Editor’s note: The following is a guest post from Autumn Stanish, director analyst at Gartner.
AI agents are emerging as one of the disruptive forces in digital workplace IT operations.
Beyond automating routine tasks, their real impact comes from the ability to make independent decisions and act within dynamic, unstructured environments. As automation expands, AI agents will unlock new opportunities for IT functions to operate with minimal human intervention.
Nearly one-third of organizations will achieve autonomous operations for 80% of their digital workplace services by 2030, up from 0% in 2025, according to Gartner predictions. Managing agents will require strong governance that balances costs, risks and benefits of AI agent-driven automation.
CIOs must urgently identify high‑value use cases, evaluate AI effectiveness and ensure digital workplace teams have the skills needed to work alongside these agents. Building these capabilities will ensure successful implementation and long‑term value of AI agents in digital workplace IT operations.
Where agents come in
AI agents are poised to automate a wide range of digital workplace processes because they can perceive their environments, make decisions and take actions to pursue goals autonomously or nearly autonomously.
Unlike traditional rule-based automation, agents are capable of managing complex, unstructured and nondeterministic workflows, making them ideal for unpredictable IT environments.
Many digital workplace IT processes are simple and repetitive, yet the inability to integrate disparate tools have become a barrier to automation. CIOs must examine where AI agents can complement existing trigger‑based automation to overcome this limitation.
A phased approach is essential to success. CIOs need to evaluate current capabilities, vendor maturity and automation gaps to determine which use cases are realistic today. While AI agents show strong potential, maturity is still developing.
In early stages, low‑impact, routine activities handled by digital workplace services teams are most likely to become autonomous. CIOs must first identify which low-impact tasks could be automated either through existing trigger-based automation or AI agents.
The next step is to evaluate the maturity of AI agents for IT operations within digital workplace tools, work with vendors to outline roadmaps and costs, establish a dedicated automation function and invest in staff training for AI agent development and configuration.
CIOs must also put together a solid business case that accounts for costs, security, operational complexity and employee experience. Staffing reallocations or reductions must be avoided until AI agent efficacy is known.
Governance for AI‑driven operations
AI agents in digital workplace services are forcing CIOs to rethink governance frameworks.
While AI agents promise major efficiency gains, they also introduce new operational, security and compliance risks that require governance aligned with existing IT structures. Gartner research shows that 84% of IT leaders agree that additional technical controls will be needed to manage, govern and secure AI agents.
CIOs must partner with enterprise leaders to build governance practices that directly address the risks associated with AI agent adoption in digital workplace IT operations.
Governance should cover the full AI agent lifecycle, including selection, design, testing, deployment, updates, performance audits and decommissioning. Tech chiefs also need tight alignment with AI leaders, security teams, in‑house developers and vendors to ensure AI governance caters to IT operations use cases.
Clear ownership of workplace products, structured governance boards and updated change management policies will ensure AI agents operate autonomously within safe, trusted boundaries.
Developing AI‑ready skills
The introduction of AI agents in digital workplace services is enabling advanced capabilities such as predictive analytics, adaptive training and agent‑assisted troubleshooting. These shifts require digital workplace teams to update their skills and methods to maintain operational efficiency, manage costs and improve user experience.
The rise of multiagent architectures will further increase demand for advanced engineering skills as AI agents interact with traditional tools in new ways.
CIOs will need to manage significant reskilling but can also expect a reduction in effort across traditional digital workplace support and operational activities. The type of engineering work required for digital workplace IT operations will shift and may decrease over time.
Meanwhile, engineering and enablement roles that focus on enabling AI agents for business users will become increasingly important.
To build these capabilities, CIOs must identify engineering‑level staff who can transition into AI agent specialist roles and plan to align some of them to IT operations use cases and others to business use cases.
Creating a safe learning environment will allow engineers and technicians to practice the skills required for designing, deploying and controlling AI agents. CIOs must facilitate a minimal, cloud‑hosted, non‑production sandbox environment to support this skill development without introducing any operational risk.
