Create Generative AI Value at Scale

Generative AI presents an organizational puzzle. Businesses have collectively invested billions of dollars to give employees access to general-purpose large language models (LLMs) to enhance personal productivity while in most cases struggling to develop and adopt more strategic applications of the technology. Meaningful return on investment, much less competitive advantage, is likely to remain elusive unless companies can use GenAI to make innovative process improvements that scale across functions and business units.1

Our interviews with 87 practitioners in 23 large organizations revealed that leaders who scale value creation with generative AI cultivate three key practices. First, they expand the scope of use cases across processes rather than remaining focused on a specific task. Second, they treat each use case as a work in progress to be continually improved. And third, they quickly identify and abandon use cases that fail to bring measurable value to the organization.

However, most traditional companies are not structured to institutionalize these three practices. Many operate as multidivisional organizations characterized by multiple profit and loss units with duplicated functions, limited cross-functional information flow, and internal competition for resources.2 This setup makes it difficult to scale generative AI use cases across processes and units.

In our research, we found that the few leaders who are overcoming these challenges are moving beyond the classical hub-and-spoke models that many organizations have used to connect centralized AI technical expertise to each unit. They are developing a new kind of internal resource that we call the AI spine. It provides a flexible core structure for implementing, evolving, and abandoning LLM use cases at scale, keeping the generative AI portfolio both focused and current.