Digital Economy Dispatch #243 -- 10 Academic Voices Every AI Leader Should Follow

Academia is under siege. From company boardrooms and news websites to government policy discussions, the value of academic research is increasingly questioned. Critics point to graduates struggling to find relevant careers, research papers written in impenetrable jargon, and the persistent gap between university insights and entrepreneurial success. University finances are in a bit of a mess. The perception is growing that academia is disconnected from the real world, producing knowledge that gathers dust rather than drives innovation.

Yet in the midst of this criticism, and particularly in the rapidly evolving landscape of AI, I believe that dismissing academic voices entirely would be a grave mistake for digital leaders. While the AI space is flooded with AI snake oil (consultants promising miracle transformations, vendors overselling capabilities, and influencers recycling surface-level insights), there exists a cadre of academic researchers providing genuinely valuable, evidence-based guidance on AI adoption and implementation. If you can spot them.

The challenge for decision-makers isn't finding information about AI—it is knowing which voices to listen to amid the overwhelming hype cycle. Marketing materials promise revolutionary change while implementation realities prove far more complex. Vendor case studies showcase best-case scenarios while independent research reveals the messy truth of AI deployment. This is where rigorous academic research can be invaluable, offering data-driven insights and considered analysis stripped of commercial bias.

What follows is my personal selection of ten academics whose work rises above the noise. These are the voices I listen to, and turn to frequently. Researchers who combine theoretical rigour with practical relevance, offering digital leaders the kind of grounded insights needed to navigate AI adoption successfully. I follow these voices not because they tell me what I want to hear, but because they tell me what I need to know. And you should seek them out too.

Ethan Mollick - University of Pennsylvania, Wharton School

Mollick is Professor in Management and co-director of the Generative AI Lab at Wharton. He has emerged as perhaps the most practically-minded academic voice on AI implementation. His background spans entrepreneurship education and innovation management, giving him unique insight into how new technologies actually get adopted in organizational settings. His research on LLMS, AI tools, and AI vendor directions focuses on real-world usage patterns rather than theoretical possibilities.

For digital leaders, Mollick's work offers immediately actionable insights on AI tool deployment. His studies reveal how different roles within organizations can leverage AI most effectively, what training approaches actually work, and which implementation strategies lead to sustainable adoption. I find his discussions on the latest AI foundations and tools to be particularly insightful to keep me informed on quickly changing AI capabilities.

Erik Brynjolfsson - Stanford University, Human-Centered AI Institute

A longtime observer of technology's economic impact, Brynjolfsson is a professor at Stanford Institute for Human-Centered AI (HAI), and Director of the Stanford Digital Economy Lab. He brings decades of research on digital transformation to current AI discussions. His work on productivity paradoxes and technology adoption cycles provides crucial context for understanding why AI implementations often fall short of expectations. His collaboration with major corporations offers insight into large-scale AI deployment challenges.

Brynjolfsson's research helps digital leaders set realistic expectations for AI returns on investment. His work demonstrates why productivity gains from AI often take years to materialize and require fundamental changes to business processes, not just technology adoption. His framework for measuring AI's economic impact provides essential metrics for evaluating success beyond vendor-supplied case studies.

Andrew McAfee - MIT Sloan School of Management

McAfee' is co-founder and co-director of MIT’s Initiative on the Digital Economy. His research focuses on how digital technologies reshape work and organizations. His work on digital transformation provides a foundation for understanding AI as part of broader organizational change rather than isolated technological deployment.

For leaders managing AI initiatives, McAfee's insights highlight the organizational capabilities needed for successful implementation. His research shows how companies that succeed with AI differ in their approach to data, experimentation, and change management. His frameworks help leaders identify where their organizations need development before AI tools can deliver meaningful value.

Andrew Ng - Stanford University

Ng  is founder of DeepLearning.AI, Chairman and Co-Founder of Coursera, and an Adjunct Professor at Stanford University’s Computer Science Department. He brings unique credibility through his combination of academic research and industry leadership roles at Google, Baidu, and through his AI ventures. His work spans technical AI development and practical deployment strategies. His educational initiatives have trained thousands of practitioners, giving him insight into common implementation challenges across different organizational contexts. His free online education is spectacular.

Ng's contribution to digital leaders is based on his systematic approach to AI implementation, practical AI project delivery, and his realistic assessment of current AI capabilities. His AI transformation playbook provides concrete steps for building organizational AI capability, while his technical insights help leaders distinguish between genuine AI opportunities and overhyped applications.

Rita McGrath - Columbia Business School

McGrath is Academic Director for Executive Education at Columbia Business School. Her expertise in strategic innovation and competitive dynamics brings a crucial perspective on AI's strategic implications. Her research on discovery-driven planning offers a framework for managing AI investments as strategic experiments rather than guaranteed transformations. Her work helps leaders think beyond operational efficiency to competitive positioning.

McGrath's insights guide digital leaders in developing AI strategies that create sustainable competitive advantage. Her research opens up how companies can use AI to reshape industry boundaries and create new value propositions, moving beyond cost reduction to revenue generation and market expansion opportunities.

Julian Birkinshaw - Ivey Business School, Western University

Birkinshaw was for a long time at London Business School, but is now Dean of the Ivey Business School in Canada. His research on management innovation and organizational agility provides essential context for AI adoption within existing organizational structures. I often quote his work on how leaders can speed up decision making. His work on corporate entrepreneurship and strategic renewal helps leaders understand how AI initiatives interact with established business models and organizational cultures.

For digital leaders, Birkinshaw's work explains the management practices needed to support AI adoption successfully. His research shows how companies can build the experimental capabilities and risk tolerance needed for AI innovation while balancing this with operational excellence in core business functions.

Fei-Fei Li - Stanford University, Human-Centered AI Institute

Li is Professor in the Computer Science Department at Stanford University, and a Founding Co-Director of Stanford's Human-Centered AI Institute. Her leadership in computer vision research and her role in founding Google Cloud's AI division provide unique perspective on both technical possibilities and practical limitations of AI systems. Her work on human-centered AI emphasizes the importance of designing AI systems that augment rather than replace human capabilities. I find her perspective on the future of AI to be particularly useful.

Li's contributions guide digital leaders in developing AI strategies that consider both technical capabilities and human factors. Her research on AI bias and fairness provides frameworks for responsible AI deployment, while her industry experience offers realistic timelines for AI capability development.

Mariana Mazzucato - University College London, Institute for Innovation and Public Purpose

Mazzucato is Professor in the Economics of Innovation and Public Value at UCL, where she is Founding Director of the Institute for Innovation and Public Purpose. Her influential books including "The Entrepreneurial State" and "Mission Economy" have reshaped UK government policy on public sector innovation. She advises governments worldwide and her work has influenced EU innovation policy, UK industrial strategy, and investment approaches globally.

For digital leaders, Mazzucato's mission-oriented approach provides crucial frameworks for thinking about AI's societal impact and long-term value creation. Her recent work specifically addresses AI governance, arguing that the technology should be steered toward public value creation rather than commercial exploitation. Her insights help leaders understand how AI investments can create sustainable competitive advantage while contributing to broader societal goals, moving beyond short-term efficiency gains to transformative innovation.

Annabelle Gawer - University of Surrey, Centre for Digital Economy

Gawer is Professor in Digital Economy at the University of Surrey. She is a globally recognized expert on digital platforms and ecosystems. Her research on platforms has been read widely and she serves as a Digital Expert for the UK Competition and Markets Authority and has advised the European Commission on platform regulation.

Gawer's work on the implications of AI for the digital economy provides digital leaders with essential frameworks for understanding how AI transforms platform-based business models. Her research helps leaders navigate the intersection of AI adoption and platform strategy, offering insights into how AI changes competitive dynamics in digital ecosystems and the regulatory challenges that emerge when AI capabilities are deployed at platform scale.

Sandra Wachter - Oxford Internet Institute

Wachter is Professor of Technology and Regulation at the Oxford Internet Institute and one of Europe's leading voices on AI regulation, algorithmic accountability, and data protection. Her research focuses on the intersection of AI, law, and ethics, particularly around explainable AI. As AI regulation becomes increasingly important globally, her centre’s work on algorithmic auditing and compliance frameworks is becoming essential reading for business leaders.

For digital leaders, Wachter's insights are crucial for navigating the complex regulatory landscape around AI deployment. Her research provides practical frameworks for ensuring AI systems meet emerging legal requirements while maintaining business value. Her work on algorithmic impact assessments and transparency requirements helps leaders build compliant AI systems from the ground up rather than retrofitting compliance later.

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Voices That Matter

These ten academics represent diverse perspectives on AI and its future, but are united by an academic perspective, rigorous methodology, and practical relevance. Their work provides the evidence-based foundation that digital leaders need to move beyond AI hype toward successful implementation. In a field dominated by marketing messages and vendor promises, I urge you to listen to these voices offering the kind of independent, research-backed insights that drive genuine transformation and complement commercially driven messages that dominate the airwaves.

You never know, while guiding your thoughts on the future of AI, they may even change your perspective on why academia’s role, value, and impact remain so important!