Dr Anand Rao is a Distinguished Service Professor of Applied Data Science and AI in the Heinz College of Information Systems and Public Policy at Carnegie Mellon University and a faculty with the Block Center for Technology and Society at CMU. He has focused on research, innovation, applications, business and societal adoption of data, analytics, and artificial intelligence over his 35-year consulting, industry, and academic career. Anand was the Global Artificial Intelligence Leader for PwC, a Partner in their Data, Analytics, and AI practice, and the Innovation lead for AI in PwC’s Products and Technology segment. With his PhD and research career in Artificial Intelligence and his subsequent experience in management consulting he brings business domain knowledge, software engineering expertise, statistical expertise, and modeling expertise to generate unique insights into the practice of ‘data science’ and artificial intelligence.
Anand’s current research interests include operationalizing AI, responsible AI, systems thinking, ROI of AI, theory and practice of building agent-based models and digital twins, behavioral economics, and human decision-making. He received his PhD from University of Sydney (with a University Postgraduate Research Award-UPRA) in 1988 and an MBA (with Award of Distinction) from Melbourne Business School in 1997. Anand has also co-edited four books on Intelligent Agents and has published over fifty papers in Computer Science and Artificial Intelligence in major journals, conferences, and workshops. In addition, he has authored over a hundred articles in the business and trade press.
Recently, in an exclusive interview with Higher Education Digest, Dr Rao shared his professional trajectory, insights on the most critical skills required by students and professionals to thrive in an AI-driven world, the secret manta behind his success, significant career milestone, words of wisdom, and much more. The following excerpts are taken from the interview.
Hi Dr. Rao. Please tell us about your background and areas of interest.
I am a Distinguished Service Professor of Applied Data Science and AI at the Heinz College of Information Systems and Public Policy at Carnegie Mellon University, with a career spanning over 30 years across five continents. Until last year, I served as the Global AI Leader at PwC, advising senior executives on harnessing AI for business and societal impact. My AI journey began in the 80s, where I developed real-time decision-making systems using agent-based models. As the Chief Research Scientist at the Australian AI Institute, I contributed extensively to AI research, publishing over 50 refereed papers and earning multiple awards.
My shift to the business world, following an MBA, allowed me to combine my technical expertise with strategic consulting, advising leaders in sectors such as telecommunications, financial services, healthcare, and retail. After moving to the U.S., I led the development of big data, analytics, and AI practices, pioneering applications like agent-based simulations for strategic decision-making and behavioral economics. Over the past decade I also became interested in the societal impact of AI working with multiple governments around the world on their National AI Strategies and more generally around the topic of AI risks and responsible AI practices for enterprises.
Currently, I’m a Venture Partner at Golden Sparrow, supporting early-stage startups, and the Chair of the Advisory Council at the Center for Data Science and Social Impact at the Indian School of Development Management, where I focus on bringing AI and data science into India’s development sector. I’m passionate about fostering diverse perspectives to create positive change. My research centers on operationalizing AI, responsible AI, and exploring the intersection of AI and ancient Indian philosophy, particularly Advaita, with modern science.
What do you love the most about your current role?
What I love most about my current role is the opportunity to simplify complex AI concepts for graduate students. I enjoy breaking down intricate subject areas, making them accessible and engaging, while also incorporating recent generative AI research to bring innovations into the way we teach. By using advanced AI tools, I can create more dynamic and interactive learning experiences, helping students better understand and apply these concepts.
Additionally, working with senior executives through the executive education programs allows me to translate academic AI concepts into pragmatic, real-world solutions. I find it fulfilling to bridge the gap between theory and practice, helping leaders apply AI insights to drive business innovation. Being part of the Block Center for Technology and Society at Carnegie Mellon University enables me to work with researchers across the campus to bring rigorous and pragmatics solutions to corporate clients.
Another aspect I cherish is mentoring AI founders in India and supporting their journey from research to impactful business solutions. Being involved in the development sector in India, particularly through my work with ISDM, gives me the opportunity to leverage data science and AI for social good. It’s deeply gratifying to see how technology can positively influence societal outcomes when applied thoughtfully.
What do you believe are the most critical skills or knowledge areas that students and professionals need to develop to thrive in an AI-driven world?
In an AI-driven world, the most critical skills students and professionals need go beyond technical knowledge. AI is evolving from an academic discipline into a professional and engineering one. This shift requires understanding how AI can automate or augment human tasks and decisions, and knowing how to deploy, scale, and monitor AI systems effectively.
In a world where AI is advancing rapidly, it’s essential that we add value to our work rather than get trapped in routine tasks. As AI systems become more intelligent and creative, humans must focus on strategic, and value-added roles.
Equally important is understanding AI’s societal impact and managing it responsibly. Ensuring AI is safe, secure, transparent, explainable, and fair is a growing priority as AI integrates further into our lives.
Lastly, two key skills stand out: translational skills—bridging the gap between technical AI knowledge and practical application—and the ability to “learn how to learn.” In a field that’s constantly evolving, adaptability and continuous learning are vital for long-term success.
How do you approach teaching AI ethics, bias, and responsibility, and what strategies do you use to ensure students understand the social implications of AI?
I approach teaching AI ethics, bias, and responsibility through three distinct methods. First, I use real and synthetic case studies that showcase multiple harms caused by AI, consolidating them into single scenarios. This helps students grasp the adverse impacts AI can have and the various strategies to mitigate them.
Second, I engage students in debates, encouraging diverse perspectives on difficult ethical issues in AI. These discussions foster critical thinking and expose them to the complexity and trade-offs involved in ethical decision-making.
Lastly, I develop and encourage students to create games that illustrate the societal impact of AI. Some examples include “AI Futures – The Workforce Odyssey,” “AI Governance Game,” and “AI Bias Game.” These interactive tools help students experience firsthand the challenges AI presents in a societal context.
What are some common misconceptions or myths about AI that you encounter in your teaching or leadership, and how do you address them?
One of the most common misconceptions about AI is that current advances, particularly in generative AI, will automatically lead to better ROI for companies, economic growth for countries, and societal benefits. I counter this by emphasizing the potential harms AI can cause if left unchecked. Effective social policies and regulations are needed to ensure AI augments human decisions rather than replaces them, balancing innovation with responsibility.
Another prevalent myth is that building trustworthy AI systems will naturally result in humans trusting AI, leading to widespread adoption. However, my research shows that trust is not automatic. AI systems must ‘earn’ trust, and this trust must be actively managed, as the complex human-AI dynamics depends on both participants and can change over time. It’s essential to develop ways in which AI systems can demonstrate reliability, transparency, and fairness over time to maintain this trust.
Finally, there’s often an assumption that AI can operate independently without much human oversight, which overestimates the technology’s current capabilities. AI requires ongoing human guidance to ensure it aligns with ethical standards and business goals. I address this by engaging students and executives in discussions around AI governance and ethical deployment, ensuring that AI is not only technologically sound but socially responsible.
How do you stay current with the latest AI advancements and trends, and how do you incorporate those into your teaching and leadership to ensure students are prepared for the future?
I stay current with the latest AI advancements by reading a wide range of articles and research papers that span diverse fields and perspectives. I actively follow thought leaders, bloggers, and opinion makers from across the globe, ensuring I have insights from both technology and societal viewpoints. My extensive global social network, which includes connections from corporate and academic circles, as well as from both emerging and developed countries, keeps me informed about cutting-edge developments and societal implications.
Incorporating this knowledge into my teaching and leadership is crucial. I frequently develop new case studies that reflect the latest AI trends and challenges. I also adopt innovative ways of teaching, such as using generative AI to create interactive learning experiences like multi-agent games and Socratic books. These tools help students engage with complex AI concepts in dynamic, forward-thinking ways, preparing them to tackle future challenges in the AI landscape.
You were recently recognized as one of the Top AI Leaders of 2024. Our readers would love to know the secret mantra behind your success.
The recognition as one of the Top AI Leaders of 2024 is deeply humbling. If I were to attribute my success to a “mantra,” it would be a combination of lifelong learning, staying curious, and maintaining a balance between technological innovation and societal responsibility. I believe in continuously evolving—by understanding diverse viewpoints, mentoring others, and being open to the ever-changing landscape of AI. Ultimately, success comes from fostering collaboration and always looking for ways to use AI to create positive, meaningful impact.
What is your favorite non-academic book and why?
My favorite non-academic book is Service Model by Adrian Tchaikovsky. The novel presents a world where humanity relies entirely on artificial labor, and a domesticated robot begins to question its purpose after gaining a new perspective. What resonates with me is how the book tackles one of AI’s most profound challenges—value alignment. The story illustrates the difficulty in getting AI to understand and align with human values, which is a central theme in my own research. Through its characters, the book powerfully explores the consequences of AI autonomy and ethical dilemmas.
What has been your most career-defining moment that you are proud of?
One of the most defining moments in my career has been successfully transitioning between research, corporate, and academic roles. I’m particularly proud of blending my early research in AI with my professional corporate career, where I advised global leaders on implementing AI solutions, and then returning to a more academic and research-focused role. Navigating these transitions across different cultures and countries has been incredibly rewarding. It’s this ability to adapt and innovate across diverse environments that has shaped my career and continues to drive my passion for AI.
Where do you see yourself in the next 5 years?
In the next five years, I aim to further my research in critical areas such as operationalizing AI and developing responsible AI systems. I’m passionate about teaching business executives, mid-level management, and young graduate students about AI systems management and helping build the discipline of AI engineering. Additionally, I hope to continue making a significant impact in the AI startup ecosystem and the development sector, both in the U.S. and in India, by fostering innovation and supporting responsible AI applications.
What piece of advice would you give to aspiring AI professionals across the globe?
My advice to aspiring AI professionals is to focus on the 3 Cs: Collaboration, Content, and Curiosity. First, collaborate across disciplines and within your organization to unlock the full potential of AI in different domains. Second, develop deep content expertise in at least one business domain and one technical area to effectively apply AI. Finally, stay curious—always ask why and how things work, and dare to imagine what seems impossible. This mindset will help you innovate and shape your future in AI.