Generative AI taught organisations that machines can write, summarise and answer questions. Agentic AI is the next step: software agents that pursue a goal, decide which steps to take, use tools and complete work. Gartner has predicted that by the end of 2026, 40 percent of enterprise applications will include task specific AI agents, up from less than 5 percent in 2025. For leaders, the question is no longer whether agents will arrive, but how to adopt them responsibly.
Where agents create value first
- Employee services. Resetting access, provisioning software and answering policy questions end to end.
- IT operations. Gathering diagnostics, correlating alerts and preparing incident summaries.
- Customer service. Resolving routine requests and handing complex cases to people with full context.
- Back office. Reconciling records, preparing reports and routing approvals.
A governance model for agents
Define the scope of authority
Every agent needs a clear list of what it may do alone, what needs approval and what is forbidden.
Act through governed workflows
Agents should operate through the same service and approval workflows as people, so every action is logged, auditable and reversible.
Ground agents in curated knowledge
Accuracy depends on current, well owned knowledge and data. Investment here pays back across every AI initiative.
Measure outcomes, not activity
Track resolution rates, accuracy, user satisfaction and cost per outcome, and compare against a baseline.
Getting started
Start with two or three high volume, low risk processes, prove value with real users and expand deliberately. Our AI Use Case Discovery Workshop identifies the right starting points, and our Agentic AI services take them into production.
