Practical agentic AI for marketing | 2 x 90-min Skills-Lab
Stop just Prompting. Start building. Go beyond basic chatbots and learn to create autonomous AI agents that execute complex marketing tasks for you. In this hands-on skills lab, you won't just learn about Agentic AI - you'll build, test, and deploy a working AI agent tailored to solve your specific marketing challenges.
This is your opportunity to move from simple AI productivity hope to developing robust, scalable Agentic AI with business cases that multiply your team's capacity and deliver measurable ROI.
In Session 1, you'll learn the Agentic AI for marketing fundamentals and build your first working AI agent. You'll then have 2 weeks to test it in your own workplace, with direct support from our AI helpline and weekly office hours. In Session 2, you'll return with your real-world results to troubleshoot, optimise, and build a powerful business case to scale your agent's adoption across the business.
No technical background is required. You only need to bring a marketing challenge you want to solve.
Who is This For?
This lab is designed for professionals ready to lead the next wave of AI adoption in marketing:
- Marketing Managers & Teams wanting to build robust business cases for AI that solve entire marketing processes, not just isolated tasks.
- Agency Managers aiming to multiply their team's capacity, automate research, and deliver more value to clients at a fraction of the cost.
- Product Marketers & Strategists looking to develop powerful agents for competitive intelligence, pricing analysis, and market segmentation.
- Any Marketer who wants to automate a repetitive, time-consuming, or research-intensive workflow and prove the value of AI to their organisation.
Key Learning Outcomes
By completing both sessions and the testing period, you will be able to:
- Build and deploy practical AI agents that solve your real-world marketing problems.
- Design custom agents tailored to your specific workflows, from campaign briefing to lead scoring.
- Use agents safely and responsibly, with proven governance checklists and quality control principles.
- Validate and verify agent outputs to ensure accuracy and brand alignment before business use.
- Troubleshoot and optimise your agents based on real-world testing and performance data.
- Calculate and demonstrate the practical ROI of your agent to justify wider adoption.
How It Works: The Skills Lab Format
This unique format is designed for practical success and real-world application.
- SESSION 1: BUILD (90 Mins). You'll learn the fundamentals of how agents work and immediately get hands-on. By the end of this session, you will have built your first functioning AI agent, ready for deployment. You'll leave with safety checklists and a clear plan for testing.
- TESTING PERIOD (2 Weeks). This is where the real learning happens. You will deploy your agent in your actual job, applying it to real tasks. Track its successes, failures, and time savings with our templates. Get support when you need it via a private email helpline and weekly office hours.
- SESSION 2: OPTIMISE & SCALE (90 Mins). Return to the lab with your findings. We'll work together to analyse your results, troubleshoot challenges, and refine your agent for peak performance. You'll finish by building a high-level business case and a 90-day implementation plan to scale its use in your team.
Choose Your Agentic AI for Marketing Path in Session 1
You have the flexibility to work on a pre-defined agentic AI use case or bring your own marketing process or problem for an agentic AI application. By the end of the first session, you will have created one of the following:
- A Competitive Intelligence Agent that autonomously researches competitors, analyses their market positioning, and generates actionable intelligence reports.
- An AI Pricing Strategy Agent that analyses market data and competitor pricing to recommend data-driven pricing approaches for your products.
- Your Own Custom Agent is designed to solve your unique challenge, with expert guidance from instructors to scope your problem and structure the workflow.
Facilitators
John Paul Danaee
John Paul Danaee is a British-Australian, co-founder and director of equitably.ai and CEO of AI First Trading (an equitably.ai joint venture) in London, UK.
He is a Chartered Marketer, a Fellow of the Australian Marketing Institute and the Chartered Institute of Marketing in the UK and an alumnus of the University of Sydney and UNSW, Australia. He is a Chartered Quality Professional and Lean Six Sigma certified Black Belt.
John Paul is a frequent writer and speaker on AI including the UK Department for Business & Trade, Bayes Business School (London), the Federation of Small Business and the Investment Association. He is passionate about getting impact from AI, recently published a feature in Quality World on it, organises the executive AI roundtable in London focused ROI from AI, chairs the AI working group at the Chartered Quality Insitute and working with UN’s AI for Good Learning Coalition.
John Paul has 30+ years of experience in marketing, performance improvement, and automation across technology, media, manufacturing, professional services, investment management and banking sectors in the UK, Australia, the US, Europe, and internationally in major corporations such as HSBC and tech startups including equitably.ai in roles including, marketing strategy, product management, partnerships, sales, and account management.
Dr Tillman Weyde
Dr Tillman Weyde is Director Co-founder, Artificial Intelligence & Data Science at equitably.ai in London, UK.
In his role as Director of AI, he manages data scientists working at equitably.ai, captures and translates leading-edge AI research from Universities, research centres, and partner collaborations to solve business problems through AI.
He has published 150+ peer-reviewed papers, won research grants from national and international institutions and companies, has been awarded several awards for research and developed and award-winning software.
Tillman is a Reader at the Department of Computer Science, School of Science and Technology at City, University of London. He is a member of the Research Centre for Adaptive Computing and Machine Learning and of the Data Science Institute at City, University of London.
