Integrating AI Into Leadership: A Practical Model Managers Can Use
Two years ago, some students were told to avoid AI in class. Today, it's part of how they learn to lead. That shift is playing out in Leadership in Organizations (MGT 243), led by associate professor of management Ozias Moore, where AI is built directly into the leadership toolkit.
The driver wasn't hype. Moore leaned on market data from the Graduate Management Admission Council (GMAC) and student demand for hands-on AI experience to redesign the course. The goal: graduate leaders who can manage people and projects with AI as a core capability-right now, not "someday."
Why the Course Changed
GMAC's reporting on employer expectations and student priorities pointed to a clear message: leaders need practical AI fluency, not just awareness. That includes knowing where AI fits, how to evaluate it, and how to talk about it in interviews and performance reviews.
Moore's update follows that data-first approach. AI isn't a bolt-on topic; it's integrated into how students plan their growth, solve leadership problems, and communicate value to recruiters.
GMAC market intelligence is a helpful benchmark for any manager updating leadership programs.
What Managers Can Borrow From This Model
- Industry-recognized AI credential: Students complete the Microsoft and LinkedIn Professional Certification in AI for Managers through LinkedIn Learning. It gives them language, tools, and proof of skill they can put on resumes and LinkedIn profiles. Consider adopting a similar standard inside your org and making it part of development plans. See LinkedIn Learning's AI track.
- Personal 12-month leadership plan: Each student builds a growth roadmap tailored to role, industry, and geography-with AI competencies embedded (e.g., prompt writing for reports, AI-assisted decision support, change communication with AI).
- Business-case capstone: Teams frame a real leadership dilemma and build a solution with clear ROI, ethics, and team impact. They present findings at the end of the term.
- AI + discussion, side by side: In-class work pairs AI outputs with group critique. The lesson: your judgment and team dialogue are the point-AI is the accelerator, not the answer key.
- Student-selected guest experts: Learners choose and prep speakers from diverse fields-finance, healthcare, engineering, consumer brands, college athletics, and a CEO/founder. Engagement rises when the team owns the questions.
AI Literacy Meets Trust
Moore's research looks at how AI literacy and trust shape effective human-AI teamwork. That shows up in class design: students practice evaluating AI output, checking bias, and deciding where human oversight is essential.
Tools like ChatGPT are used in class as a manager's aid-drafting, summarizing, pressure-testing ideas-so students build judgment, not shortcuts.
The Market Signal Students-and Employers-Care About
One student, Aubrey Ide '27, saw the difference during internships. A firm that ignored AI slowed work down. In her next search, she targeted companies investing in AI and found opportunities aligned with that filter. That is exactly how candidates now assess employers-and how employers assess candidates.
The certification and the capstone give students a clean way to prove skill. For hiring managers, it's a fast screen for readiness.
Guest Speakers: Leadership Under Real Constraints
Across six guest sessions, leaders spoke about managing risk, budgets, brand, engineering quality, team performance, and organizational change. Five shared how they're bringing AI into their work. Even the head football coach, who hasn't fully implemented AI yet, discussed where it could fit and how he plans to get up to speed.
The through-line: leaders don't wait for perfect conditions. They test, learn, and communicate with clarity.
How to Apply This Inside Your Organization
- Set a baseline: Pick one recognized AI credential for managers and make it part of your development path and promotion criteria.
- Require a 12-month plan: Every manager documents two to three AI use cases that improve team output (e.g., reporting, knowledge reuse, onboarding) and the metrics that prove it.
- Run scenario labs: Take real leadership dilemmas (budget cuts, quality issues, change fatigue) and have teams use AI to create options, then defend the final decision.
- Give learners the mic: Let teams select external experts, prepare briefs, and lead Q&A. Accountability boosts learning.
- Measure trust and quality: Track where AI helps, where it hurts, and how managers validate outputs. Make review checklists part of SOPs.
- Update policies: Publish clear rules on data use, confidentiality, and reviewer responsibility. Treat AI as a co-pilot with named owners.
What's Next
Per Moore, attitudes flipped fast-from "AI equals cheating" to "AI is a leadership skill." Expect more providers (including OpenAI and others) to roll out certifications and for employers to ask for them in job postings. The smart move is to embed AI across leadership training now.
If you're building manager capability this quarter, start with one credential, one playbook, and one visible win. Momentum builds from proof, not slogans.
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