We are on the cusp of a new wave of hybrid work where organizations won’t just mix in-person and remote workers—they’ll pair humans and AI agents as co-workers. These AI agents will have the ability to take and act on decisions independently and will not be reliant on detailed user inputs, as today’s mainstream GenAI tools are. For example, they will be capable of interpreting context, adapting dynamically to new information, independently ideating, and even partnering with human colleagues to tackle complex and varied tasks.

AI agents are set to go beyond simply augmenting humans to being true co-workers alongside us. By combining human and AI capabilities, these hybrid teams promise to create new possibilities to deliver competitive advantage far beyond incremental productivity gains. This coming shift also demands thoughtful leadership to balance human workers and AI technologies to ensure the unique strengths of each are maximized.

The new hybrid

In large global organizations, many workers already find themselves collaborating through Slack or Microsoft Teams with colleagues they have never spoken to, let alone met in-person. Even with close colleagues, these real-time digital interactions often outnumber face-to-face meetings. Today, there is another human at the other end of those interactions, providing their expertise or performing a specific task. While many workers have already begun incorporating GenAI tools, like ChatGPT, to help with targeted analyses and tasks, the increasing maturity of AI will take this relationship a crucial step further: rather than being a tool or aide to existing human workers, the AI agent will become the “coworker” on the other end of those digital interactions.

This emerging hybrid workforce has been made possible by advances in the natural language processing of large language models (LLMs) that enable humans to communicate with AI agents in the same way they would with a human team member. The reasoning capabilities of LLMs allow natural language instructions to be translated into action without the need for prescriptive code or detailed instructions, or even well-defined steps. Inputs can be more notional, and the AI coworker can still develop and execute a plan, coming back for feedback as needed.

In many ways, the interactions of humans and AI colleagues will be analogous to human passengers in self-driving cars. The cars require a destination, but not specific instructions on when to brake or accelerate. Self-driving cars plot a course, but also receive new data about their surroundings, processing it to plan and execute actions. AI coworkers will be able to act similarly: interpreting context, interacting with other tools and external systems to develop a plan, and even making certain decisions autonomously. They will also maintain task memory so they can learn and improve on the jobs they do regularly.