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Agentic AI for Leaders: Orchestrating Human + Machine Teams

Name: Agentic AI for Leaders: Orchestrating Human + Machine Teams
Rating: 4.7 (2 reviews)

Lead human + AI teams with confidence, clear accountability, responsible governance, and measurable value.

Created bySchool of AI

Last updated 6/2026

English

English [Auto],

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.

Course content

12 sections • 84 lectures • 18h 28m total length

Why AI changes the leadership contract13:19
Identify how artificial intelligence changes the leadership contract by integrating speed, judgment, and accountability into leadership systems, with actionable decisions, governance, and risk controls.
External forces reshaping the decade13:01
Explore how external forces reshape the decade and learn to orchestrate human and agentic AI through governance, decision models, and a 30-60-90 day execution plan.
Executive sponsorship model13:02
Master the executive sponsorship model to own outcomes, define trade-offs, and grant decision rights as leaders orchestrate human and agentic AI through governance, rituals, and a 30-60-90 day plan.
From tool enthusiasm to strategic intent13:11
Transition from tool enthusiasm to strategic intent by aligning AI initiatives with a value thesis, governance, and measurable outcomes for leading human and agentic AI teams.
Leadership behaviors that signal seriousness13:26
Leadership behaviors signaling seriousness translate AI ambition into concrete governance, decision models, and execution, aligning human and agentic teams with trust, accountability, and measurable outcomes.
Stakeholder expectations and trust13:12
Week 1 executive charter workshop13:15

AI, GenAI, and agentic AI explained for leaders13:51
Discover how artificial intelligence, generative artificial intelligence, and adjentic artificial intelligence empower leaders to orchestrate human and adjentic artificial intelligence through a shared language and a 30-60-90 day execution plan.
Capabilities, limits, and failure modes12:59
Learn to navigate the capabilities, limits, and failure modes of agentic AI to orchestrate human and machine teams through governance, decision models, and evidence in leadership.
Data, context, and judgment13:11
Prompting as a leadership communication skill13:26
Develop prompting as a leadership communication skill to orchestrate human and agentic AI, guiding decisions, governance, and execution with a 30-60-90 day plan.
Model selection and vendor conversations13:26
Master model selection and vendor conversations to drive value, risk, and governance, while orchestrating human and agentic AI through intent, design, control, and scale with evidence.
Reading AI claims with healthy skepticism13:41
Read AI claims with healthy skepticism to distinguish evidence from demo theater. Learn governance, decision models, and ownership to orchestrate human and agentic AI teams.
Week 2 literacy diagnostic13:19

Revenue, efficiency, innovation, and risk value pools13:16
Orchestrate human and agentic artificial intelligence by mapping revenue, efficiency, innovation, and risk-value pools into a portfolio. Leaders use decision models and 30-60-90-day plan to govern and scale with trust.
Use-case discovery with business owners13:15
Map value pools with business owners, rank opportunities, and select bets to orchestrate human and agentic artificial intelligence, guided by governance, risk, and measurable outcomes.
Prioritization criteria leaders can defend13:02
Defend defensible prioritization criteria that link workflows to value pools, govern human and agentic ai teams, and guide a disciplined ai value portfolio with concrete decisions.
Pilot design that tests the real bet13:12
Orchestrate human and AI teams by designing pilots that test the real bet, validate riskiest assumptions, and translate insights into decisions, governance, and a scalable AI value portfolio map.
Investment thesis and funding gates13:19
Apply an evidence-based investment thesis and funding gates to orchestrate human and ai teams, aligning value pools, ownership, and governance for scalable, measurable outcomes.
Portfolio sequencing and dependency mapping13:03
Orchestrate human and agentic ai teams by applying portfolio sequencing and dependency mapping to value pools, risk, and governance, guiding leadership decisions through a thirty sixty ninety day execution plan.
Week 3 value portfolio workshop12:32
Lead value pools and portfolio choices to orchestrate human and agentic AI, turning decisions into measurable outcomes through the value portfolio map and governance for scalable leadership.

Decision rights for AI initiatives13:19
Explore decision rights for artificial intelligence initiatives to orchestrate human and machine teams. Apply governance design, workflows, and 30-to-90-day execution plans to scale AI responsibly.
Executive forums and escalation paths13:27
Orchestrate human and agentic AI teams through executive forums and escalation paths. Define governance and an operating model, and implement a 30-60-90 day plan with clear roles and cadence.
Centers of excellence, federated teams, and business ownership13:24
Harness centers of excellence, federated teams, and business ownership to orchestrate human and agentic AI through a defined operating model and governance that emphasizes decision-making and trust.
Policy, standards, and practical guardrails13:33
Lead AI orchestration of human and agentic AI teams by turning policy, standards, and practical guardrails into actionable decisions and rituals within the operating model, driving trust and measurable value.
Funding, intake, and portfolio cadence13:21
Orchestrate human and agentic AI teams by mastering funding intake and portfolio cadence, turning scattered demand into a governed AI operating model blueprint.
Partner, vendor, and procurement governance13:02
Explore partner, vendor, and procurement governance to align human and agentic AI orchestration under leadership frameworks with defined ownership, decision rights, evidence, and measurable outcomes.
Week 4 operating model design lab13:19
Shape the operating model and governance for human and agentic AI, defining roles, decision rights, and evidence to drive trusted, scalable outcomes.

Data quality and access as leadership issues12:56
Elevate leadership by treating data quality and access as governance and decision models issues, driving data readiness, risk management, and orchestration of human and agentic AI teams.
Platform architecture leaders should understand12:55
Orchestrate human plus machine teams by mastering data readiness, governance, and risk for platform architecture leaders. Translate concepts into executive action with decision models and an AI readiness heat map.
Security, privacy, and integration readiness13:06
Learn to wire security, privacy, and integration readiness into workflows, govern risk, and orchestrate human and agentic AI teams with clear accountability and measurable outcomes.
Build, buy, partner, and configure choices13:19
Talent, roles, and capability gaps13:17
Identify talent, roles, and capability gaps to orchestrate human and agentic AI teams, translating capability planning into actionable governance, decision models, and 30-60-90 day plans.
Ecosystem partnerships and vendor leverage11:15
Week 5 readiness review13:15
Assess data, tech, and ecosystem readiness; transform constraints into actionable sequencing decisions, implement 30-60-90 day plans for orchestrating human and agentic AI teams, and update the AI readiness heatmap.

Workflow mapping before automation12:58
Map workflows before automation to align human and agentic ai roles, governance, and decision ownership. Leaders drive measurable value through design, controls, adoption, and evidence-based scaling.
Human judgment, AI assistance, and escalation points13:03
Role redesign and new collaboration norms13:08
Clarify ownership, redesign handoffs, and govern roles to accelerate human-plus-AI orchestration with measurable outcomes for leaders.
Prompt libraries, playbooks, and reusable patterns13:16
Orchestrate human and AI workflows with quality assurance, review loops, and governance, building a reusable playbook and a 30-60-90 day plan for trusted decisions.
Quality assurance and review loops13:16
Orchestrate human and machine teams through quality assurance and review loops in AI-enabled workflows. Lead governance, risk management, and evidence-based decisions to improve trust, accountability, and execution.
Measuring productivity without eroding trust13:01
Measure productivity by outcomes while preserving trust in human and agentic artificial intelligence teams. Apply decision, value, risk, and governance lenses to the workflow with a 30-60-90 day execution plan.
Week 6 workflow redesign studio13:04

Risk taxonomy for leaders13:15
Leaders learn to apply risk taxonomy to human and agentic AI orchestration, using a shared risk language to make actionable decisions, govern, and update the responsible AI control plan.
Ethics, fairness, and accountability13:32
Privacy, security, and confidential information13:04
Orchestrate human and agentic AI teams by applying privacy, security, and confidential information principles through governance, decision models, and a responsible AI control plan to drive trusted, scalable leadership decisions.
Human oversight and review thresholds13:36
Lead with human oversight thresholds tied to impact, reversibility, and uncertainty, and translate decisions into actionable controls and a responsible AI control plan.
Model monitoring, auditability, and incident response13:18
Leaders orchestrate human and agentic AI by applying model monitoring, auditability, and incident response through governance, decision models, and a responsible AI control plan.
Communicating responsible use to stakeholders13:27
Learn to communicate responsible artificial intelligence to stakeholders, defining risk-aware workflows, decision owners, and controls that build trust and empower leaders to orchestrate human and agentic artificial intelligence teams.
Week 7 risk and controls tabletop13:20
Engage in the week 7 risk and controls tabletop to test governance, risk, and the orchestration of human and agentic AI through concrete decisions and controls.

Adoption psychology and resistance patterns13:15
Manager enablement and team rituals12:05
Enable leaders to orchestrate human and agentic ai through manager enablement and team rituals, applying governance, decision models, and a 30-60-90 day plan.
Training design for practical confidence13:12
Communication that reduces fear and ambiguity13:09
Incentives, norms, and accountability13:26
Lead the orchestration of human and agentic artificial intelligence teams by aligning incentives, norms, and accountability through actionable decisions, governance, and a practical 30–60–90 day plan.
Communities of practice and champions13:28
Week 8 adoption plan clinic13:32

Outcome trees and value logic13:12
Align human and agentic AI through outcome trees and value logic to link decisions to business outcomes, govern with clear metrics, and drive 30–60–90 day execution plans.
Baselines, counterfactuals, and benefit tracking13:02
Orchestrate human and agentic AI teams by applying baselines, counterfactuals, and benefit tracking to drive credible before-and-after narratives, governance, and 30–60–90 day execution plans.
Leading and lagging indicators13:33
Lead with leading and lagging indicators to guide proactive decisions in orchestrating human and agentic AI teams, using governance, scorecards, and a 30–60–90 day plan.
Cost model and total cost of ownership13:19
Leverage cost models and total cost of ownership to guide leadership in orchestrating human and agentic AI, linking value, risk, governance, and actionable decisions.
Dashboard design for executive review13:11
Learning agenda and kill/scale decisions13:02
Orchestrate human and agentic AI teams by applying learning agendas, kill-slash-scale decisions, and the AI value measurement scorecard to guide value, risk, and funding decisions.
Week 9 ROI review simulation13:18
Explore week 9 return on investment review simulation to quantify value, learning, and decisions in orchestrating human and agentic artificial intelligence, using the AI value measurement scorecard.

From assistants to agents13:03
Learn how agentic AI transforms accountability by enabling systems to plan, act, and delegate tasks, and how leaders move from assistants to agents through governance, risk management, and measurable guardrails.
Agent roles, boundaries, and permissions12:36
Define agent roles, boundaries, and permissions with clear job descriptions and escalation paths. Align governance, decision models, and guardrails to orchestrate human and agentic AI, measure outcomes, and scale responsibly.
Human-agent team design12:48
Explore practical human-agent team design for leadership, emphasizing supervision, review, and ownership of outcomes through governance, risk management, and decision models.
Control points for autonomous workflows13:03
Identify control points for autonomous workflows to manage risk and maximize value, empowering leaders to orchestrate human and agentic AI teams through governance, guardrails, and a 30-60-90 day execution plan.
Failure recovery and exception handling13:13
Scaling patterns across functions12:55
Learn to apply scaling patterns across functions to orchestrate human and agentic AI, defining workflows, owners, guardrails, and measurable outcomes for scalable, trustworthy leadership.
Week 10 agentic workflow review13:15
Week 10 reviews agentic workflows for orchestrating human and ai teams, applying decision models, governance lenses, and guardrails to ensure accountable, measurable autonomy and effective leadership decisions.

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.

Agentic AI for Leaders: Orchestrating Human + Machine Teams

What you'll learn

Explore related topics

Course content

Week 1: Strategic Mandate and Leadership Context7 lectures • 1hr 32min

Week 2: AI Literacy for Strategic Decision-Making7 lectures • 1hr 34min

Week 3: Value Pools and Portfolio Choices7 lectures • 1hr 32min

Week 4: Operating Model and Governance Design7 lectures • 1hr 33min

Week 5: Data, Technology, and Ecosystem Readiness7 lectures • 1hr 30min

Week 6: Human + AI Workflow Redesign7 lectures • 1hr 32min

Week 7: Responsible AI, Risk, and Controls7 lectures • 1hr 34min

Week 8: Change Leadership and Adoption7 lectures • 1hr 32min

Week 9: Metrics, ROI, and Business Case7 lectures • 1hr 33min

Week 10: Agentic AI and Scaled Automation7 lectures • 1hr 31min

Requirements

Description

Who this course is for: