Will AI Disrupt Your Business? Key Questions to Ask
How AI will affect individual businesses will vary widely. A diagnostic framework can help you identify its potential impacts and navigate disruption strategically.
MIT SMR | Getty Images
Artificial intelligence won’t disrupt all industries equally. While new technology creates both threats and opportunities, most businesses — especially those with physical offerings — will find AI to be a sustaining rather than disruptive force. A structured diagnostic framework offers practical questions leaders can ask to assess the specific impact AI and other emerging technologies will have on their businesses.
As artificial intelligence changes the business world, executives’ biggest concern is disruption. They worry that their companies will go the way of Blockbuster or AOL if their leaders aren’t quick enough to respond to emerging technologies. In a 2024 survey conducted by MIT Technology Review, 60% of respondents agreed that “generative AI technology will substantially disrupt our industry over the next five years.”1
Most businesspeople now understand that there’s a distinction between disruptive and sustaining technologies and that they should be concerned about the disruptive ones.2 But only in retrospect is it clear whether a technology was disruptive to incumbents or helped them sustain their market leadership. For example, many observers predicted that the internet would disrupt financial services, yet the big banks of today are as powerful as they were 30 years ago.3
The usual inclination of executives in many industries is to assume that AI will be disruptive. I am not convinced that that is an adequate or correct response. Yes, it’s important to take looming threats seriously, but fearmongering isn’t helpful either, as it can lead to defensive behavior and a narrow set of responses.4 A smarter approach is to frame AI as both a threat and an opportunity, and for executives to think about how AI might affect their business specifically. This creates space for thoughtful and creative ways of responding.
For academic research about disruption to be helpful to executives, it needs to provide better guidance on how to act. Prescriptions will never be definitive because circumstances change, but diagnostic questions can help business leaders assess the risks and opportunities of a new technology. This article offers such an approach — a set of questions that, together, provides an assessment of the disruptive potential of any emerging technology. While this article primarily concerns AI (including generative AI), the framework should also be applicable to a wide variety of other technologies.
The counterintuitive takeaway is that for most industries, AI will not be disruptive. There are important exceptions, but in most cases, incumbents can anticipate that AI will help them sustain their positions — as long as they stay on top of what’s happening in their industry.
References
1. “Generative AI: Differentiating Disruptors From the Disrupted,” MIT Technology Review, Feb. 29, 2024, www.technologyreview.com.
2. Disruptive technologies improve a product or service in ways that the market does not expect, typically by being lower priced or designed for a different set of consumers. These features make it difficult for incumbents to respond. See, for example, C.M. Christensen, M. Raynor, and R. McDonald, “What Is Disruptive Innovation?” Harvard Business Review 93, no. 12 (December 2015): 44-53.
3. Consider the argument in H. Totonis and R. Foster, “Breakthrough Banking: The Technology Is Here, the Revolution Has Begun,” Strategy+Business 5, no. 4 (October 1996).
4. C. Gilbert, “Change in the Presence of Residual Fit: Can Competing Frames Coexist?” Organization Science 17, no. 1 (January-February 2006): 150-167.
5. J. Gans, “The Disruption Dilemma” (Cambridge, Massachusetts: MIT Press, 2016). In this book, Gans distinguishes between demand-side and supply-side mechanisms.
6. F. Dell’Acqua, E. McFowland III, E. Mollick, et al., “Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality,” working paper 24-013, Harvard Business School, Boston, Massachusetts, October 2023.
7. A. Agrawal, J. Gans, and A. Goldfarb, “Power and Prediction: The Disruptive Economics of Artificial Intelligence” (Boston: Harvard Business Review Press, 2023).
8. R.M. Henderson and K.B. Clark, “Architectural Innovation: The Reconfiguration of Existing Product Technologies and the Failure of Established Firms,” Administrative Science Quarterly 35, no. 1 (March 1990): 9-30.
9. Agrawal, Gans, and Goldfarb, “Power and Prediction.”
10. The question of which users to focus on is difficult. Certainly, it’s important to think about your existing mass-market users to see how AI might change their experience. But prior research on disruption has shown that you also need to think about non-users or those users with more basic needs, and to see how AI might be helping them. In other words — keep both groups in mind.
11. D.J. Teece, “Profiting From Technological Innovation: Implications for Integration, Collaboration, Licensing and Public Policy,” Research Policy 15, no. 6 (December 1986): 285-305.
12. P. Fore, “Chegg Got Its ‘Ass Kicked’ by AI but Hopes to Turn That Around With a New Investment in AI-Powered Tools,” Fortune, Sept. 11, 2023, https://fortune.com.
13. “Shutterstock Partners With OpenAI and Leads the Way to Bring AI-Generated Content to All,” PR Newswire, Oct. 25, 2022, www.prnewswire.com.
14. I. Visnjic, J. Birkinshaw, and C. Linz, “When Gradual Change Beats Radical Transformation,” MIT Sloan Management Review 63, no. 3 (spring 2022): 74-78.
15. J. Birkinshaw, “How Incumbent Firms Respond to Emerging Technologies: Comparing Supply-Side and Demand-Side Effects,” California Management Review 66, no. 1 (November 2023): 48-71.