Each pillar raises an urgent question for CEOs. What innovations become possible when every employee has access to the seemingly infinite memory generative AI offers? How will this technology change how employees’ roles are defined and how they are managed? How do leaders contend with the fact that generative AI models may produce false or biased output?
Clearly, generative AI is a rapidly evolving space, and each of the pillars above involves short- and long-term considerations—and many other unanswered questions. But CEOs need to prepare for the moment when their current business models become obsolete. Here’s how to strategize for that future.
Potential: Discover Your Strategic Advantage
AI has never been so accessible. Tools such as ChatGPT, DALL-E 2, Midjourney, and Stable Diffusion allow anyone to create websites, generate advertising strategies, and produce videos—the possibilities are limitless. This “low-code, no-code” quality will also make it easier for organizations to adopt AI capabilities at scale. (See “The Functional Characteristics of Generative AI.”)
The immediate productivity gains can greatly reduce costs. Generative AI can summarize documents in a matter of seconds with impressive accuracy, for example, whereas a researcher might spend hours on the task (at an estimated $30 to $50 per hour).
But generative AI’s democratizing power also means, by definition, that a company’s competitors will have the same access and capabilities. Many use cases that rely on existing large language model (LLM)1 1 Large language models, also known as foundation models, are deep- learning algorithms that can recognize, summarize, translate, predict, and generate content based on its training data. Today these models are mostly trained on text, images, and audio, but they can also go beyond language and images into signals, biological data, and more. Models trained on data beyond language are called multimodal models. Notes: 1 Large language models, also known as foundation models, are deep- learning algorithms that can recognize, summarize, translate, predict, and generate content based on its training data. Today these models are mostly trained on text, images, and audio, but they can also go beyond language and images into signals, biological data, and more. Models trained on data beyond language are called multimodal models. applications—such as productivity improvements for programmers who use Github Copilot and for marketing content developers who use Jasper.ai—will be needed just to keep pace with other organizations. But they won’t offer differentiation, because the only variability created will result from users’ ability to prompt the system.
Identify the Right Use Cases
For the CEO, the key is to identify the company’s “golden” use cases—those that bring true competitive advantage and create the largest impact relative to existing, best-in-class solutions.
Such use cases can come from any point along the value chain. Some companies will be able to drive growth through improved offerings; Intercom, a provider of customer-service solutions, is running pilots that integrate generative AI into its customer-engagement tool in a move toward automation-first service. Growth can also be found in reduced time-to-market and cost savings—as well as in the ability to stimulate the imagination and create new ideas. In biopharma, for example, much of today’s 20-year patent time is consumed by R&D; accelerating this process can significantly increase a patent’s value. In February 2021, biotech company Insilico Medicine announced that its AI-generated antifibrotic drug had moved from conceptualization to Phase 1 clinical trials in less than 30 months, for around $2.6 million—several orders of magnitude faster and cheaper than traditional drug discovery.
Once leaders identify their golden use cases, they will need to work with their technology teams to make strategic choices about whether to fine-tune existing LLMs or to train a custom model. (See Exhibit 1.)