AI for Agile Leaders

Learning objectives

By the conclusion of the course, you will be capable to:

• Describe the basics of artificial intelligence and data science, and distinguish where machine learning and data science make better Agile decision making.
• Plan the end-to-end data science pipeline to Agile cadences including feature engineering, data gathering, deployment and model choosing.
• Choose and exercise machine learning algorithms that back prioritizing, estimating, and risk recognition.
• Create an AI strategy that reinforces Agile standards, backs customer oriented discovery, and scales reliably through programs.
• Create a workflow plan that connects Jira Align, Jira, ServiceNow and Azure DevOps with AI driven insights.
• Assess when and how to make use of large language models, generative AI, and GPT style tools, as well as integration safeguards and prompt patterns.
• Improve control mechanisms and governance, as well as data privacy, transparency, model monitoring, and change management

Learning outcomes

Graduates will be able to:

• Produce AI augmented backlog items, release plans, and roadmaps that are traceable to metrics and outcomes
• Implement lightweight experiments that test hypotheses with synthetic data and user research, then feed learnings back into sprints
• Build a simple value delivery dashboard that surfaces flow, quality, and customer indicators enhanced by AI signals
• Facilitate conversations with security, legal, and data teams to operationalize responsible AI controls
• Coach teams on practical uses of artificial intelligence and data science that improve cycle time, quality, and customer satisfaction

Curriculum at a glance

• Chapter 1: Deeper Insights AI, Machine Learning and Data Science, core concepts, practical use cases for Agile delivery, and how ai and data science intersect with product strategy
• Chapter 2: Deep Dive into Data Science Pipeline, framing data sources, labelling, feature engineering, model training, validation, deployment, and feedback loops inside Agile cadences
• Chapter 3: Practice Machine Learning Algorithms, hands on exercises with classification, regression, clustering, and time series methods applied to forecasting and prioritization
• Chapter 4: AI Strategy For Agile Methodology, aligning AI value streams with OKRs, product discovery, and portfolio governance
• Chapter 5: Blueprint of Workflow (Jira, Jira Align, Azure DevOps and ServiceNow), connecting tools to capture data, automate insights, and visualize outcomes
• Chapter 6: Generative AI, LLM and GPT (LLM Integrations), prompt design, patterns for refinement, summarization, and ideation, and integration guardrails
• Chapter 7: Case Study, end to end scenario walking from problem framing to model deployment and change adoption across teams
• Chapter 8: AI and Governance, responsible use, risk management, audits, monitoring, and executive stewardship

The course emphasizes practical artificial intelligence integration in real delivery systems, combining artificial intelligence and data science with Agile rituals to elevate planning and execution. 

How this course is different?

• Practical, not theoretical, every concept is anchored to Agile cadences, team rituals, and portfolio decision making
• Tool centric patterns, clear steps to connect delivery platforms with AI signals and insights
• Governance first, responsible practices are built in from the start, not added later

Ready to bring AI into your Agile practice with confidence and control, Join the next AI for Agile Leaders cohort in Canada and accelerate value delivery with responsible, measurable outcomes. Speak with a Coach2Reach advisor to discuss schedules and team bookings. 

Contact Coach2Reach today and Enroll Now.