Faculty
Paul McDonagh-Smith is a Visiting Senior Lecturer in Information Technology at the MIT Sloan School of Management. In his research and teaching, Paul creates key intersection points between technology and business. He specializes in translating computer and data science into measurable business value that evolves organizational capability, transformation, and strategy.
He teaches in MIT Sloan’s, ‘Accelerating Digital Transformation with Algorithmic Business Thinking’, and ‘Digital Learning Strategy’ programs. As an early pioneer in Digital Reality, Paul has successfully invented and innovated with Extended Reality (XR) and metaverse technologies across multiple industries for more than 20 years and is a featured lecturer in MIT Sloan’s, ‘Business Implications of Extended Reality (XR): Harnessing the Value of AR, VR. Metaverse, and More’ program. Paul also teaches in, and contributes to, a wide range of MIT Sloan Executive Education programs.
McDonagh-Smith plays a role in shaping the growing portfolio of digital programs at MIT Sloan Executive Education. He collaborates with the MIT Sloan team to define digital strategy and drives transformative technology experimentation.
Through close collaboration with Faculty, Labs, and Schools across MIT, as well as an extensive global network of industry partners, Paul’s approach to entrepreneurship is built upon a bias for practical action. More technology ‘presentist’ than technology ‘futurist’, Paul enables teams to invent their future, starting today.
He provides digital transformation, business model and strategy guidance to organizations across multiple industries and geographies as well as to a range of international government departments.
Prior to MIT, Paul held senior roles in Optical Network Systems Engineering, R&D, Emerging Products and Technology, Business Transformation and Human Resources during a 20-year career in the telecoms industry.
Paul is an advisor to NASA Goddard Space Flight Center, under the supervision of chief scientist, Dr. James B. Garvin.
Garvin, James B., Paul McDonagh-Smith, Rebecca M. Peters, Helen Amanda Fricker, John David Armston, J. Bryan Blair, and Scott B. Luthcke. New Space Vol. 14, No. 2 (2026): 82-98.
Enterprises looking to make the most of agentic AI will have to rethink how work gets done and how teams are organized, without forgetting the human workers who set their companies apart.
A new MIT Sloan executive education course looks at how organizations need to align “the work, the workforce, and the workplace” to succeed with artificial intelligence.
Visiting senior lecturer Paul McDonagh-Smith recommends treating vendor exits as staged migrations rather than one-time cutovers. "Proceed like a surgeon, not a butcher: map dependencies, shadow-run replacements for one cycle before decommissioning and negotiate from the renewal date backwards."
Visiting senior lecturer Paul McDonagh-Smith wrote: "If you want to measure AI transformation, don't start with 'How many people used the tool?' Start with the work itself: decompose it, see which tasks have changed, and then ask what new meaning is being created. The value is often there. The question is whether we've built the units of measurement to see it."
"The organizations I've seen moving fastest have built governance systems early enough that they became a permission structure, rather than a constraint," said visiting senior lecturer Paul McDonagh-Smith. "Governance is scaffolding: a structural condition to make ambitious experimentation possible without systemic collapse."
Visiting senior lecturer Paul McDonagh-Smith said: "AI implementation isn't the same as AI adoption. If employees distrust AI or fear job disruption, executive acceptance can translate to workforce aversion. Senior leaders need to frame AI pilots as process redesigns. Establishing corporate AI governance early helps build confidence and trust."
This two-day course demonstrates that the organizations winning with AI are not those taking the fewest precaution but the companies that have built the data governance, risk architecture, legal clarity, and stakeholder accountability that enables them to move with both speed and confidence. Drawing on frameworks including from IBM Research's AI Security, Safety, and Governance practice and Harvard Law School's work on the legal, ethical, and regulatory dimensions of AI, participants will develop a comprehensive, actionable understanding of what it means to be truly AI-ready. The program covers the full stack of AI risk, from model-level vulnerabilities and system-level threat vectors to liability law, bias, informed consent, data privacy, and the global regulatory landscape, and translates each into practical governance decisions. The organizing question across both days is not 'what could go wrong?' but rather: 'what does getting this right actually look like, and how does it unlock everything else?' Join your peers on campus to reframe risk as a strategic enabler and to build the infrastructure required for responsible, high-velocity AI adoption.
The AI Adoption: Driving Business Value and Impact 6-week course from MIT Sloan equips you to move from AI awareness to AI transformation throughout your organization. You'll map AI technologies to your critical business challenges and opportunities, navigate adoption barriers, and build sustainable co-creation practices that amplify human capability rather than replace it.
