AI Agents in Market Research: Revolutionising Data Insights and Decision-Making

This course on AI Agents takes you from simple prompting to building sophisticated multi-agent systems that can autonomously complete complex tasks across the research workflow.

From gathering insights to generating reports and running ongoing data pipelines, autonomous AI agents are redefining the boundaries of what's possible in market research and customer experience (CX). This course explores how agentic frameworks like LangGraph and CrewAI reshape quantitative, qualitative, and analytics workflows—enabling research and insight teams to design always-on systems, automate complex tasks, and focus on higher-level thinking.

Through live demos, hands-on coding sessions, and accessible no-code walkthroughs, you'll learn how to prototype and deploy AI agents that collaborate, reason, and unearth impactful CX insights with minimal human intervention. This course offers an actionable roadmap to amplify your work using cutting-edge AI architectures. If you're ready to move beyond chatbots and ad hoc prompting and embrace the next wave of AI disruption, this course is your launchpad.

Learning outcomes

By the end of the course, you will:

• Understand the core principles of autonomous AI agents and multi-agent
• Learn how agentic AI applies to qualitative, quantitative and CX data
• Design simple AI agents using LangGraph and CrewAI with real market research
• Build modular workflows that automate insight generation and decision
• Explore no-code tools for deploying agent-based workflows without
• Identify the strategic impact of AI agents on team roles, operations, and client
• Gain inspiration from real-world agentic use cases across the research and CX

Who will benefit?

• Market researchers (quant and qual)
• CX professionals and insight leads
• Data scientists and analytics managers
• Innovation leads and product managers
• Anyone curious about the future of AI in research and customer understanding

Learning method

• Live demonstrations with OpenAI, LangGraph and CrewAI
• No-code tutorials and walkthroughs for non-technical attendees
• Hands-on coding for those with Python familiarity
• Group discussions, breakout tasks and case study explorations
• Real-world agentic workflow examples from CX and research domains

Course delivery mode

• Online (Interactive & Engaging Sessions)

Trainer biography Dr Aji Ghose

Dr Aji Ghose has 15+ years of experience in AI, machine learning, and market research and is a seasoned expert passionate about transforming the industry. Aji serves as the Vice President of Data & Research at Chattermill, an AI-powered platform to help companies understand and improve customer experience, where he leads Data Science, MLOps, and MLEng. Previously, he was the Head of Research & Analytics at Sky, managing a large group of data scientists, researchers, engineers, and analysts. Aji earned his PhD in Computational Cognitive Science, specialising in deep learning, from Birkbeck, University of London. He holds an MSc in Computer Science with Artificial Intelligence from the University of York. Aji also serves as a chair and lecturer for Data Science, Statistics, and AI courses at the Market Research Society.

Harassment and Safeguarding Policy

By booking this event, you agree to abide by the MRS Harassment and Safeguarding Policy. A copy of the policy can be viewed here: MRS Event Harassment and Safeguarding Policy.

MRS is committed to providing a safe, inclusive and respectful environment for all attendees. Failure to adhere to this policy may result in you being asked to leave the event without a refund. In addition, MRS reserves the right, at its discretion, to refuse future bookings and attendance at MRS events.

If at any point during the event you experience or witness anything concerning, please speak to a member of the MRS team immediately so we can address it promptly and appropriately.

If you wish to raise a concern after the event, please email conference@mrs.org.uk. Concerns will be handled sensitively and proportionately, and MRS may take action where expected standards are breached.