SAFe Achieving Responsible AI

This is a micro-certifying SAFe course designed to equip Agile professionals with the skills needed to implement responsible artificial intelligence (RAI) within their organizations. It combines a one-hour online learning module with a four-hour hands-on session, focusing on practical applications and real-world scenarios. Participants will learn to identify key stakeholders, evaluate RAI policies, write epic hypothesis statements, and develop action plans for continuous improvement of RAI practices.

Self-paced , on-demand eLearning (1 hour)

The eLearning module provides a comprehensive introduction to Responsible AI, covering foundational concepts and their alignment with SAFe practices. The eLearning includes several pre-work assignments intended to fully prepare students for the facilitated session — including researching current RAI practices in your organization, reviewing example policies, and ensuring access to a generative AI tool for practical exercises.

Activity 1 – Identifying Stakeholders (~30 minutes)

As future change agents and advocates for RAI, one of the first and most important tasks is to think about the people in an organization who would need to be involved in implementing RAI, what their responsibilities would be, as well as their top concerns. Learners will leave this activity with an actionable stakeholder map for future initiatives.

Activity 2 – Evaluating RAI Policies (~25 minutes)

Get hands-on experience using GenAI applications such as SAFe CoPilot and ChatGPT to apply critical thinking to an example set of policy elements in an RAI plan, identifying improvements needed for organizational fit.

Activity 3 – Communicating the Need for RAI (~30 minutes)

Become empowered to effectively communicate the need for Responsible AI (RAI), the goals of
an RAI initiative, and the alignment of an RAI initiative with organizational objectives through a concise “elevator pitch” activity.

Activity 4 – Writing an RAI Epic Hypothesis Statement (~35 minutes)

Practice identifying an actionable element of an RAI implementation that requires a significant effort by one or more Value Streams, and scoping that effort using a SAFe Epic hypothesis statement.