Cancel Preloader

What You'll Learn

Explain the differences between traditional AI
generative AI
and agentic AI in clear business terms.,Identify high-value opportunities for introducing AI agents into teams
workflows
and business functions.,Design human–AI workflows with clear roles
responsibilities
decision rights
and escalation points.,Establish appropriate boundaries
permissions
and oversight mechanisms for AI agents.,Evaluate AI use cases based on business value
feasibility
risk
cost
and organizational readiness.,Build an operating model that supports responsible AI adoption
governance
and accountability.,Redesign roles and workflows to combine human judgment with AI-powered execution.,Develop responsible AI controls covering privacy
security
fairness
monitoring
and incident response.,Lead organizational change
address employee concerns
and build confidence in human–machine collaboration.,Create metrics
business cases
and executive dashboards for measuring AI value and return on investment.,Communicate AI strategy effectively to executives
boards
employees
customers
and business partners.,Develop a practical human + machine team charter and a 12-month agentic AI leadership roadmap.

Requirements

No programming
data science
or machine learning experience is required.,No previous experience building AI agents is necessary.,A basic understanding of business operations
team leadership
or organizational decision-making is helpful.,An interest in using AI to improve productivity
collaboration
innovation
and business performance.,Access to a computer or mobile device with a reliable internet connection.,Willingness to evaluate current workflows and explore how human and AI responsibilities can be redesigned.,Openness to considering the ethical
governance
workforce
and risk implications of AI adoption.,The course is designed to explain technical concepts in accessible
leadership-focused language.

Description

This course contains the use of artificial intelligence.

AI agents are rapidly moving from experimental tools to active participants in business workflows. For leaders, this shift creates a new responsibility: deciding where agents should operate, how much autonomy they should receive, when humans must intervene, and how accountability will be maintained. Agentic AI for Leaders: Orchestrating Human + Machine Teams gives executives and managers a practical framework for leading this transition with confidence.

This course is designed for business leaders, executives, managers, transformation professionals, and decision-makers who need to understand agentic AI without becoming programmers. You will learn how AI agents, generative AI, automation, data, and human judgment work together to create new operating models and more intelligent workflows.

Across 12 weeks, you will move from foundational AI literacy to strategic execution. You will explore how to identify valuable AI opportunities, evaluate use cases, prioritize investments, and create a defensible AI portfolio. You will also learn how to design an effective AI operating model with clear decision rights, executive forums, governance structures, funding processes, and escalation paths.

A major focus of the course is human + AI workflow redesign. You will learn how to map existing processes before automating them, assign work between people and machines, define review thresholds, and build exception-handling paths. The course also examines role redesign, prompt libraries, reusable playbooks, quality assurance, and productivity measurement without damaging employee trust.

Responsible adoption is embedded throughout the program. You will develop practical approaches to AI governance, privacy, security, fairness, oversight, auditability, model monitoring, and incident response. You will learn how to set permissions and boundaries for autonomous agents while preserving human accountability for high-impact decisions.

Because technology alone does not create transformation, the course also addresses change leadership and workforce adoption. You will study resistance patterns, manager enablement, communication strategies, training design, incentives, team rituals, and communities of practice. These tools will help you reduce uncertainty and build practical confidence across your organization.

You will also learn how to measure the impact of agentic AI using outcome trees, baselines, leading indicators, cost models, executive dashboards, and ROI measurement. You will practice making evidence-based decisions about whether an initiative should be improved, scaled, paused, or stopped.

The course connects strategy with execution through practical leadership artifacts, including an AI leadership charter, value portfolio map, readiness heatmap, workflow map, responsible AI control plan, adoption plan, and stakeholder narrative. Each artifact helps translate complex ideas into decisions, conversations, and operating practices your organization can use immediately.

By the end of the course, you will be prepared to communicate an AI strategy to boards, employees, customers, and partners. You will complete a human + machine team charter and a practical 12-month AI leadership roadmap covering value, governance, talent, technology, risk, change, and accountability.

You will leave with a repeatable leadership system for reviewing performance, resolving exceptions, strengthening controls, developing team capabilities, and expanding successful agentic workflows across functions without losing focus or organizational trust.

This course will help you move beyond AI experimentation and lead the creation of trusted, scalable, and business-aligned human–machine teams.

Who this course is for:

Executives and senior leaders responsible for AI strategy
transformation
innovation
or business performance.,Managers preparing to introduce AI assistants or autonomous agents into their teams.,Business leaders who need to coordinate human judgment with machine-driven execution.,Department heads in operations
marketing
finance
human resources
sales
technology
and customer experience.,Transformation
change management
and organizational development professionals.,Product
program
and project leaders managing AI-enabled initiatives.,Strategy and innovation professionals evaluating agentic AI opportunities.,Risk
compliance
governance
privacy
and security leaders overseeing responsible AI adoption.,Consultants and advisors helping organizations implement AI operating models and workforce strategies.,Non-technical leaders who want to understand AI agents without learning programming.,Emerging leaders who want to develop future-ready skills for managing human + machine teams.,Anyone responsible for ensuring that AI adoption creates measurable value while maintaining trust
accountability
and human oversight.