Program overview
This three-day executive program examines how organizations can be understood and transformed through the disciplined use of data, experimentation, and AI designed and implemented under real-world operational and strategic constraints. The course adopts a systems and evidence-based perspective, focusing on the interaction among organizational behavior, data infrastructure, analytical methods, and leadership choices, as well as the trade-offs inherent in transformation processes.
The course combines conceptual frameworks with applied analytical methods and hands-on exercises grounded in real-world examples. The science of what predicts positive organizational outcomes serves as an empirical reference point, complemented by selected insights from experimentation and data practice, to explore how internal processes can be interrogated, measured, and iteratively improved in practice.
As a complementary lens, the course integrates an examination of generative AI and its underlying mechanics to ground the discussion in current technological realities. This perspective is used to contextualize key concepts such as systemic limitations, ethical considerations, and deployment trade-offs and to connect the maturation of internal data capabilities to the responsible adoption of emerging AI technologies.
Mode of Learning
In-person
Location
KSPP
Language
English
Duration
3 Days
Program Start
June 9, 2026
Program End
June 11, 2026
Program Hours
09:00 AM – 05:00 PM
Learning Outcomes
Diagnose an organization’s readiness for AI and data-enabled change by clarifying long-term goals, constraints, and key capability and data gaps.
Select and critique people-analytics metrics and measurement approaches for a specific management problem, including common failure modes (gaming, bias, misalignment).
Design an organizational experiment (or rollout-based alternative) with clear hypotheses, outcome metrics, assignment strategy, and evaluation criteria.
Assess AI (including generative AI) for workplace deployment by identifying limitations, risks and ethical issues, and governance safeguards.
Who Should Attend?
Managers
Aspiring managers, current managers, and executives with strategic decision-making responsibilities to effectively analyze, articulate, and apply key AI management and leadership insights within their roles, teams, and organizations.
Experience
Managers with 5 to 7 years of experience.
Proficiency of written and spoken English
The program will be delivered in English. Applicants should be proficient in written and spoken English.
Faculty
Dr. Abdullah Almaatouq
is an Associate Professor at MIT. He is affiliated with the Center for Computational Engineering and the Institute for Data, Systems, and Society in the Schwarzman College of Computing. He holds a Ph.D. and an M.S. in Computational Science and Engineering from MIT, an M.S. in Media Arts and Sciences from the MIT Media Lab, and a B.S. from the University of Southampton.
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Dr. Ben Waber
is recognized worldwide as one of the leading thinkers at the intersection of management, data, workplace, and people. He is the President and co-founder of Humanyze and a visiting scientist at the MIT Media Lab, where he received his Ph.D. His research was named a Top 10 Emerging Technology by MIT Technology Review and a Breakthrough Idea by Harvard Business Review. He was previously a senior researcher at Harvard Business School.
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