Economists

Economists today often use advanced software and even AI to speed up data work. For example, researchers note that machine‐learning models are increasingly applied to economic forecasting – they can scan huge datasets and spot patterns that help predict markets or GDP ^[1]. In practice this means routine data crunching and charting can be done by algorithms, giving economists faster results.

However, the core judgment and advice tasks remain human. Explaining a forecast, writing clear reports, or testifying to policymakers still require human insight. In short, AI is acting more as a helper or tool: it automates boring number‐crunching but economists add the experience, context and communication that machines can’t handle ^{[1] [1]}.

Whether economists actually use these AI tools quickly depends on many factors. On the upside, the software and cloud services for data analysis are widely available and can be cheaper than hiring extra staff ^[1]. This means companies or governments can adopt AI if it clearly improves forecasts or saves time.

On the downside, implementing AI can take money, training, and good data. In fields like economic policy and regulation, people also worry about trust and transparency. Economists working for governments or big firms may move cautiously, since “black box” AI might not explain its reasoning in a way that regulators or the public can understand ^[1].

In short, AI will likely grow in economics – handling big‐data analysis and initial forecasting – but human economists will still do the final interpreting, advising, and ethical decision‐making ^{[1] [1]}. In fact, experts emphasize that people skills (explaining results, making judgment calls) remain as important as ever even as AI does more of the routine work ^{[1] [1]}.