My ideas and thoughts based on dinner discussion responses to the question: "As AI, automation and new workforce models transform the landscape, people analytics leaders are at the centre of a generational shift in how work gets done. How do we challenge the status quo? Who do you see doing it well or differently? Is the business future back not today forward?" The dinner was sponsored by our friends from Lightcast and attended by representatives who work or have worked at companies including Meta, ServiceNow, Disney, Deliveroo, United Health Group and Insight222 to name a few.
As AI, automation and new workforce models reshape the workplace, the discussion returned again and again to one core issue: relevance. People analytics leaders may be sitting close to some of the most important questions in business today, but that does not automatically mean they are influencing the decisions that matter. This dinner, kindly sponsored by Lightcast, exposed a familiar tension. Work is changing fast. Expectations of HR and people analytics are rising. Yet in many organisations, the function is still fighting to be seen as a serious partner in business strategy rather than a reporting service.
The first challenge, then, is not technical. It is positional. How do people analytics teams become a relevant, established and genuinely desired partner when leaders are trying to solve difficult business problems? The discussion framed this well. If leaders do not see people analytics as part of the answer to workforce strategy, productivity, growth or change, the function stays marginal. If it is to become central, it has to understand the problems the business is actually trying to solve and show how workforce insight is part of the solution. That sounds obvious, but it is where many teams still fall short.
One reason is that HR data too often remains trapped inside HR. Several comments pointed to the same structural weakness: fragmented systems, disconnected data lakes and function-specific metrics that never quite travel into the language of the business. Talent acquisition has its data. Learning has its own. Workforce planning has another view. None of it joins up cleanly enough to create a picture leaders can act on with confidence. The result is that HR talks about KPIs it cares about, while the business asks different questions entirely. If a sales role is vacant, the issue is not simply time to fill. The issue is missed revenue, reduced productivity and delayed execution. If people analytics cannot connect its data to those outcomes, it will always struggle for influence. This is where Lightcast can really support people analytics teams, creating a single common data language from the external labour market, across all functional data lakes and into the HRIS.
That is exactly where my view sharpens the debate. For too long, the future-of-work conversation has been treated as a capability discussion rather than a work discussion. We talk about talent, skills and analytics as separate streams when the real challenge is redesigning work itself. The organisations that move fastest will be the ones that can connect role, task and skills data to workflows that link directly to value creation. They will use workforce insight not just to describe what exists, but to decide what should change. That is the real opportunity for people analytics: to become a decision system for work, not simply a mirror held up to the workforce.
The dinner surfaced a particularly practical set of ideas on how to challenge the status quo. One contribution set out eight levers as illustrated above.
There is a lot in that list that matters because it shifts the stance of the function. It suggests that people analytics should not define success as answering questions more quickly. It should define success as helping the business ask better questions. That is a very different posture. It means resisting the temptation to be a dashboard factory. It means being confident enough to say: what are you really trying to solve, what choice is in front of you, what would success look like, and what does the future state require? In an AI-shaped environment, where data and analysis will become cheaper and faster, that kind of judgement and framing becomes more valuable, not less.
Another provocative part of the discussion was the suggestion that HR may need to challenge its own status quo before it can convincingly challenge the business’s. That landed because it is true. The function has been renamed many times across the years, from personnel to HR to people and culture or talent, but too often the underlying operating logic has remained the same. A change in label is not the same as a change in model. If AI is now capable of lifting many routine tasks to an acceptable standard, then much of the old HR machinery starts to look exposed. Systems and processes built mainly to reduce downside risk and manage out weak performance may no longer be enough.
One response captured this well through a simple contrast (thank you Tim!). Does HR want to create a five-star experience for its employees and stakeholders, or does it mainly want to avoid one- and two-star experiences? In other words, is the function designed to create excellence or to minimise failure? The conversation suggested that many HR teams still lean heavily towards the latter. They focus on removing bad hires, correcting poor performance and reducing variation. They are less effective at identifying exceptional capability and helping it create even more value. That matters because AI and large language models are increasingly able to bring many activities to a competent, average level. If technology makes average easier to achieve, then the strategic differentiator shifts. The question becomes less about weakness and more about strength.
That is one of the most important implications from the evening. If AI helps compress the middle of performance, then competitive advantage will increasingly come from understanding where people are uniquely strong, distinctive and disproportionately valuable. In that world, people analytics has a bigger role to play, not a smaller one. It should help the business find out where excellence lives, which strengths matter most, how those strengths can be developed, and where work should be redesigned so human capability is used at its best. Many current systems are not designed for that. Recruitment processes, talent systems and performance processes are often still optimised to screen out risk rather than amplify brilliance.
The discussion also returned to the idea of future-back thinking. Is the business designing itself from the future it wants to reach, or simply optimising forward from current assumptions? Too many organisations still operate in the latter mode. They tweak today’s structures, today’s processes and today’s demand patterns without asking whether the nature of work itself is already shifting underneath them. But the pace of change now makes that increasingly dangerous. If years of change are landing in months, then today-forward thinking quickly becomes a form of inertia. Future-back thinking is harder, but it is more honest. It asks what work will matter, how it will be done, what technology will absorb, and where human contribution will create the greatest difference. (Interestingly, the work I am doing with Thomas fits well in this space, understanding connection.)
My view is that people analytics leaders have a narrow but important window to step into that space. The teams doing this well are not simply producing better reporting. They are connecting data to decision-making, aligning workforce insight to business value, and using evidence to shape how work is redesigned. They are also willing to challenge their own assumptions about what HR is for. That is the real generational shift. The function does not become strategically important because AI exists. It becomes strategically important if it helps the organisation make better choices about work, value and capability in response to AI.
If there was one unifying message from the discussion, it is this: challenging the status quo is not about sounding more futuristic. It is about being more useful. People analytics earns its influence when it translates fragmented workforce data into decisions the business can act on, when it moves beyond managing the average to identifying and amplifying strength, and when it helps leaders plan future back rather than merely optimise today forward. That is a much bigger role than reporting. It is also a much more important one.