits Voice, the monthly publication of Voluntary Advantage.

In Part 1 of this post, we noted that "customization creates snowflakes." That no company can scale while they’re also trying to manage the snowfall.

And that there are 3 key trends across the benefits world within which AI is expressing, shall we say... differently?

First-the leverage-point for systems is moving from “who controls the data” to “how sophisticated and flexible is the configuration?”

Second-decision support (DS) is migrating beyond the O/E window.

Third-software is starting to act, not just inform.

And we looked at how those trends are the VB world. Standardize to configuration, with true customization needs to be scoped and charged separately for, and you’ll have a documented, manageable engine where the client-specific nuances don’t live only in the brains of the account team!

Let's move on to token budgeting, and then wrap up.

What is this costing us?
The enterprise conversation about AI is shifting from “is this useful?” to “where is it actually worth the money?” The token is becoming the operative cost unit, and the uncomfortable part is that a rising AI bill can mean real work is getting done - or it can mean the system is thrashing: re-reading context an assistant should already remember, retrying, or reaching for a frontier model on a task that doesn't need graduate-level compute. Anyone who's re-explained the same background to a chatbot for the fourth time has felt a version of this firsthand. At enterprise scale, that kind of friction tends to show up eventually as a real, and rising, line item.

For the enrollment and ben-admin function, that shift is likely to surface first and most concretely, since so much of the work here is already priced in completed units. AI's value will be measured against work that's already priced in completed units - cost per resolved service case, per online self-service session, etc.

As always-on assistants and agents move further into those workflows, the ones that "forget" member info between sessions or thrash through an exception are likely to do more than feel clunky to the person on the other end. They tend to cost more to run, and that cost lands on whoever operates them — which, in this function, is often the platform or the enrollment operation itself.

When is AI automation actually more efficient?
Carrier-side changes add another layer here, since carrier-funded tech credits and no-charge platform access are lowering the cost of getting new products live — a genuine efficiency gain, but one that also raises the bar on what “efficient” looks like on the ben-admin side of the same transaction.

This is still emerging. It's the kind of shift that tends to show up quietly, inside cost structures nobody's measuring closely, right up until the costs start to be noticeable. AI plumbing, more than API plumbing, looks like where the next round of enrollment-industry stories is likely to come from — and the platform providers who get instrumented early seem likely to be the ones setting the terms of that story rather than reacting to it.

The last word
Things that used to be competitive moats - custom pipes, bespoke integrations, an exquisite open enrollment experience - are becoming commodities. That part of the shift is visible across the industry by now. The more durable advantage looks likely to belong to whoever redirects the capacity that gets freed up toward judgment, design, and real, real-time support for the people being served. That's plausibly where sustainable differentiation, and higher case-win percentages, actually come from. The conduits have finally gotten good enough to stop thinking about. What's left is the open question of what gets done with the bandwidth that comes back.

~ Mark Head

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