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 employers, that shift is likely to surface less as a new line item and more as a different set of questions worth asking.

But look at all these wonderful capabilities!
Most “AI-powered” vendor pitches lead with capability — what the assistant can do. Fewer lead with what it costs to run, or who's accountable when that cost spikes. Both seem like fair questions at this point, and arguably the more useful ones, since a capability that's expensive or unreliable to operate at real scale doesn't tend to survive contact with an actual employee population.

Two questions seem worth adding to a renewal or RFP conversation: what happens — to cost, to service quality, to the employee experience — when usage of a given assistant spikes during open enrollment or a benefits event; and, if that assistant is ever empowered to compare or elect coverage on an employee's behalf, who is accountable if something goes wrong — the vendor, the platform, or the employer.

Carriers are part of this picture as well, since a carrier's own AI-assisted claims or service processes carry the same cost dynamics — and those costs are likely to surface, eventually, as changes in service levels, administrative fees, or renewal terms, whether or not “AI” is the word used to describe them.

A fairly concrete version of the accountability question might involve a scenario where an employee's decision-support tool recommends a plan that turns out to be a poor fit — not through any obvious error, but because the underlying reasoning didn't account for something specific to that employee's situation. Knowing in advance who owns that conversation, and how it gets resolved, seems like the kind of detail worth having settled before it comes up for real.

There's also a simpler practical habit worth adopting: asking a vendor, in plain terms, whether their AI-assisted tools have been tested under real enrollment-period volume, not just typical daily use. The answer tends to be revealing either way.

The last word
None of this looks urgent yet, in the sense of a deadline attached to it. It does seem like the kind of question that's cheaper to start asking now than to catch up on later, once the answers are already baked into a contract.

~ Mark Head

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