If you’ve been awake for the last few years, you’ve heard much about generative artificial intelligence (GenAI). Almost universally, it’s characterized as a wildly game-changing technology.
Some assert that it will be the best thing ever happening to humanity—putting the world at our fingertips and making our lives infinitely easier. To others, it represents the beginning of a terrifying future where “the robots” take all the jobs, and humans forget how to engage with each other.
The extreme views are eye-popping and catch much attention, but they’re unlikely to match reality. Even knowing that it’s hard to know what to think and who to believe.
We at the Christensen Institute hear a steady drumbeat of questions such as: “You all study innovation, so what do you make of AI?” “Is it disruptive?” “Should we worry?” “How do we harness the best of this technology?” “Can we regulate this somehow so that things don’t go completely off the rails?” and so on.
With so much hype and discussion around GenAI, it seemed only appropriate for our team to explore what Disruptive Innovation and other theories have to say about GenAI in and of itself— and its implications within each of our research areas. In the weeks ahead, we’ll share articles about our emerging perspectives on the technology, its applications, and what questions remain. Potential abounds for GenAI to democratize access to economic prosperity, personalized and relationship-rich education, and transformative health care.
It’s tempting to suggest that it will “disrupt everything,” but that’s certainly not what the theories suggest. The theory of Disruptive Innovation has long demonstrated that an enabling technology will not by itself transform any sector or industry—it needs an innovative business model to bring it to market and a coherent value network of customers and suppliers. With business models and value networks only beginning to form, it’s not obvious how things will go. Further, the theory—and history—tells us that a technology that’s sustaining in some settings can also be disruptive in others.
In the first part of our series, we’ll explore some of these disruption dynamics and ask sector-specific questions, including: What type of GenAI-enabled business models are needed to ignite equitable prosperity? What student-centered metrics must be embedded into education institutions to ensure student and teacher growth and progress? And how can robust security and trust be integrated into an AI-supported health care system?
We’ve also opened the lid on our toolbox of theories, applying everything from Disruptive Innovation and Business Model Theory to Jobs to Be Done.
My father, the late Harvard Business School professor Clayton Christensen (also our cofounder and namesake), was known to point out that data is only available about the past. He became an advocate for robust theories that help us understand what causes what—and why—so that we can look at what lies ahead and make predictions about how competitors are likely to react and how customers are likely to respond with increasing certainty. So much in our world feels uncertain, and we hope this series will help clarify your vision for this exciting technology and how we can deploy it for maximum positive impact, as well as areas where we should proceed with caution.
As an Institute team and community, we are eager for these articles to spark a conversation rather than be one-way statements. We value your thoughts and reactions to each piece and are keen to hear your questions and ideas for what else we should explore. Please comment by reaching out to us via our new Connect page.
(Note: If you’re interested in contributing to this series, please contact our Senior Director of Communications, Meris Stansbury.)
