Organizations weighing the adoption of personal AI agents in the workplace face a trade-off: enterprise agents deliver scale by operating across workflows, while personal AI agents let individual workers tailor AI to their specific tasks and ways of working.
Enterprise agents offer an important advantage: scale. Because they are designed to operate across workflows, their value depends less on individual workers and teams deciding whether or how to use them. That breadth can also be a limitation, however, since enterprise agents may not account for the specific work individuals and teams do or the different ways people approach that work.
Personal AI agents: when to choose them
Personal agents, developed by individual workers and deployed primarily on the local desktop to enhance individual productivity, can help fill that gap by allowing workers to tailor AI more closely to their own tasks and ways of working. Encouraging use of personal agents could help organizations move faster toward integrating agentic technology into workflows and, by enabling workers to create and manage their own tools, could help attract and retain differentiated talent in an AI era. As individuals develop preferences around AI tools in their own lives, being able to develop and use personal agents could also enable workers to perform better on behalf of the organization. Workers on the ground may be best positioned to develop agentic solutions to augment work they are already doing.
Governance challenges for personal AI agents
Giving workers latitude to develop and use personal agents introduces governance challenges. An agent used by one employee for a limited task raises different issues from one that can connect to enterprise systems, access an organization’s data or interact with customers. An agent built entirely within an organization’s approved technology environment poses different risks from one a worker brings from outside the organization.
Governing agents is already proving to be a challenge for many organizations. According to Deloitte’s 2026 State of AI in the Enterprise, only one in five surveyed companies has a mature governance model for autonomous AI agents, despite three-quarters planning to deploy them within two years. Additionally, a separate Deloitte survey of 500 senior managers and above found that 70% of respondents viewed the inability to trust and govern agents as a barrier to adopting agentic technology.
To weigh opportunities and trade-offs, organizations can first consider whether a particular use case is better suited to an enterprise agent or a personal agent. If a use case cannot be addressed by enterprise-level agents, organizations can consider two approaches to personal agents: workers might bring their own external agent to address the use case or build an agent within enterprise frameworks (figure 1).
