Economic Consultant

AI Impact Assessment

AI is actively being used in many tasks within this career, though human expertise remains important. Graduates who understand AI tools will have a competitive advantage.

Methodology: Anthropic's March 2026 research into real-world AI task adoption across occupations.

Evolving Role — Adaptation Required

A degree in economics, PPE or a quantitative social science still carries real weight with UK employers in consulting, policy and finance. The analytical rigour you build at university is precisely what lets you direct AI tools rather than be replaced by them. Postgraduate study or a strong quantitative specialism, such as econometrics or applied micro, increasingly separates candidates who can verify and interrogate AI outputs from those who simply accept them. The degree is not losing value, but it is shifting from signal of technical competence alone to signal of critical oversight capability.

By 2031, most economic consultancies will have embedded AI research assistants that handle first-draft modelling, data cleaning and regulatory literature searches. Graduate intakes are likely to shrink at major firms like Oxera, NERA and Frontier Economics as junior task volumes consolidate. Graduates entering now will be expected to produce senior-quality output faster and with smaller teams, so those who can fluently use AI tools while spotting their errors will pull ahead sharply.

By 2036, the traditional pyramid of one senior economist supported by several junior analysts will be largely inverted or flattened. Fewer people will do more, and the premium will fall on economists who can manage AI workflows, communicate complex uncertainty to non-technical clients, and hold credibility in regulatory or legal settings. Competition policy, public sector advisory and litigation support are likely to remain human-intensive because the outputs face direct scrutiny and challenge. Economists who have built deep sectoral expertise, in energy, healthcare or telecoms for instance, will be significantly more defensible than generalists.

By 2046, economic consulting will look closer to medicine than to its current form: a smaller, highly credentialled profession where AI handles diagnostic groundwork and humans make the consequential calls. Entirely new roles around AI model auditing, economic impact verification and algorithmic regulation are plausible growth areas that do not yet exist at scale. The profession survives and arguably gains influence, but the number of roles at any given seniority level will be materially lower than today. Students entering now should plan for a career defined by continuous specialism rather than a stable ladder.

Go deep on econometrics and causal inference

The economists who will audit and correct AI-generated models need to understand what the models are actually doing. Investing in causal inference, instrumental variables and structural modelling at university or postgraduate level gives you the technical authority to direct AI tools rather than trust them blindly. This is the skill that separates a defensible expert witness from an expensive chatbot operator.

Pick a regulated sector and own it

Competition economics in energy, pharma or telecoms involves regulatory proceedings where outputs are scrutinised by the CMA, Ofgem or sector tribunals. These settings require human accountability and nuanced contextual knowledge that AI cannot credibly provide alone. Becoming the person who understands both the economics and the regulatory culture of a specific sector makes you far harder to displace.

Build client-facing and communication skills deliberately

As AI absorbs more of the analytical pipeline, the differentiating skill shifts toward explaining uncertainty, managing stakeholder expectations and defending conclusions in contested environments. Seek out opportunities during your degree and early career to present, write for non-technical audiences, and engage in structured debate. These are the skills that determine who gets in the room with the client.

Treat AI tools as a core technical competency

Knowing how to structure a research brief for an LLM, validate its outputs against primary sources, and identify where it hallucinates or oversimplifies is already a practical skill employers value. Students who arrive at consultancies able to cut a week of junior analyst work into a day, while maintaining rigour, will be rewarded quickly. Build this fluency now through side projects, open datasets and tools like Python, R and emerging AI research platforms.