Scrum Foundation, Scrum Master, and AI Fundamentals

Course overview

This training combines Scrum Foundation, Scrum Master with Artificial Intelligence fundamentals and imparts comprehensive knowledge in the Scrum framework as well as agile team leadership and foundational knowledge of artificial intelligence. It covers the roles, artifacts, and events of Scrum, as well as the responsibilities of the Scrum Master in team coaching and conflict management, along with key AI concepts such as machine learning, data quality, and generative AI. Additionally, agile estimation methods, monitoring techniques, and the scaling of agile projects are taught, along with AI ethics, data protection, and the business case for AI. The content connects agile project management with technological understanding of AI. The length of the training depends on the specific composition of the course modules and individual prior knowledge. Agile Scrum Foundation o Agile Manifesto, agile principles, and differentiation from traditional methods o Scrum roles (Product Owner, Scrum Master, Development Team) and Scrum events (Sprint, Sprint Planning, Daily Scrum, Review, Retrospective) o Scrum artifacts: Product Backlog, Sprint Backlog, Product Increment o Agile estimation methods such as Planning Poker and Story Points o Monitoring with Burndown Charts and other Scrum metrics o Scaling Scrum in large and complex projects o Exam preparation and certification: Agile Scrum Foundation Agile Scrum Master o Agile mindset and continuous process improvement o Responsibilities of the Scrum Master, team coaching, and conflict management o Agile estimation, planning, monitoring, and management of the backlog o Scaling agile projects and tool integration in complex endeavors o Introduction of Agile in organizations and promotion of self-organization o Exam preparation and certification: Agile Scrum Master EXIN BCS Artificial Intelligence Foundation o Fundamentals of Artificial Intelligence (AI) o Terminology, history, and types of AI, as well as its societal significance and sustainable AI o AI ethics, bias, data protection, ethical principles, regulation, and risk management for AI o Application examples, robotics, fundamentals of machine learning, as well as supervised, unsupervised, and semi-supervised learning o Data quality, data risks, data governance, big data, data visualization, large language models, generative AI, and AI training o Business case for AI, stakeholders, project management approaches, risks, costs, benefits, as well as AI governance and implementation o Career opportunities, practical examples, future impacts of AI, as well as AI awareness and ethical issues o Exam preparation and certification: EXIN BCS Artificial Intelligence Foundation Next step: Request a non-binding consultation for individual course composition and flexible start dates. This training can be combined with other courses from our offerings in IT and project management (PRINCE2®, ITIL®, Kanban, DevOps) as well as Microsoft® Office. Through the Qualification Opport…

Upcoming dates

• 2026-10-25T23:00:00.000Z · 2027-01-14T23:00:00.000Z · Hannover · Combined Learning
• 2026-10-25T23:00:00.000Z · 2027-01-14T23:00:00.000Z · Bremen · Combined Learning
• 2026-10-25T23:00:00.000Z · 2026-12-03T23:00:00.000Z · Oldenburg (Oldb) · Combined Learning
• 2026-10-25T23:00:00.000Z · 2026-12-03T23:00:00.000Z · München · Combined Learning
• 2026-10-25T23:00:00.000Z · 2026-12-03T23:00:00.000Z · Stuttgart · Combined Learning