Statisticians

However, deeper statistical work still needs humans. High-level tasks – choosing the right model for a tricky problem, checking that data and methods are valid, or designing a careful research study – involve judgment and creativity. For instance, experts note that high-skill jobs like statisticians face lower automation risk, because many subtasks (like adapting methods or evaluating validity) remain “bottlenecks to automation” ^[3].

In short, AI can speed up many core tasks (making graphs, running code, finding obvious trends), but statisticians’ more creative and critical tasks still rely on their expertise.

On the other hand, many employers are cautious. Recent studies show AI adoption is still limited: only a modest share of companies have fully integrated AI into their analytics ^[3]. Reasons include cost and trust.

Setting up AI systems (training models, buying computing power) can be expensive compared to hiring staff. And statistical work often involves sensitive data or complex questions, so firms worry about accuracy and transparency. The OECD notes that high-skilled roles like statisticians involve many skills that are hard to automate ^[3], so companies tend to use AI to assist experts rather than replace them outright.

In fact, surveys find AI tends to change tasks more than cut jobs – one UK study saw AI use shift work around without reducing overall employment ^[3].

In summary, AI is helping statisticians with data prep, visualization and simple analysis, but core responsibilities (valid methods, problem design, insight interpretation) still need people ^{[1] [3]}. As tools improve, statisticians who learn to work with AI (e.g. using it for routine analysis) will be in demand. The human skills of critical thinking and domain knowledge remain valuable, so there are reasons for hope as well as caution ^{[2] [3]}.