PSM-AI study guide
PSM-AI · Work through your exam, one topic at a time.
Professional Scrum Master – AI Essentials (commonly shortened to PSM-AI Essentials) is a Scrum.org certification that Scrum.org describes as one that "Validates your knowledge in applying Artificial Intelligence as a Professional Scrum Master." It sits alongside, rather than inside, the PSM I, II and III progression: the focus is the intersection of the Scrum Master accountability and AI - how a Scrum Master uses AI tools and techniques in their own work, how they help a Scrum Team use AI effectively, and how they help an organization adopt AI with Scrum. Scrum.org pairs the assessment with a free-style practice test, the Scrum Master AI Open, and with a one-day Professional Scrum Master – AI Essentials course in which, in Scrum.org's words, students "engage in discussions and exercises to explore practical uses of AI tools and techniques to help them as a Scrum Master and to learn how to enable their Scrum Teams and organization to effectively use AI." Material here is organized around five study areas: AI fundamentals and fluency; applying AI tools and techniques in the Scrum Master accountability; enabling Scrum Teams to use AI effectively; enabling the organization to adopt AI with Scrum; and risks, ethics and responsible use. The PSM-AI study guide has 5 sections and about 1,827 words.
01AI Fundamentals and Fluency for the Scrum Master
Generative AI, notably large language models, produces output by predicting likely next tokens from patterns in training data. It has no model of your team, your product, or your Definition of Done.
381 words, about 2 min02Applying AI Tools and Techniques in the Scrum Master Accountability
The Scrum Master remains accountable for the Scrum Team's effectiveness: coaching the team in self-management and cross-functionality, teaching and facilitating Scrum events, and causing the removal of impediments. AI can accelerate the work inside those accountabilities but cannot hold them.
349 words, about 2 min03Enabling Scrum Teams to Use AI Effectively
Teams adopt AI well when it is treated as an experiment: a hypothesis, a small change, a Sprint-sized window, and inspection against evidence. The Scrum Master's job is to make AI use transparent and inspectable so the team can adapt.
360 words, about 2 min04Enabling the Organization to Adopt AI with Scrum
Organisation-wide AI adoption is a complex problem, so apply what already works: a clear goal, small increments, real users, and frequent inspection. Treat adoption as a product with hypotheses - "if we pair the model with our incident runbooks, on-call triage time falls" - and test it in a timebox with a baseline measurement.
366 words, about 2 min05Risks, Ethics and Responsible Use of AI within Scrum
Accuracy : hallucination and silent errors. Bias and fairness : outputs that disadvantage groups or entrench historical patterns, especially in hiring or performance contexts.
371 words, about 2 min