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AI Learning Over Time • Cohort-Based

Cohorts and Workshops

These offerings are designed for groups who want to build practical AI capability together over time—using a repeatable, outcomes-focused approach. Explore the options below, then visit each class page for the full details.

  • Team Activation — align on goals, tools, and guardrails.
  • AI Audit — assess readiness, risks, and highest-value use cases.
  • AI + Scrum Cohorts — build habits across roles with hands-on practice.
  • AI for Scrum Teams — practical, role-based workflows your team can adopt.
Tip: If you’re not sure where to start, choose AI Audit first—then map a cohort plan from the findings.

Ready to start?

Pick your next step—start with free learning, watch the videos, or browse the full course catalog.

Prefer Virtual or On-Site delivery for your team? See Corporate Training Offerings.

Search Results

24 Feb 2026

Step 1: Start with product vision that teams can actually execute

If the team cannot use it to prioritize backlog items, it is not actionable.

Author: Rod Claar
0 Comments

24 Feb 2026

Step 2: Identify customers, users, and the decisions that matter

If you cannot name:

  • Who you serve

  • What they are trying to decide

  • What “job” they need completed

Your backlog will drift.

Author: Rod Claar
0 Comments

24 Feb 2026

Step 3: Turn outcomes into backlog slices (without giant stories)

If a backlog item cannot be completed inside a Sprint with clear acceptance criteria, it is not sliced—it is deferred complexity.

The goal is not smaller tasks.
The goal is small increments of validated outcome.

Author: Rod Claar
0 Comments

24 Feb 2026

Step 4: Prioritize with Confidence: Value, Risk, and Learning

Prioritize with Confidence: Value, Risk, and Learning

This step introduces a simple, explicit prioritization model based on three dimensions: Value, Risk, and Learning (V-R-L).

Instead of relying on vague “priority” discussions, teams score each backlog item (1–5) on:

  • Value — business impact delivered

  • Risk — uncertainty reduced or exposed

  • Learning — validated insight gained

Making these criteria visible reduces backlog thrash, clarifies trade-offs, and exposes hidden assumptions. It also encourages earlier risk burn-down and faster validation of uncertainty.

The exercise requires scoring the top five backlog items and reviewing the ranking for balance. The goal is not mathematical precision, but strategic clarity.

AI can strengthen this process by stress-testing assumptions, surfacing overlooked risks, and simulating alternative rankings—while leaving final decisions to human judgment.

The broader outcome is disciplined, transparent prioritization aligned with strategy rather than habit.

For deeper capability, the next step is the AI for Scrum Product Owners class, which expands on using AI to refine backlog items, quantify value hypotheses, and improve decision quality.

Author: Rod Claar
0 Comments
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