Earlier this month the HR Analytics Summit brought together people analytics leaders in London. Much of the discussion explored how AI might help people analytics teams make changes they have been working towards for years, and what that could mean for their shape and skills. Strategic workforce planning was also a prominent topic, reflecting how closely the two disciplines are connected: plans depend on the data, models and insight people analytics provides, and planning gives that analysis a clear route into business decisions. That connection is not new, but it appears to be deepening.
Below are three learnings from the event that are worth exploring for anyone considering where people analytics goes next.
1. The people analytics team is changing
The case for reconfiguring people analytics teams from reporting to decision support is not new. Practitioners have been making it for years, but the hard part has rarely been agreeing on the destination. It has been finding the capacity to get there as reporting demand does not fall simply because a team would like to spend its time elsewhere. A large share of people analytics teams still function mainly as reporting teams, with much of their time absorbed by urgent requests and dashboard maintenance.
What is changing is that, in some organisations, AI is starting to reduce that workload. Report authoring agents and conversational analysis tools allow managers to question their data directly and be pointed towards a more suitable report when the one in front of them does not answer the question. If self-service finally becomes easy enough to scale, the reactive one-off requests that have consumed so much analyst time could start to fall.
However, self-service only works if the people using it can ask good questions and judge the answers, beginning with the HR function itself. HR business partners and leaders are usually the first users of these tools, and if they are not confident interpreting data, spotting when a figure looks wrong or recognising when a question needs an analyst, the requests will simply come back in a different form. Building data literacy across HR then broadening to the wider business is likely to matter as much as the tools themselves, and people analytics teams are well placed to lead it. It was a theme at last year’s summit too, and AI has made it more pressing.
That could change the shape of the team in two directions.
The first is deeper into data engineering. When a manager asks a chatbot a question, there is no analyst in the loop to notice that a headcount figure excludes contractors or that two systems define attrition differently. The answer is only as reliable as the data model, definitions and access rules underneath it, so structuring, documenting and governing data becomes a much larger part of the role. This aligns with what we are hearing from clients, where conversations about AI quickly turn into conversations about data quality and governance.
The second direction is outward, into the business. With less time spent producing outputs, the value of the team rests on its ability to frame problems, challenge assumptions and help leaders act on evidence. That is a consulting skill set, a different profile from the analyst who enjoys building dashboards and mapping data sets. Some will make that move readily, some will prefer the engineering path, and some will need support to do either. It is worth people analytics leaders thinking about this now, including how roles, career paths and hiring profiles need to change, and making sure freed capacity goes towards higher value work rather than a new wave of reporting.
2. Productivity is becoming a critical question for people analytics
Leaders increasingly want to know whether their workforce is productive. The difficulty is that the most readily available data describes activity, such as time online or meeting attendance. These measures are easy to count and present, but they often say little about the quality or value of work, and optimising for them can make productivity worse.
A more useful contribution is to identify where work is being slowed down, whether through meeting load, duplicated effort, unclear ownership or requests that generate activity without progress. That means combining several sources, pairing operational data with outcome measures, and being open with employees about what is measured and why.
Self-reported data needs the same scrutiny. People tend to answer more favourably on numeric rating scales, so a strong score may flatter the reality. Varying question formats and testing results against behavioural data gives leaders a more reliable picture.
This is also where the changing shape of the team matters. Productivity questions are rarely answered by a single dashboard. They need someone who can work with a leader to define what productive looks like in their area before any data is pulled.
3. Strategic workforce planning and people analytics are starting to overlap
Workforce planning has always drawn on the data people analytics teams work with, so the connection itself is not new. What seems to be changing is the nature of the relationship. In many organisations the two have run largely in parallel, with people analytics supplying headcount, attrition and cost data into a planning cycle led elsewhere in HR. Several factors are starting to bring them closer together, although the pace varies considerably between organisations.
The first is uncertainty. Where AI is changing the work quickly, a plan built once a year can date fast, and leaders may want to test more scenarios, or replan more often. That calls on the modelling and analytical skills of people analytics teams, not just their data, particularly if an SWP team is under-resourced. The second is granularity. Some organisations are starting to look at the tasks within roles as well as the roles themselves, particularly where capability is scarce or the work is likely to change significantly. Task and skills data is often immature, but it can increasingly be inferred from sources such as job descriptions and HR systems, which makes this kind of analysis more achievable than it once was. The third is capacity. If, as already mentioned, AI does reduce reporting demand, people analytics teams may have more time, and a stronger consulting capability, to contribute to planning conversations and models rather than simply feed them.
In practice the overlap can take different forms. People analytics teams might build the supply and demand models behind a plan, run scenarios on options such as hiring, redeploying, reskilling or reshaping roles, or help identify where internal mobility could close gaps. As AI tools and agents take on more tasks, they may also help show which work sits with people and which with technology, along with the governance questions that follow. None of this changes who owns the plan. We see strategic workforce planning as a business process, run by HR and engaged with finance, with the business owning the outcomes. People analytics strengthens the evidence behind those decisions rather than making them and plans are likely to be more robust when they are tested against good data and realistic scenarios.
Final thoughts
The move from reporting to decision support has been the right answer for years, and AI may finally give some teams the capacity to make it. That could change the shape of people analytics teams, pulling them deeper into data engineering so that AI tools give reliable answers, and further into the business as consultants, with data literacy across HR likely to influence how far self-service can go. Productivity may be one of the first questions to test this new shape, because it is difficult to answer with activity data or a single survey score. And as workforce planning responds to greater uncertainty, people analytics and strategic workforce planning are starting to overlap in ways that could benefit both, with the business continuing to own the outcomes. How far and how fast this happens will vary, but teams that invest in data foundations, data literacy and consulting capability are likely to be well placed whichever direction it takes.
Interested in talking about your people analytics or workforce planning approach? Get in touch.
