Three years on since the launch of consumer-facing Large Language Models (LLMs), and the AI conversation has travelled from amazement to business function. The initial launch was thrilling; the future is still tantalizingly close with new developments every month, and nowhere is the AI shockwave more exciting than in the world of market research.
So, where is AI already making an impact? What do insight teams need to know to stay ahead without getting overwhelmed? And what happens when AI advances to the point of working alongside you as a digital coworker? This ebook is your companion to understanding the present and the future of agentic AI in market research.
What’s AI doing for market research?
AI, at its core, refers to machines performing tasks that typically require human intelligence, such as recognizing patterns, generating text, or sorting data.
In market research, we’re mostly talking about:
- Machine learning: Tools that learn from data and get better at predictions or classifications.
- Natural language processing (NLP): Teaching machines to understand and respond to human language.
- Generative AI/LLMs: Tools that create content (like summaries, follow-ups, or reports) based on training data.
These aren’t replacing researchers; they’re enhancing what you already do with speed, scale, and a few surprises along the way.
Topic preview:
- What’s AI doing for market research?
- What is agentic AI?
- What could agentic AI do for market research?
- Current limitations of agentic
- Research agents today: Agentic AI, built for researchers
- What makes Research Agent different
- Recommendations for working with agentic AI
- Is agentic AI going to replace research?