Research Analyst

AI Impact Assessment

AI can already perform a significant portion of tasks in this career. Graduates should expect the role to evolve substantially — developing AI-complementary skills will be essential.

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

Significant Transformation Underway

A degree that feeds directly into this role, such as Economics, Business, or Data Science, still carries real value, but you need to be honest about what the market is rewarding. Graduates entering purely traditional research analyst pipelines at large corporates are finding fewer rungs on the lower ladder. The degree remains a solid foundation if you pair it with genuine technical fluency and an understanding of how AI tools work, not just how to use them. Treating the qualification as a ticket to a comfortable analytical desk job, the way it was in 2015, is where the risk lies.

Over the next five years, the volume of junior and mid-level Research Analyst positions will shrink noticeably as AI handles data gathering, synthesis, and first-draft reporting at speed and low cost. Firms are already restructuring analyst teams, keeping fewer but more senior people who can interrogate AI outputs critically. Those entering the field now need to compete on strategic thinking and stakeholder communication, not on raw data processing ability. Expect salary compression at entry level and a tougher graduate job market than the one that existed even three years ago.

By the mid-2030s, Research Analyst as a standalone job title will mean something quite different to what it does today. The role will likely exist within smaller, more specialised teams where human analysts are valued for their sector expertise, client relationships, and ability to design research frameworks that AI then executes. Generalist research roles will have largely disappeared or been absorbed into broader strategy and operations functions. Those who have built a niche, whether in a specific industry vertical, a geography, or a methodology like ethnographic or qualitative research, will be in a much stronger position than those who stayed broad.

In twenty years, the profession will have bifurcated sharply. A small cadre of highly skilled, domain-expert analysts will work alongside very powerful AI systems to produce insight that genuinely shapes major decisions, and these people will be well compensated. The large middle tier of generalist research roles that once employed thousands of graduates will have effectively vanished. Whether you end up in the first camp or find yourself repositioning into adjacent careers will depend heavily on choices you make in the first five years of your working life. This is a career that rewards early specialisation and continuous reinvention.

Build a sector specialism early

Generalist research skills are the most exposed to AI displacement. Pick an industry, whether healthcare, energy, fintech, or public policy, and go deep on it during your degree and first role. Clients and employers pay a premium for someone who understands the nuances of a sector that an AI model cannot fully replicate from training data alone.

Master AI tooling, not just outputs

Understanding how to prompt, evaluate, and critique AI-generated research is now a core professional skill, not a nice-to-have. Analysts who can identify where an LLM has hallucinated a statistic or missed a crucial market dynamic are far more valuable than those who simply pass on AI outputs. Take courses in prompt engineering, data validation, and AI literacy alongside your main degree.

Develop qualitative and primary research skills

AI is strongest at synthesising existing secondary data and weakest at conducting original qualitative research, such as expert interviews, ethnographic observation, and stakeholder facilitation. These methods require human presence, trust-building, and interpretive judgement that remains genuinely hard to automate. Making these skills central to your offering protects you from the most direct displacement pressure.

Treat communication as a technical skill

The ability to translate complex findings into clear, persuasive narratives for non-expert audiences is what separates analysts who influence decisions from those who produce reports nobody reads. AI can draft a report, but it cannot read a room, manage a difficult stakeholder, or know when to push back on a flawed brief. Invest deliberately in presentation, writing, and consulting skills throughout your training.