Deciding Where AI Actually Belongs in Your Product: AI for Product Managers Training Course
1Summary
A support chatbot that frustrates more customers than it helps, a "smart" recommendation feature nobody asked for, a summarisation button buried three clicks deep and barely used — most disappointing AI features share the same origin story: a competitor shipped something first, and the roadmap decision came from panic rather than product judgment. The Deciding Where AI Actually Belongs in Your Product: AI for Product Managers Training Course, delivered by Arab British Fellowship Training Academy, is built for product professionals who want to make that call with confidence instead of adding AI to the backlog because everyone else is talking about it.
The course treats AI as a capability to be evaluated critically rather than a buzzword to be adopted on faith. Participants work through what generative AI tools like ChatGPT can genuinely change about a product's value, where machine learning and automation actually save time versus where they just relocate the same problem, and how to build an AI strategy shaped by real user needs rather than a generic industry template. Risk management — data quality, bias, cost, and user trust — and measuring whether a shipped AI feature is actually earning its place round out a practical, grounded framework participants can apply directly.
2Objectives and target group
By the end of this course, participants will be able to:
- Evaluate where AI genuinely adds product value instead of chasing a trend
- 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
- Identify where automation genuinely improves workflows versus where it hides complexity
- Build an AI strategy grounded in actual user needs rather than industry trends
- Manage risks specific to AI features, including data quality, bias and cost
- Communicate AI capabilities and limitations clearly to technical and non-technical stakeholders
- Measure whether a shipped AI feature is delivering genuine value, and decide what to do if it isn't
Target Audience
- 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
3Course Content
Module 1: Stop Chasing the Trend — Evaluating Genuine AI Value First
- Separating genuine AI value from feature-list trend-chasing
- Building a working vocabulary: generative AI, machine learning and automation compared
Module 2: Generative AI and Tools Like ChatGPT — Realistic vs Overhyped Use Cases
- Evaluating where generative AI genuinely changes a product's value proposition
- Separating realistic use cases from overhyped applications
Module 3: What Machine Learning Can, and Can't, Do for Your Product
- What machine learning can reliably deliver in a product context, and where it falls short
- Working effectively with data science teams without needing to code
Module 4: Where Automation Actually Helps — and Where It Just Hides the Problem
- Identifying workflows where automation removes genuine friction
- Recognising when automation simply relocates a problem instead of solving it
Module 5: Building an AI Strategy Around Real Users, Not Industry Templates
- Grounding AI strategy in real user needs rather than generic templates
- Prioritising AI initiatives against the broader product roadmap
Module 6: Data Quality, Bias and Trust — the Risks Nobody Puts on the Roadmap
- Understanding data quality requirements before committing to an AI feature
- Recognising and mitigating bias risks in AI-driven product decisions
Module 7: What AI Features Really Cost Once They're Shipped
- Weighing development, infrastructure and ongoing model costs realistically
- Avoiding AI features that quietly become expensive to maintain
Module 8: Proving, or Disproving, That an AI Feature Is Worth Keeping
- Explaining AI capabilities and limitations clearly to non-technical stakeholders
- Defining success metrics and deciding when to iterate, scale back, or retire an underperforming feature