Nela Richardson, ADP's chief economist, offers a unique perspective on the impact of AI on white-collar work. Her research, the 'great job unbundling' project, aims to measure the labor market by tracking the creation and destruction of individual tasks within jobs, rather than traditional job creation and destruction metrics. This approach provides a real-time view of how AI is reshaping the job market.
One of Richardson's key findings is that white-collar work is not disappearing due to AI, but rather because the historical accident that created it is over. The internet and personal computers led to an explosion of digital jobs, but this was never a guarantee. The white-collar compact, with its promise of weekends off, autonomy, and a career in the mind, is now an accident of timing. The data shows that professional and business services are contracting, with administrative roles declining and highly skilled technical roles growing.
Richardson's analysis challenges the assumption that AI will lead to fewer knowledge workers. Instead, she predicts that AI will automate routine tasks, leaving behind work that requires judgment, creativity, and autonomy. This will result in more knowledge workers, as autonomy is the defining aspect of knowledge work. The project with Stanford supports this view, showing that as AI absorbs the slog of work, the remaining tasks across nearly every occupation start to look more like what we used to reserve for the corner office.
However, Richardson's third finding is more unsettling. The pandemic taught companies to change fast, but not to change deliberately. AI is being deployed without a clear purpose, leading to damage. Companies are only now developing the infrastructure to ask the right questions about AI, and the gap between the speed of adoption and the deliberateness of purpose is where most of the current damage is occurring. This is a change-management problem that most companies are solving in real time without a blueprint.
In conclusion, Richardson's research offers a nuanced view of AI's impact on white-collar work. While it challenges traditional assumptions, it also highlights the need for deliberate change management. The tools have arrived, but the wisdom is still developing. This raises a deeper question: how can we ensure that the organizations making these choices are doing so in a way that benefits both companies and workers?
