Coderon Training AI for Product Managers
AI for Product Managers
Prioritise, price and ship AI features that hold up — decisions grounded in value, cost and risk, not the pressure to "have AI too".
The most expensive AI mistake in a product is not a bad implementation — it is a good implementation of the wrong feature, one whose variable cost eats the margin and that the user did not need anyway. This training gives a product manager the decision frame that separates AI features worth building from those that only look good on a roadmap.
Who it is for
Training for product owners who want to make sound decisions about AI features — from priorities to pricing. It is most useful when there is pressure on the roadmap to “add AI” but no shared way to judge which ideas will actually hold up.
How we run it
A workshop built on decision exercises and real product dilemmas, not a tour of technology. You work on a sample product: you assess the value and feasibility of AI features, prioritise them on the roadmap, and test your choices against variable cost and risk.
What you take away
- The ability to assess an AI feature by value, feasibility and economics, not by how impressive the demo is
- A method for prioritising AI features on the roadmap when they compete for the same team time
- Pricing tied to variable cost so a feature does not run at a loss at scale
- The ability to manage risk, trust and answer quality deliberately
- Success metrics defined and an evaluation conversation with engineering held in specifics
AAgenda
Where AI creates product value
- Value, feasibility and economics of AI features
- Patterns — assistant, search, agent
- What users actually expect
Priorities and the cost model
- Prioritising AI features on the roadmap
- Variable cost and the pricing model
- Risk, trust and quality
From idea to rollout
- Defining success and metrics
- Working with engineering and evaluation
- Iterating after launch
BWhat you will learn
- Prioritise AI features by value, cost and risk
- Choose a pricing model that survives an AI feature's variable cost
- Manage risk, trust and answer quality deliberately
- Talk to engineering in specifics, not wishes
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