The AI Forge · Advanced · F9
AI-Native Product & Delivery
Product Owners, Product Managers & Scrum Masters when agents do the delivery
- Duration
- 2 days · 9:00–17:00 each day (13h contact, incl. breaks + lunch)
- Participant level
- Beginner: no AI experience needed
- Format
- Instructor-led workshop with real artifacts
- Participants
- 12 included · 20 maximum (flexible group size)
- Prerequisite
- W1 (AI Essentials) or F1 (or equivalent)
- Ecosystem
- Agnostic: any model provider or stack
- Price
- EUR 7,000 (EUR 250 per extra participant)
Amounts in EUR, excl. VAT.
ai-generatedWhat participants will be able to do
- Apply AI-native product thinking to your roadmap
- Build and ship an AI feature within one sprint
- Design the AI product feedback loop and iteration cycle
- Measure product-market fit for AI features
Tools & resources
What changes after this module
Product Owners, Product Managers and Scrum Masters lead AI-augmented delivery teams, shifting from coordinating output to framing bets, defining value, and evaluating AI-native features, with practices grounded in durable agile principles rather than any one tool.
Who should attend
Engineers and data professionals. Beginner: no AI experience needed.
Programme
This agenda is indicative. Content, sequencing, and examples are adapted to your team's context, tools, and objectives.
Before you start
- A laptop and access to an approved AI assistant
- A real product area, backlog or delivery team to work on (anonymised as needed)
- Familiarity with an agile way of working (Scrum/Kanban)
- Your team’s current definition of “done” and a recent set of backlog items
- No coding required
| Day 1 | 9:00–10:30 | The bottleneck has moved: Why, when agents make reliable delivery cheap, the constraint shifts from delivery to judgment, value and learning: and accountability concentrates rather than disappears Lab: Audit your team’s ceremonies for the one real product you’ll carry across both days: mark which now add value vs which defend a bottleneck that has already moved. |
| 10:30–10:45 · ☕ Coffee break | ||
| Day 1 | 10:45–12:30 | Defining value: Making “value” explicit and measurable before AI speed just produces faster motion; outcome over output Lab: Write a shared, measurable Definition of Value for your product and a Definition of Done that assumes agents: the pair every later lab is judged against. |
| 12:30–13:30 · 🍽 Lunch break | ||
| Day 1 | 13:30–15:30 | AI-augmented discovery & backlog: Using AI for research synthesis, user-story drafting and backlog shaping: while owning prioritisation and intent Lab: Run an AI-assisted discovery synthesis and draft a shaped, prioritised backlog slice you can defend. |
| 15:30–15:45 · ☕ Coffee break | ||
| Day 1 | 15:45–17:00 | Refinement as bet-framing: When agents generate five viable solutions in the time to read one item, refinement becomes framing bets, not decomposing tasks Lab: Reframe a real backlog item as a bet (hypothesis, smallest test, success signal) and pressure-test it with peers. |
| Day 2 | 9:00–10:30 | Running agile with agents: How planning, stand-ups and reviews change when agents deliver; a named human still explains, defends and redoes the work: accountability concentrates, it does not delegate to an agent Lab: Redesign your sprint cadence and stand-up for an agent-augmented team and dry-run it. |
| 10:30–10:45 · ☕ Coffee break | ||
| Day 2 | 10:45–12:30 | Evaluating AI-native features: What “done” and “good” mean for probabilistic features; acceptance criteria you can actually verify Lab: Write verifiable acceptance criteria and an evaluation plan for one AI-native feature. |
| 12:30–13:30 · 🍽 Lunch break | ||
| Day 2 | 13:30–15:30 | The Scrum Master shift: From ceremony facilitation to coaching, impediment-solving from AI signals, and protecting psychological safety and data integrity Lab: Prepare an AI-informed coaching plan and a hard-conversation guide for a real team impediment. |
| 15:30–15:45 · ☕ Coffee break | ||
| Day 2 | 15:45–17:00 | Operating model & wrap: Consolidating into how the team will actually work; commitments Lab: Assemble your AI-native delivery operating model (value, cadence, evaluation) and commit with a peer. |
Deliverables
- Definition of Value + agent-aware Definition of Done
- AI-assisted discovery synthesis + shaped backlog slice
- A backlog item reframed as a testable bet
- Redesigned agent-augmented sprint cadence
- Verifiable acceptance criteria + eval plan for an AI-native feature
- AI-native delivery operating model
Interested in running this module for your team? Get in touch and we'll tailor the format, dates, and delivery to your context.
Request this trainingYour trainer
Has run AI-native product delivery with agent-augmented teams; real shipped products
Discovery Session
This module requires a full day to deliver a meaningful learning experience. We only offer it in its complete format.
Frequent questions
Can modules be taken individually?
Yes. Every module stands alone at a fixed price, and every module counts toward a programme if you continue.
Where does training happen?
At your premises or remote, on your dates, for private cohorts. Open sessions run on a fixed monthly calendar.
Which AI tools do you train on?
Yours. Every module ships in four ecosystem editions and runs its exercises on your real stack.
Who delivers?
Inforca's senior consultants and trainers. Flagged modules and the executive track are delivered at senior-expert level.
Discuss this module in a First Call: fit, dates, and the path around it.
No commitment · our team responds within one business day