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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 — What Patterns Really Solve (and When They Don’t)

This step reframes design patterns as responses to recurring design forces, not reusable templates or universal best practices.

A design force is a structural pressure in your system—often driven by business change, technical constraints, team structure, quality goals, or long-term evolution. These forces show up as friction: brittle tests, ripple effects from small changes, conditional sprawl, tight coupling, or slow feature delivery.

The key discipline is learning to detect recurring tension before introducing abstraction.

You identify forces by:

  • Observing repeated pain across sprints

  • Analyzing change frequency and co-changing files

  • Watching for conditional explosion

  • Examining test friction and isolation challenges

  • Noticing ripple effects from minor changes

  • Recognizing cognitive overload or hesitation to modify code

Only after clearly naming the force should you evaluate patterns. Each pattern optimizes for one side of a tension while introducing cost—indirection, complexity, more types, and cognitive overhead.

The core exercise is simple but rigorous:

“Because we need ______, we are experiencing ______.”

If you cannot state the force precisely, introducing a pattern is architectural guesswork.

Mastery is not knowing many patterns.
It is recognizing when a recurring force justifies their trade-offs.

Author: Rod Claar
0 Comments

24 Feb 2026

Step 4: Learn How to Be an Efficient and Effective ScrumMaster

Learn How to Be an Efficient and Effective ScrumMaster

This step defines the ScrumMaster as a systems-level enabler of performance, not merely a facilitator of meetings.

Efficiency focuses on reducing friction in the workflow.
Effectiveness focuses on improving measurable outcomes.

The foundation is mindset:

  • Servant leadership to build team ownership

  • Systems thinking to address root causes

  • Empiricism to drive decisions through evidence

Scrum events are reframed as decision and alignment mechanisms, not rituals. Sprint Planning clarifies goals and risk. The Daily Scrum inspects flow. The Review validates outcomes. The Retrospective drives structured improvement experiments.

Impediment removal requires classification and root-cause analysis. Repeating blockers indicate systemic constraints, not isolated issues.

High performance depends on:

  • Clear goals

  • Stable teams

  • Fast feedback

  • Visible metrics

  • Psychological safety

The step also integrates AI prompting for Scrum Masters as a leverage capability. AI can assist with backlog
refinement, risk analysis, retrospective structuring, and stakeholder communication—provided prompts are precise, contextualized, and iterative.

The ultimate measure of effectiveness is not event execution.
It is improved flow, predictability, quality, and team engagement.

An efficient ScrumMaster reduces friction.
An effective ScrumMaster improves system outcomes.
An AI-enabled ScrumMaster scales both.

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

16 Mar 2026

AI For Scrum Masters - March 16. 2026

AI for Scrum Masters

Transforming Team Performance with Artificial Intelligence

Date: March 16, 2026
Time: 1:00 – 5:00 PM Pacific

This live, hands-on workshop shows Scrum Masters how to use AI as a practical co-pilot for facilitation, coaching, and team leadership. You’ll learn how to streamline ceremonies, generate insights from team data, improve communication, and apply AI ethically—without losing the human-centered values at the heart of Scrum.

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17 Mar 2026

AI For Scrum Product Owners - March 17. 2026

AI for Scrum Product Owners

Mar 17, 2026
Mar 17, 2026: Learn practical GenAI tactics for Product Owners—requirements, user stories, backlog ordering, and responsible AI use—plus a hands-on prompt lab.
Author: Rod Claar
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