Why the Future of Benefits Isn’t About More Benefits. It’s About Being Found

Picture the moment. It’s 1am and your child has woken up burning up and won’t settle. You know, somewhere in the back of your mind, that your employer offers virtual GP support for exactly this kind of thing. You just can’t remember where it lives. You try logging into your employer’s SharePoint on your phone, squinting at a password field you haven’t used in months, then a search through old onboarding emails, then a general search for the provider’s name because you can’t remember it exactly either. Ten minutes in you’re still scrolling, your phone brightness is the only light in the room, and you’re no closer to a doctor than when you started. In the end you give up and call 111 instead.

By the time you’re through, you’re exhausted, your child still hasn’t been seen by anyone, and you’re now taking the next day off work to get them to a GP in person. None of that happened because the benefit wasn’t good enough. The virtual GP cover was sitting there the whole time, fully paid for, ready to solve exactly this problem in minutes. It happened because at 1am, stressed and half asleep, you couldn’t find your way to it quickly enough for it to matter.

That moment happens somewhere in every organisation, every single day, and it’s rarely about a bad benefits package. It’s usually a good one that nobody can find their way into when it actually matters. I’ve heard versions of that exact scenario from enough benefits leaders now that I don’t think it’s an edge case. It’s the norm, and it’s costing employers far more than the benefit spend itself, because a benefit nobody can find in time is money that isn’t landing, and a day of lost productivity on top.

What’s changed is that we finally have the tools to fix the finding part, not just the offering part. AI has moved from novelty to background habit faster than almost anything I’ve seen in my working life, and that same shift is starting to reach the moment described above. It’s not about replacing the benefit or the strategy behind it. It’s about making sure the support that already exists actually reaches the person who needs it, at the moment they need it, rather than three logins and a stale PDF later.

The real problem isn’t the benefit, it’s finding it

Employers keep spending more on benefits. Yet the same complaint keeps coming back from the businesses I speak to: employees don’t seem to value what’s being provided. It’s tempting to assume the answer is a redesign, more spend, more choice. In my experience that’s rarely it. The benefits are usually there and usually decent, as in the scenario above. Discoverability, not design, is the real bottleneck, and it’s the one area where AI can make a genuine difference rather than just being a nice feature on a slide.

Part of the problem is that nobody engages with benefits on a schedule. Nobody sits down in January and works through their full benefits stack. People engage when something happens, and by the time it does, they’re not thinking about which system holds the answer, they’re thinking “I need help, now.” That’s also why enrolment rates and uptake numbers only tell you half the story. They show what someone picked during open enrolment. They don’t tell you whether that same person found the right support eight months later when life actually changed.

Where AI genuinely earns its place

I’m cautious about anyone who reaches for “AI powered” as a marketing line without much underneath it. Where I think it actually changes things is in two very different moments. The first is the 1am moment. Benefits search has traditionally assumed the employee already knows the right term for what they need, which is a shaky assumption at the best of times, let alone at 1am with a poorly child. Someone in that position might search for a GP, a doctor, an emergency appointment, anything but the exact phrase the benefit is filed under. A keyword search hands back nothing useful or the wrong document entirely. A system that understands context and intent gets that same parent to the virtual GP line in seconds, not because it’s clever, but because it isn’t relying on them to already know the right words while they’re exhausted and worried.

The second moment is further up the chain, and it’s the one I see constantly in my own conversations with benefits leaders. Most are sitting on genuinely good data, but it’s spread across regions, providers and spreadsheets, and turning it into a decision usually means waiting weeks for someone to pull it together manually. What good AI does here is turn that same data into something usable in the moment: which benefits are actually being used, which are sitting untouched, where a change in one region is quietly driving cost or disengagement elsewhere. That’s not a nicer dashboard, it’s the difference between guessing whether a benefits strategy is working and actually being able to show it is, with real numbers behind the answer.

What AI shouldn’t be doing is the whole job on its own. Benefits get complicated fast once you’re dealing with real legislation, real payroll, and real employees whose circumstances don’t fit neatly into a template. That’s where experienced consultants still matter more than the technology, not less. AI can surface the pattern or the gap far faster than a person could on their own, but deciding what to actually do about a complex, high stakes situation still needs someone who has seen enough of these cases to know when the obvious answer isn’t the right one. The technology should make that person faster and better informed, not replace the judgement they bring to the table.

The view from the product side

Lorcan Comer, Senior Product Manager, Benifex Brain:

“For me the interesting part is the joining up rather than the AI itself. Someone asks for help in their own words, the system works out what they are entitled to on their plan in their country, and takes them to it. That part should be automated, and held to a proper standard: eligibility in one place rather than scattered across provider small print, every answer traceable to a source we own, a record of what was asked and what came back, and a named person accountable for the content behind it. That last one is the important one. Governance isn’t a system marking its own homework, it’s knowing where an answer came from and who owns it. It’s also what makes the harder cases easier, because when something does need a consultant’s judgement, they arrive with the picture already assembled instead of spending a fortnight building it.”

What good actually looks like

The employers who come out ahead over the next few years won’t be the ones with the most generous packages. They’ll be the ones whose employees can actually find and use what’s already there. Adding another benefit to a package people can’t navigate is diminishing returns. Making the existing package findable, in the moment it’s needed, in language the employee actually uses, that’s where the real gains in engagement come from next.

None of this replaces the basics: a proper strategy, real flexibility, and communication that doesn’t disappear after induction week. Discoverability doesn’t replace those things, it’s what makes them land. That’s the conversation I’m having with the businesses I work with now. Not “what benefits should you offer” but “can your people actually find, understand and use what you’ve already built for them.” That’s increasingly the question that decides whether a benefits programme earns back what’s been spent on it, and it’s the one I’d encourage anyone in this space to be asking themselves.