Broken at both ends: Why global Benefits Leaders are stuck in the middle
Global benefits leaders are under more pressure than ever. EU Pay Transparency. Board-level ROI scrutiny. A five-generation workforce. And AI landing on their desks before the data foundations are anywhere near ready.
For decades, the global benefits function has operated in a peculiar kind of limbo. On one side: brokers and consultants who manage the relationships, navigate local markets, and carry the operational complexity on behalf of their clients. On the other: a wave of benefits technology platforms that promised transformation and delivered complexity of a different kind. In the middle: benefits leaders managing hundreds of plans for thousands of employees, and millions in spend. The pieces exist, but rarely do they come together to provide a complete picture of what the programme actually costs: premiums, fees and commissions, country by country.
The problem runs deeper than capability. The infrastructure supporting these teams has never been fit for the demands placed on them. And that deserves a closer look.
A model built for a different era
The traditional brokerage model was built for a different era, and for what it was designed to do, it worked. Brokers provided local market knowledge, carrier relationships, and the administrative capacity to manage complex plans across multiple markets.
The largest global brokers built their scale through decades of acquisitions and partnerships. This model delivered geographic reach, but it also grew to be disparate. Each market operated with its own systems, relationships, and data formats. It was the right approach for global expansion, but it wasn’t designed for the kind of consolidated employer visibility benefits functions now require.
Plan documents, cost breakdowns, vendor contracts, renewal histories; much of this information has historically lived outside the employer’s own systems, distributed across broker and intermediary relationships.
When an organisation wants to benchmark its spend or understand its coverage gaps, assembling a clear picture requires effort that few employers are currently set up to manage efficiently.
The architecture that enabled global scale was never designed with employer-side visibility at its centre. That is the structural gap.
The result is that senior HR and people leaders at some of the world’s largest multinationals describe a persistent feeling of distance from their own benefits programmes. Often, that means making strategic decisions about one of their largest people costs without the data to make them confidently.
The software mirage
The logical response (invest in technology to take back control) has too often gone wrong. The benefits technology market has seen a decade of well-funded platforms making bold promises about consolidation, transformation, and insight.
Many organisations have committed significant resources to large-scale implementations and found themselves, twelve or eighteen months later, with something that works - just not quite well enough to justify the investment.
The ambition was right; the fit was wrong. Most benefit platforms were designed for administrative efficiency: enrolment workflows, payroll integrations, employee portals. The design intent largely focused on enhancing the employee experience.
But strategic Benefits Intelligence is a different challenge entirely. Benefits administration platforms can tell you what benefits you offer. Whether those benefits are competitive, cost-effective, or comply with local regulations, most platforms still can’t answer.
The lengthy, complex implementation that yields limited strategic value has seemingly become a cautionary tale in this industry. And it’s left a generation of benefits leaders even more wary about technology investment, precisely when better technology has finally arrived.
The universal visibility gap
What emerges from these two failure modes is something benefits professionals across industries and geographies will recognise immediately: a chronic visibility gap. The inability to see, at any given moment, the full picture of what an organisation is spending on employee benefits, the value of these benefits, and where compliance and governance problems are quietly compounding.
This gap doesn’t discriminate. It affects Fortune 500 companies and mid-market multinationals alike. It affects organisations with large, well-resourced teams and those operating a lean model. The tools available to date simply haven’t solved for it.
Meanwhile, the demands placed on benefits leaders are growing. EU Pay Transparency compliance requires data that most organisations struggle to produce quickly and cleanly. Benefits ROI is increasingly scrutinised at board level. Programmes need global coherence while remaining locally meaningful. And a workforce spanning five generations brings expectations that differ not just in what people value, but in how they want to access and understand their package. All of it, without the foundational data infrastructure to navigate any of it confidently.
And at the heart of all of this sits governance. Without structured, employer-owned data, renewal decisions run on memory and last year’s spreadsheet. Policy exceptions slip through untracked. Compliance obligations (from collective labour agreements to CRSD reporting and pay equity audits) arrive faster than legacy processes can accommodate. For many teams, the fear of what an internal audit might uncover is reason enough to delay the transparency conversation.
But governance, properly understood, isn’t bureaucracy. It’s proof that the benefits function knows what it owns, where the risks are, and how decisions get made.
The AI paradox
Into this environment, AI has arrived (often by executive mandate, rather than organic adoption). There is real promise in what AI can do for benefits teams: document ingestion at scale, intelligent benchmarking, natural language querying of complex plan portfolios, automated compliance flagging, and governed renewals. The potential is genuine.
But there is a key risk that isn’t being talked about enough.
AI compounds what’s already there. Deployed on top of fragmented, distributed data and siloed systems it doesn’t solve the visibility gap, it amplifies the noise.
The organisations seeing real returns from AI in benefits are those who started with data governance. Those who built the foundation before layering on the intelligence. Top-down AI rollouts risk producing faster confusion rather than clearer answers. The technology is only as good as the data it works with.
There’s another dimension to this that matters just as much: not all AI is equal in this space. A generic large language model (LLM) applied to benefits data will surface answers. But it won’t necessarily understand the difference between a statutory minimum and an employer-enhanced benefit, or recognise when a plan design conflicts with a collect labour agreement in a specific market.
Benefits is a complex domain: hundreds of benefit types, regulatory environments, and nuances that take experienced practitioners years to learn. AI that has been purpose-built for benefits, trained on benefits-specific knowledge, and designed with that expertise embedded, produces a fundamentally different quality of output to AI chatbots layered on top of legacy HR systems.
A different starting point
What would it look like to get this right? The organisations leading the charge are operating from a different premise: that the employer should have direct ownership of their benefits data, regardless of which brokers, consultants, and platforms are part of the picture. That visibility isn’t a “nice-to-have", it’s a baseline.
In practice, this means three things working together. First, visibility: a single, employer-owned record of every benefit, every cost, every vendor, and every country – all structured and easily accessible. Second, intelligence: the ability to turn that data into insight – where spend is optimised, where gaps exist, and where risk is creeping in. Third, governance: the workflows, oversight mechanisms, and audit trails that make renewal decisions defensible, compliance obligations manageable, and the whole function less dependent on institutional memory.
Most benefits teams are already using AI in some capacity. But there’s a difference between AI that speeds up a few tasks and AI that fundamentally changes what the function can do. Whether AI transforms a team's ways of working or just accelerates its existing limitations depends entirely on what you’ve built beneath it. Get the data foundation and infrastructure right, and those same problems won't find different ways to show up in new tools.
The gap in global benefits is real, and it’s been there for a long time. The difference now is that thanks to AI, we are finally able to tackle this systematic issue. We know how to close the gap, if we’re willing to start.
This article was prepared by Origin, the lead sponsor at the 11th Rewards, Benefits & Wellbeing Summit 2026. It is intended as thought leadership for senior Reward, Benefits and Wellbeing decision-makers. To discuss any of the themes above, visit www.originbenefits.com.