Reinventing the Organization for GenAI and LLMs

Consider this an early eulogy for the traditional organizational structure, which began in 1855 with the first modern organizational chart and thrived, more or less successfully, until the 2020s, when it succumbed to a new technology, the large language model (LLM).

This is, of course, a bold claim. While traditional approaches to organizing have been frequently threatened by technological advancements (anyone remember the brief craze for holacracy?), organizations have proven remarkably durable. In fact, each new wave of technology ushered in innovations that strengthened traditional organizations. Henry Ford took advantage of advances in mechanical clocks and standardized parts to introduce assembly lines and more precise ways of dividing work. In 2001, agile development took advantage of new ways of working with software and communicating via the internet to revamp how organizations develop products. Technology breakthroughs and organizations have long been partners.

But generative AI, and the LLMs that power it, are different. Every previous method of organizing was intensely human, built on human capabilities and limitations. That is why traditional organizational models have persisted for so long. Human attention remains finite, so we needed to delegate our tasks to others. The number of people who can work in a team is limited, so we needed to break organizations into smaller parts. Decision-making is complicated, so we embraced layers of management and authority. The technology changes, but workers and managers are just people, and the only way to add more intelligence to a project was to add people or make them work more efficiently through tools that helped them communicate or speed up their work.

But this is no longer true. Anyone can add intelligence, of a sort, to a project by including an AI. And evidence shows that people are already doing so — they just aren’t telling their bosses about it: A fall 2023 survey found that over half of people using AI at work are doing so without approval, and 64% have passed off AI work as their own.

This shadow AI use is possible partly because LLMs are uniquely suited to handling organizational roles — LLMs work at a human scale. Tools based on LLMs can read documents and write emails and adapt to context and assist with projects without requiring users to have specialized training or complex, custom-built software.