Summary:

Much of the current discourse about artificial intelligence hinges on whether it qualifies as a “disruptive innovation,” a term coined by Clay Christensen in the 1990s to explain why large firms lose their edge to unexpected competitors. But for IT and cybersecurity professionals, this academic debate is secondary to a more immediate question: how will AI impact our roles within organizations? In this opinion piece, I revisit Christensen’s original framework and explore how tools like ChatGPT might fit the model, not to predict the future of AI, but to provide a clearer lens for understanding how the large firms that employ many of us are likely to react. If AI turns out to be disruptive in some areas and sustaining in others, knowing where we sit in that divide could make a big difference in how we prepare.

A Useful Framework Often Misunderstood

In recent months, I’ve noticed many technologists discussing whether AI innovation represents “disruption” to their fields. While it’s true that large language models (LLMs) like ChatGPT and Claude are reshaping how we work, it’s worth asking whether they are truly disruptive innovations in the academic sense.

Clay Christensen’s theory of disruptive innovation wasn’t meant to describe any powerful or novel technology by itself. Instead, he used the theory to explain a pattern: when new entrants use simpler, more accessible, and often cheaper solutions to serve overlooked market segments, they eventually move upstream and unseat incumbents [1]. These incumbents often don’t fail because they lack talent or foresight, but because they are responding rationally to their current, high-end customers.

AI tools like ChatGPT are undoubtedly powerful, but do they meet this definition of disruptive innovation? Or are they simply the next generation of sustaining innovations that help large firms do more with less?

In the additional material below, I compare ChatGPT and Google Search in terms of simplicity, convenience, affordability, and helpfulness for new customers to fully discuss how Christensen’s model can be applied. Also, see the second reference below from the Christensen Institute for a graphic, authoritative, and concise explanation of disruptive innovation.

Why This Matters for Cybersecurity and IT Professionals

For those of us working inside large enterprises, especially in IT, cybersecurity, or compliance, understanding whether AI is sustaining or disruptive is more than semantics. It directly affects how our organizations will deploy these tools and whether we, personally, stand to benefit from them or be replaced by them.

• If AI is a sustaining innovation, large firms will adopt it to enhance existing processes, and the demand for skilled professionals who understand how to integrate and govern these tools will increase. The increases in efficiency and resource adjustment to get this done will mean active hiring and firing.
• If AI is a disruptive innovation, we may see smaller players or new service providers using AI to bypass the complex systems we’ve built, offering cheaper, faster alternatives that challenge the value of our roles. If we work for a large employer, we can expect that firm to set up separate, new, and autonomous organizations to try and shed their old processes and become disruptive players themselves.

 

I don’t think it’s a binary outcome. In fact, AI can be sustaining in some domains and disruptive in others—and that’s precisely why professionals should take a nuanced view.

 

Navigating This Ambiguity

My personal view is that AI, as it exists today, is mostly a sustaining innovation within large firms. It is being deployed to automate workflows, enhance productivity, and quickly generate insights. But that doesn’t mean it’s safe to dismiss concerns about disruption.

Small vendors and startups are already building security platforms, compliance assistants, and coding copilots that make it easier for non-experts to do what used to require a team of professionals. Some of these tools may eventually evolve to challenge enterprise solutions or erode the complexity moat that many of our roles depend on.

The safest assumption is that the ground is shifting, and professionals who understand the difference between sustaining and disruptive forces will be better prepared to navigate those changes.

 

Conclusion: Read the Signals, Not the Hype

Rather than asking whether AI is “disruptive” in the abstract, we should ask: disruptive to whom, and in what context? As professionals, we need frameworks that help us read those signals with clarity.

Below, I’ve provided resources for further reading and five indicators from Christensen’s model that indicate disruption. Christensen’s theory isn’t perfect, but it offers a useful map. And right now, we’re in uncharted territory. If you work in cybersecurity, IT, or any knowledge-driven field, this is a good time to study the map—and keep your boots laced.

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Indicators, Examples, and Further Reading

Indicators of Disruption

1. Early performance gaps, improving fast
2. Adoption in fringe or underserved use cases
3. Dismissal by market leaders or gatekeepers
4. Reduced barriers to entry
5. Shift in value proposition

 

Four Criteria of Disruptive Innovation: The Example of ChatGPT and Google Search

Simplicity: When comparing OpenAI’s ChatGPT to Google Search, can we say it is simpler to use OpenAI to gather online information? If we define simpler as ease of setting up and getting information, then I think we must give the nod to ChatGPT. Yes, maybe we must create an account for the free version of ChatGPT. Still, this step is relatively no effort compared to the value provided by the ability to use natural language and full sentences to get information and find resources. Yes, we can recognize there is much to learn from prompt engineering, but we don’t have to be an engineer to derive considerable value from ChatGPT.

Convenience: ChatGPT is more convenient for synthesizing information and asking complex questions. The service LLMs provide adds a level of convenience that was not easily predicted [8][9].

Affordability: ChatGPT is a general-purpose tool that makes several activities cheaper. I won’t try to replicate the obvious or already-mentioned use cases, but we can all agree that several consultative, educational, coding, and processing services are made cheaper using ChatGPT. The value is there. There are no initial costs or advertisements, but a fee structure for enterprises.

New Customer Set: The business use at the Enterprise and small business levels could be considered a new customer set. Considering how these two tools are used, one is more easily applied to adding value in a firm and charging customers for that value. Because of ChatGPT’s maturity at gathering information from the internet, even small businesses can use ChatGPT to develop a strategy, reduce expenses, and provide products. This means these businesses are more likely to pay a fee and become customers of ChatGPT under a different model than the indirect advertising model of Google Search.

 

Further Reading

The first two sources below provide 95% of what you need to better understand the disruptive innovation map.

1. Christensen, C. M., & Raynor, M. E. (2003). The Innovator’s Solution: Creating and Sustaining Successful Growth. Harvard Business Review Press.
2. Christensen Institute (2018). Disruptive Innovation Theory. Retrieved from: https://www.christenseninstitute.org/theory/disruptive-innovation/
3. Horn, M. B. (2024, June 3). What does disruptive innovation theory have to say about AI? Christensen Institute. https://www.christenseninstitute.org/blog/what-does-disruptive-innovation-say-about-ai/
4. Ojomo, E. and Sanchez, S. (2024, June 10). Why AI’s business models will determine its potential to ignite global prosperity? Christensen Institute. https://www.christenseninstitute.org/blog/why-ais-business-models-will-determine-its-potential-to-ignite-global-prosperity-part-2/
5. Christensen, A. (2024, June 3). Stewardship in AI: Our new series helps see into the future. Christensen Institute. https://www.christenseninstitute.org/blog/stewardship-ai-new-series-helps-see-future/
6. Horn, M. B. (2025, April 29). Disrupting US schools wasn’t possible before. That may be changing. Christensen Institute. https://www.christenseninstitute.org/blog/disrupting-u-s-schools-wasnt-possible-before-that-may-be-changing/
7. Staker, H. (2025, February 27). A new era for teachers as AI disrupts instruction. Christensen Institute. https://www.christenseninstitute.org/blog/a-new-era-for-teachers-as-ai-disrupts-instruction/
8. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems (Vol. 30). Curran Associates, Inc. https://papers.nips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
9. Levy, S. (2024, March 20). 8 Google employees invented modern AI. Here’s the inside story. Wired. https://www.wired.com/story/eight-google-employees-invented-modern-ai-transformers-paper/
10. Pot, J. (2023, July 11). Google’s New Search Tool Could Eat the Internet Alive. The Atlantic. https://www.theatlantic.com/technology/archive/2023/07/google-search-generative-experience-content-creation/674674/
11. Dave, P., & Pardes, A. (2025, March 21). Inside Google’s two-year frenzy to catch up with OpenAI. Wired. https://www.wired.com/story/google-openai-gemini-chatgpt-artificial-intelligence/
12. Novet, J., & Vanian, J. (2025, April 29). Satya Nadella says as much as 30% of Microsoft code is written by AI. CNBC. https://www.cnbc.com/2025/04/29/satya-nadella-says-as-much-as-30percent-of-microsoft-code-is-written-by-ai.html
13. Allen, J. V., Mike. (2025, May 28). Behind the Curtain: Top AI CEO foresees white-collar bloodbath. Axios. https://www.axios.com/2025/05/28/ai-jobs-white-collar-unemployment-anthropic
14. Roose, K. (2025, May 30). For Some Recent Graduates, the A.I. Job Apocalypse May Already Be Here. The New York Times. https://www.nytimes.com/2025/05/30/technology/ai-jobs-college-graduates.html