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 of AI for Agile Leaders will be able to:

• Build a pragmatic, value focused AI roadmap for their portfolio or product area
• Align cross functional teams on data needs, experiment design and success measures
• Integrate AI decision points into Agile planning, delivery and retrospectives
• Improve forecasting and prioritisation using data informed techniques
• Introduce safeguards and ethical checks without slowing delivery
• Communicate clearly with executives and engineers about constraints, risks and evidence

Programme curriculum

Structure follows eight focused chapters, each grounded in practical decisions leaders make every week.

Chapter 1: Deeper Insights, AI, Machine Learning and Data Science

Build a shared language for data science and AI. Clarify where AI in data science adds value for prioritisation, forecasting and service reliability. Gain confidence discussing supervised and unsupervised learning, feature engineering and model evaluation without getting lost in jargon.

Chapter 2: Deep Dive into the Data Science Pipeline

Trace data from capture to action. Explore data acquisition, quality, labelling, model training, validation, deployment and monitoring. Identify where Agile ceremonies, Definition of Done and guardrails must evolve to handle data products and models in production.

Chapter 3: Practice Machine Learning Algorithms

Work through leader level exercises that translate algorithm capabilities into outcomes. Compare classification, regression and clustering use cases in product backlogs. Link experiments to hypotheses, acceptance criteria and success metrics so teams learn fast with minimal waste.

Chapter 4: AI Strategy for Agile Methodology

Craft an AI strategy that respects Agile values. Prioritise problems worth solving, shape lean discovery experiments, decide buy versus build, and align with value streams. Learn to thread AI initiatives through OKRs, roadmaps, risk reviews and release planning.

Chapter 5: Blueprint of Workflow, Jira, Jira Align, Azure DevOps and ServiceNow

Operationalise strategy in your tooling. Configure transparent work types for data and model tasks, set up traceability from hypothesis to model to customer impact, and establish dashboards that show ethical review, technical debt and model health alongside delivery flow.

Chapter 6: Generative AI, LLM and GPT, LLM Integrations

Understand how Generative AI changes knowledge work. Explore prompt design patterns, retrieval augmented generation and integration patterns that keep humans in the loop. Learn when to use off the shelf capabilities, when to fine tune and how to assess cost, latency and quality trade offs.

Chapter 7: Case Study

Walk through an end to end scenario from idea to value. You will analyse the opportunity, shape an experiment backlog, choose data and modelling approaches, implement governance and present trade offs to stakeholders.

Chapter 8: AI and Governance

Make responsible, scalable choices. Cover fairness, bias, interpretability, security, privacy, IP, auditability and regulatory expectations. Build working agreements and lightweight controls that protect customers and teams while maintaining flow.

Ready to lead with confidence

Move beyond buzzwords and turn AI into real outcomes for your teams and customers. Enrol in AI for Agile Leaders with Coach2Reach UK to build an actionable strategy, a robust delivery blueprint and the leadership confidence to guide responsible adoption. Contact our team to discuss schedules and enrollment options.

Contact Coach2Reach today and Enroll now.