The Product Owners Collaboration Plan

A working agreement that lays out how several Product Owners coordinate with the Chief (or Chief Product Owner): who owns which backlogs, how priorities are aligned, how decisions are made and escalated, the cadences for joint events, shared information flows, and rules for handling dependencies and conflicts across teams.

Key Points

  • Clarifies decision rights, including who makes final prioritization calls and how issues escalate to the Chief.
  • Defines shared cadences (refinement, planning, reviews), communication channels, and artifacts for cross-team alignment.
  • Specifies backlog boundaries, integration points, and how dependencies and conflicts are identified and resolved.
  • The Product Owners Collaboration Plan should define how multiple Product Owners collaborate with the Chief and Product Owner.

Example

A company running five Scrum teams appoints a Chief Product Owner. The Product Owners create a collaboration plan that states the Chief sets the overall product goal and resolves prioritization conflicts, each Product Owner manages a feature-area backlog, joint refinement occurs weekly, and cross-team dependencies are tracked in a shared board. The Product Owners Collaboration Plan should define how multiple Product Owners collaborate with the Chief and Product Owner.

PMP Example Question

In a scaled agile program with several Product Owners and a Chief, which element should be documented in the Product Owners Collaboration Plan?

  1. The teams coding language standards
  2. The Definition of Done technical acceptance checks
  3. Decision rights and escalation paths between Product Owners and the Chief
  4. Daily standup agendas for each team

Correct Answer: C — Decision rights and escalation paths

Explanation: The plan exists to define how multiple Product Owners coordinate with the Chief, including decision-making, prioritization, and escalation. Technical standards and team-level ceremonies are documented elsewhere.

AI Systems with Claude, for Scrum Masters and Project Managers

Most AI pilots in project management do not fail on the model. They fail because somebody pointed the thing at a decision instead of at a task. This course is built around that distinction, and around the work that follows once you get it right.

Eight teaching sections, taught against one running project from the first lecture to the last. You watch a system get built, then you build the same one against your own sprint. Nothing here is a demo that works only on the example.

You finish with five working systems and you keep them. The sprint report machine turns your board export and standup notes into the report you currently write by hand. The retro intelligence system tells you what the team keeps saying, not just what it said this time. The stakeholder comms engine drafts the update in the register the audience expects. The backlog health monitor flags the quiet decay nobody has time to check for. The risk and dependency tracker follows the chains that actually bite.

Seventy-six working files ship with it, across four sections, so every system is built against real sprint data rather than material invented for a slide. Four sprints of retrospectives, board exports, standup notes, backlog and risk data.

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Build the Sprint Reporting System, Not Another Prompt

AI Systems with Claude is our own course for Scrum Masters and Agile Project Managers, taught against one running project across eight teaching sections. You build five working systems and keep them: the sprint report machine, the retro intelligence system, the stakeholder comms engine, the backlog health monitor, and the risk and dependency tracker. Direct from HK School of Management and Technology, not on Udemy.

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