Every product roadmap in 2026 seems to have an AI feature on it somewhere, and most of them were added because a competitor shipped one first, not because anyone worked out what problem it actually solves. AI Product Management is the discipline that fixes that: knowing when artificial intelligence genuinely improves a product, and when it's just an expensive way to chase a trend. The AI for Product Managers Training Course, delivered by The British Academy for Training and Development, is built for product professionals who need to make confident, grounded decisions about AI — not just add it to the backlog because everyone else is talking about it.

This course treats AI as a product capability to be evaluated critically, not a buzzword to be adopted uncritically. Participants explore how tools like ChatGPT and other generative AI systems can genuinely change a product's value proposition, how machine learning differs from generative AI in terms of what it can realistically deliver, and where automation quietly saves real time versus where it just moves the same problem somewhere less visible. The course also covers how to build an AI strategy that fits a product's actual users and constraints, rather than a generic industry template, and how to manage the genuine risks — data quality, bias, cost, and user trust — that come with shipping AI features into production. Participants leave with a clear, practical framework for evaluating and managing AI in their own products, not just enthusiasm for the technology.

By the end of this course, participants will be able to:

• Apply AI Product Management principles to evaluate where AI genuinely adds product value
• Distinguish between generative AI, machine learning, and automation, and when each fits
• Assess tools like ChatGPT and similar generative AI systems for real product use cases
• Build an AI strategy grounded in actual user needs rather than industry trends
• Identify where automation genuinely improves workflows versus where it adds hidden complexity
• Manage risks specific to AI features, including data quality, bias, and cost
• Communicate AI product decisions clearly to technical and non-technical stakeholders
• Measure whether an AI feature is delivering genuine value after launch

Target Group

• Product managers and product owners evaluating or building AI-powered features
• Heads of product shaping AI strategy across a product portfolio
• Technical product managers working closely with data science and engineering teams
• Founders and startup leads considering AI as a core part of their product
• Innovation and R&D professionals exploring AI-driven product opportunities
• Professionals seeking a practical, non-technical grounding in AI for product decisions

• Making Sense of AI as a Product Capability
• Separating genuine AI value from feature-list trend-chasing
• Building a working vocabulary: generative AI, machine learning, and automation compared
• Generative AI and Tools Like ChatGPT in Product Work
• Evaluating where generative AI genuinely changes a product's value proposition
• Realistic use cases versus overhyped applications of tools like ChatGPT
• Machine Learning Fundamentals for Product Managers
• What machine learning can and cannot reliably deliver in a product context
• Working effectively with data science teams without needing to code
• Where Automation Actually Helps
• Identifying workflows where automation removes genuine friction
• Recognising when automation simply relocates a problem rather than solving it
• Building an AI Strategy That Fits the Product
• Grounding AI strategy in real user needs rather than generic industry templates
• Prioritising AI initiatives against the broader product roadmap
• Managing Data, Bias, and Trust in AI Features
• Understanding data quality requirements before committing to an AI feature
• Recognising and mitigating bias risks in AI-driven product decisions
• The Real Cost of Shipping AI Features
• Weighing development, infrastructure, and ongoing model costs realistically
• Avoiding AI features that quietly become expensive to maintain
• Communicating AI Decisions to Stakeholders
• Explaining AI capabilities and limitations clearly to non-technical stakeholders
• Managing expectations when AI features don't perform as initially hoped
• Measuring Whether AI Features Deliver Real Value
• Defining success metrics specific to AI-powered features
• Deciding when to iterate, scale back, or retire an underperforming AI feature