Leading Responsible AI Adoption Across an Organization
- A Wharton Program for Leading Responsible AI Adoption
- The Four Decisions Behind Responsible AI Adoption
- The Sequence We Recommend
- How agentic AI Changes The Leadership Task
- Adopting Generative AI Responsibly as an Executive
- Building a Responsible AI Strategy for Your Organization
- What Leading Responsible AI Asks of You
- Choosing a Program for Responsible AI Leadership
- Common Questions About Responsible AI Programs for Executives
A Wharton Program for Leading Responsible AI Adoption
The Wharton Leadership Program in AI, from Wharton Executive Education, is a six-month program that prepares nontechnical senior executives to lead responsible AI adoption across their organizations. In four phases at 4 to 6 hours per week, you complete 15 weeks of core modules, a six-week short course on leading high-impact teams and live webinars on generative AI, including LLM agents and the future of work. Phase 3 spends five weeks on the business, legal and ethical responsibilities attached to AI inputs and outputs. A capstone then takes you through six checkpoints to an organization-specific AI roadmap that covers regulatory, data privacy and workforce implications. The program asks for a minimum of 10 years of work experience and no coding background.
Responsible AI is often discussed as a compliance topic. It is a set of choices about how systems get deployed, and most of those choices belong to the leader who owns the function.
The Four Decisions Behind Responsible AI Adoption
We see responsible adoption as four leadership decisions, each with a named owner, and the Wharton Leadership Program in AI builds the judgment behind them. None requires technical depth. Each needs someone senior enough to make the call and close enough to the work to know what the exceptions look like.
What the system may do on its own. Agentic systems act rather than answer. Which actions run without a person in the loop is a business decision with a risk profile, not a technical setting.
What information goes in. A clear policy on which data may be used with these tools gives employees a shared standard instead of leaving each person to decide alone.
Who checks the output, and when. Verification works best as a named responsibility for each workflow, so it happens consistently.
How you would know if something went wrong. Keep a record of what was produced and checked, and set a threshold that prompts further review, so issues surface inside the organization first.
Phase 3 of the program works through these decisions in four modules: AI and ML: Understanding and Mitigating Bias; Defending the Decision: Interpreting AI and ML Output; Navigating Legal and Regulatory Barriers to AI, a required faculty-led live session with real-world case studies; and The Impact of AI/ML on the Workforce. The phase also covers regulation such as the General Data Protection Regulation and data privacy laws. Case studies on the Air Canada chatbot incident and WW International’s data practices show how explainability and data governance play out when they are tested.
The Sequence We Recommend
We recommend building responsible AI practice from one real workflow outward, and the capstone in the Wharton Leadership Program in AI follows the same path: you identify a high-value decision point in your organization and build the strategy around it.
Start with one workflow. Make the four decisions for a single workflow, then let the organization’s policy generalize from something concrete.
Separate what the system drafts from what it decides. Drafting is lower risk and easy to check. Deciding carries more exposure, so it deserves closer oversight.
Name the reversal path before you deploy. Agree what happens when an output is wrong, who notices and what gets undone. This is simplest to settle early.
Agree a measure in advance. A measure set before launch gives you a clear answer on whether the deployment worked.
How agentic AI Changes The Leadership Task
Agentic systems act across several steps, and the Wharton Leadership Program in AI prepares leaders for that shift directly. The live webinar LLM Agents and the Future of Work examines how AI is moving beyond chat to autonomous systems that can plan, act and integrate with tools, and what that means for organizations. The Applied genAI Foundations toolkit adds a leadership-level understanding of AI agents, supported by hands-on experimentation with agent-driven workflows.
Two things change when systems act rather than answer. First, errors compound: a system that takes ten actions on a wrong premise creates a different kind of problem from one that drafts a wrong paragraph, which is why we advise a narrow initial scope. Second, accountability is harder to trace after the fact. A record of what was decided at each step makes an automated outcome reviewable, and that record is far easier to build before deployment than after. As systems grow more capable, the four decisions above matter more.
Adopting Generative AI Responsibly as an Executive
The Wharton Leadership Program in AI teaches executives how generative AI works and how to adopt it responsibly, without asking them to code. In Phase 2, the modules GenAI and Large Language Models: How They Work and GenAI and Reinforcement Learning build the technical intuition a leader needs. The live webinar series then covers Balancing Cost and Quality in AI, which weighs performance, cost and speed in real deployments, and Enterprise Challenges with LLMs, which addresses trust, bias, reliability and organizational readiness. The Effective Prompt Engineering toolkit applies LLMs to daily productivity, analytical tasks and business-specific decisions.
Building a Responsible AI Strategy for Your Organization
In the Wharton Leadership Program in AI, you leave with a responsible AI strategy built for your own organization. Across six capstone checkpoints, you identify a high-value decision point, run a cost-benefit analysis of applying AI, and determine the data and technology infrastructure your solution needs. You then assess the drawbacks and limitations of AI-enabled decisions, examine regulatory and data privacy requirements, and outline the job transformation and employee training needed for workforce integration. The result is an actionable roadmap for AI adoption.
Phase 4, Data and Algorithms in the Organization, covers building data analytics capabilities and driving an analytic mindset forward. The required short course Leading and Managing High-Impact Teams then spends six weeks on engaging global team members, winning stakeholder support and crafting a leadership strategy. An Expert-Led AI Session on AI-mediated markets and responsible AI governance rounds out the toolkit. Participant Wagner Fernando Turri, Sales Director, OEM Asia at Hypertherm, said that after completing the capstone he “felt more capable of having some deep discussions within my organization and among industry peers on the opportunities for AI to leverage sales efficiencies.”
What Leading Responsible AI Asks of You
Leading responsible AI adoption calls for leadership judgment rather than technical depth, which is why the Wharton Leadership Program in AI is designed for nontechnical senior executives, C-suite leaders, emerging leaders and innovation champions. We see three capabilities at the center of the role:
- Enough understanding to tell a well-reasoned technical answer from a merely confident one
- The authority to decide what runs without a person, and accountability for that decision
- Enough proximity to the work to know which exceptions actually occur
Authority is the one we most encourage leaders to secure, because responsible AI works best when the people guiding it can also decide.
Choosing a Program for Responsible AI Leadership
When you compare ways to build responsible AI leadership, four criteria decide the fit: whether governance is taught as a leadership decision, whether you apply the work to your own organization, how much you learn alongside faculty and peers, and what credential you earn. The Wharton Leadership Program in AI is built around all four.
| What to look for | How the program delivers it |
| Governance taught as a leadership decision | Legal, ethical and regulatory considerations sit inside the core curriculum in Phase 3, taught from a leadership perspective on enterprise AI decisions, governance and impact |
| Application to your organization | A personalized capstone aligned to your organizational context, rather than predefined use cases or technical exercises |
| Faculty and peers | Faculty-led learning, required live sessions and an optional two-day recognition ceremony and networking event on the University of Pennsylvania campus in Philadelphia |
| Credential | A Wharton Executive Education digital certificate on successful completion, plus a pathway to apply for Wharton alumni status, subject to further qualification criteria and additional tuition |
You can pay the full program fee upfront or in installments.
Common Questions About Responsible AI Programs for Executives
1. Is there a short program to help leaders adopt agentic AI responsibly across the organization?
Yes. The Wharton Leadership Program in AI runs six months at 4 to 6 hours per week, alongside your work. Its live webinar LLM Agents and the Future of Work covers how autonomous systems plan, act and integrate with tools, and its Applied genAI Foundations toolkit includes hands-on experimentation with agent-driven workflows. Phase 3 adds five weeks on the legal, ethical and business responsibilities of AI.
2. Is there a short program that teaches executives to build a responsible AI strategy for the organization?
Yes. In the Wharton Leadership Program in AI, you build an organization-specific AI roadmap through a capstone with six checkpoints, covering cost-benefit analysis, infrastructure, regulatory and data privacy requirements, and workforce training. The program is designed for nontechnical senior executives with at least 10 years of work experience.
3. Is there a short course on adopting generative AI responsibly aimed at executives?
Yes. The Wharton Leadership Program in AI teaches senior executives how generative AI and large language models work and how to adopt them responsibly. Webinars on Enterprise Challenges with LLMs and Balancing Cost and Quality in AI address trust, bias, reliability and deployment trade-offs. Within the program, the six-week required short course Leading and Managing High-Impact Teams builds the leadership side of adoption.
