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Paul McDonagh-Smith

Senior Lecturer of IT at MIT Sloan School of Management
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Paul 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. Paul 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.

Employment
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  • Navigating the AI Divide: 'Mind the Gap' for Strategic Success
    Paul highlights the need to 'Mind the Gap' between top-down strategies and bottom-up AI experiments. While GenAI tools like ChatGPT are boardroom priorities, they also serve as employee playgrounds. Paul notes, "Bridging this divide is crucial to avoid misfires and leverage innovation." Companies must channel grassroots creativity into strategic innovation to stay competitive and optimize ROI.
  • AI Bias: Challenges and Strategies for Fairer Outcomes
    Paul explains that AI bias stems from "unfair outcomes due to biased training data." While eliminating bias is challenging, businesses can adopt strategies like diverse datasets and human oversight. He emphasizes the importance of "human-centric approaches and ethical governance" to minimize bias and promote fairness in AI systems.
Recent Quotes
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  • AI systems are fundamentally reliant on the quality of the data they are trained on. I see many organizations struggling with data silos, inconsistent data formats, and complex privacy concerns that span geographies and jurisdictions.

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