Generate Value From GenAI With ‘Small t’ Transformations
Business leaders are getting real value from large language models by working their way up the risk slope and building the foundation for larger, future transformations.
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How to Maximize the Business Value of Generative AI
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CGINeil Webb
Less than two years ago, generative AI made headlines with its amazing new capabilities: It could engage in conversations; interpret massive amounts of text, audio, or imagery; and even create new documents and artwork. After the fastest technology adoption in history — with over 100 million users in the first two months — businesses in every industry began experimenting with it. Yet, despite two years of broad managerial attention and extensive experimentation, we are not seeing the large-scale GenAI-powered business transformations that many people initially envisioned.
What happened? Has the technology failed to live up to its promise? Were experts wrong in calling for giant transformations? Have companies been too cautious? The answer to each of those questions is both yes and no. Generative AI is already being used in transformative ways in many companies, just not yet as the driver of a wholesale redesign of major business functions. Business leaders are finding ways to derive real value from large language models (LLMs) without complete replacements of existing business processes. They’re pursuing “small t” transformation, even as they build the foundation for larger transformations to come. In this article, we’ll show how they’re doing this today and what you can do to generate value with generative AI.
How Businesses Are Transforming With GenAI
Our project team interviewed the senior managers of various functions, including artificial intelligence, data science, innovation, operations, and sales, at 21 large companies. We focused on understanding what organizations with relatively early and broad GenAI adoption are doing and why. We also reviewed public information about companies similar to those we studied.
To start, we needed a definition of what digital transformation means. An early definition is “the use of technology to radically improve the performance or reach of an organization.”1 More recently, OpenAI’s ChatGPT synthesized this definition: “a comprehensive integration of digital technologies that fundamentally reimagines business models and processes, contrasting with incremental change, which focuses on gradual improvements.” Digital transformations, in general, consist of numerous technology-enabled improvements, often assembled over time, to create broader change in how a company operates. They are driven not by a single technology but rather by using the right technologies for the right tasks to deliver a new way of doing business.
Our research shows that most companies are following a more targeted approach to transforming with generative AI.
References
1. G. Westerman, D. Bonnet, and A. McAfee, “Leading Digital: Turning Technology Into Business Transformation” (Boston: Harvard Business Review Press, 2014).
2. Z. Cui, M. Demirer, S. Jaffe, et al., “The Effects of Generative AI on High Skilled Work: Evidence From Three Field Experiments With Software Developers,” SSRN, published Sept. 5, 2024. https://ssrn.com.
3. E. Brynjolfsson, D. Li, and L.R. Raymond, “Generative AI at Work,” working paper 31161, National Bureau of Economic Research, Cambridge, Massachusetts, April 2023.
4. “CFO Signals,” first quarter 2024, PDF file (London: Deloitte Development, 2024), https://www2.deloitte.com.
5. S. Randazzo, H. Lifshitz-Assaf, K. Kellogg, et al., “Cyborgs, Centaurs and Self Automators: Human-Genai Fused, Directed and Abdicated Knowledge Co-Creation Processes and Their Implications for Skilling,” SSRN, Aug. 8, 2024, https://ssrn.com.
6. L. Gevelber, M. Eijsackers, and P. Natarajan, “Skills, Jobs, and LLMs — How Companies Are Redesigning Work as They Deploy AI,” panel discussion moderated by B. Armstrong, Sept. 17, 2024, at the 2024 MIT Digital Technology and Strategy Conference, Cambridge Massachusetts, video, 46 min., 21 sec., https://ilp.mit.edu.