How to Get a Job For Project Managers

Learn how to write resume and interview

Leadership for Project Managers

Learn how to lead and drive project success.

Project Management with AI

From a beginner to project management professional.

Agile Project Management and SCRUM with AI

Learn to run Scrum and Agile with AI.

Accepted Deliverables

Work products that satisfy the user story acceptance criteria and are formally approved by the Product Owner. Once approved, they are treated as accepted deliverables and may be released to customers at the Product Owner's discretion.

Key Points

  • They must meet all defined user story acceptance criteria.
  • The Product Owner provides the formal approval (acceptance) after review.
  • Acceptance makes the deliverable eligible for release, but release timing is a business decision.
  • Evidence of acceptance often includes passing tests, a successful demo, and documented sign-off or status change.

Example

In a sprint review, the team demonstrates the "one-click checkout" user story. All acceptance criteria (tax calculation, saved address selection, and confirmation page) are met. The Product Owner marks the story as accepted and moves it to "Ready for Release," to be included in the next deployment.

PMP Example Question

In an Agile project, what best describes an accepted deliverable?

  1. A feature the Scrum Master confirms meets the Definition of Done.
  2. Work the Product Owner approves because it meets the acceptance criteria, making it eligible for release.
  3. An increment deployed to staging with no critical defects.
  4. A user story completed with development and unit testing finished.

Correct Answer: B — Deliverables approved by the Product Owner after meeting acceptance criteria

Explanation: Acceptance is the Product Owner's decision based on the agreed acceptance criteria; completion, DoD, or deployment alone does not equal acceptance.

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.

Explore the Course


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.

See What Is Included