The future of research: how AI is reshaping the profession

The research industry is changing fast. Powerful structural forces, especially automation, are reshaping how insight is created, who creates it, and what skills will matter most in the years ahead.

What is changing

Five forces are reshaping research:

What it means for careers

The impact on young insight professionals

Tasks that once defined early research careers — scripting surveys, cleaning data, producing charts, writing summaries, and running desk research — can now be automated. As a result, many organisations are hiring fewer junior researchers, reducing the traditional entry route into the profession.

A growing talent pipeline problem

With fewer early-career roles, the industry risks a shortage of mid-level talent in the future. Adjacent sectors — marketing, media, and consulting — are also cutting trainee positions, and often offer higher salaries, making it harder for research to attract talent from these areas later on.

There is also a deeper risk: without grounding in research fundamentals, fewer professionals may be able to critically evaluate or validate AI-generated work.

More researchers are moving client-side

AI is shifting the balance of employment. Some large organisations are reducing insight budgets and headcount, but many smaller businesses are building insight capability for the first time because AI tools make it more accessible.

Today, far more researchers work client-side than in agencies — around 227,000 versus 59,000, according to the MRS Business of Evidence report. The centre of gravity is moving toward in-house teams.

Team structures are becoming flatter and more flexible

With fewer junior roles, teams are becoming flatter. Organisations are experimenting with two models:

• Embedding AI across all research roles.
• Creating specialist AI units that partner with insight teams.

At the same time, employers are starting to hire more freelancers, contractors, and part-time experts as roles evolve quickly and experienced talent becomes harder to replace.

Human value is shifting from production to influence

As AI takes over executional tasks, the value of the researcher is moving toward interpretation, strategic guidance, and decision influence. Senior operational specialists are also in demand to ensure quality and validation remain strong.

The AI-ready researcher needs a new skill blend

Tomorrow’s researcher will combine human judgement with AI fluency. Key capabilities include:

• Business adviser: connecting insight to commercial priorities.
• Collaborative partner: working across research, tech, and client teams.
• Agile, curious thinker: adapting as tools and expectations evolve.
• AI-fluent professional: using AI confidently while understanding its limits.
• Critical evaluator: validating evidence in increasingly data-rich environments.

The gap between agency and client-side roles will narrow

As agencies focus more on consultancy and customer success and client-side teams gain more in-house capability, the skills required on both sides are becoming increasingly similar. For mid-level professionals, the distinction between “agency” and “client” will matter far less than it once did.

What it means for employers

Partnership makes AI useful

Insight teams can start with the business decision, then ask what evidence would change it. AI can speed up drafts and exploration, but researchers still need to check methods, provenance and context. When working with technology providers and research partners, agree how automated outputs will be validated and where human judgement enters.

Junior roles need deliberate practice in questionnaire logic, sampling, moderation and interpretation, even when tools handle preparatory tasks. Training should make verification and explanation visible alongside tool use.

The insight talent market is tightening, and the competition for AI-ready researchers is intensifying.

Source for the client-side and agency figures: MRS, The Business of Evidence.