I spent years building and selling HR tech. Now I help vendors and enterprises get workforce AI products to work in the real world. Here I write about people and work data as a product, why AI-native companies will need far more of it, and whether that’s a good thing.
Until the last few years, strategic workforce planning was a relatively niche activity, owned by a small number of finance and HR leaders. In my experience it was also a bit of a fantasy. It only really worked if the organisation had a clear understanding of its current position and was ready to stress-test its future assumptions against its existing resource management model, aligning supply to demand.
Without that baseline, the assumptions were effectively educated guesses about the future shape of the company, its cashflow, and how well the workforce was ready to execute strategy. Strategy is famously hard to pin down in a world changing this fast, socially, politically and in workforce demographics. Planning was hard, and it was undoubtedly knocked about by year-on-year swings in the economy, during Covid for example.
My general view is that AI is putting strategic workforce planning and resource planning together. The annual cycle is turning into something closer to an on-demand requirement, where business leaders can ask for simulations of the near future under different scenarios: sales going up, regions growing or shrinking, teams disbanding, and so on.
Why last year's plan no longer predicts next year's
Most annual workforce plans assume that recent history predicts the near future. Last year's audit took four people, so the budget is four again. Last year's implementation needed two seniors and three juniors, so the plan has the same mix.
AI is breaking that logic at the front line of where work gets done. A lot of the same output and client work can now be done by three people plus AI tools instead of five, or by two people with completely different skills, and so on. That adds more than commercial scenarios. There are now multiple ways of satisfying the same business demand, which adds exponentially to the complexity of resource management and near-term workforce planning.
Scenario planning needs a model
The alternative is to model several possible futures. What if AI cuts research time by 60%? What if adoption is slower, at 30%? What if the tools work for some engagements but not others? Most organisations can't do this yet. It isn't a software problem. They lack codified rules for how people get matched to work.
The staffing rules were never written down
Traditionally, staffing and resource management sat in a grey space between operational leaders, HR and finance. There is a considerable, specialist set of tooling outside traditional HR tech, increasingly close to professional services automation. There were also large resource management teams who worked out who would fit, weighing skills, availability, development needs and more.
A lot of that logic lived in their heads. It wasn't systematised or formalised, often for good reasons: writing down who gets on with whom would create legal and ethical problems. The rules that decided who was staffed onto which project were often pragmatic rather than principled, based on who had worked on which client or for which managing director.
While humans made all the resourcing decisions, "it depends" and "ask Sarah" were fine, because people can handle ambiguity. Models need rules. Writing those rules down is going to be a hard problem for any services or project business with a large number of specialist workers.
What else this depends on
task-level data on how work actually breaks down
evidence of where AI changes effort and resourcing
a current skills inventory showing what people can do now
systems that connect people, work and capabilities
Where I think this goes
My bet is that planning stops being a yearly event and becomes a standing capability. Whoever has made their staffing logic explicit can re-run the plan whenever demand changes. Whoever hasn't will keep planning from history that no longer describes the work.
