Many organisations have already taken their first steps with generative AI - in the form of internal chatbots, copilots, and automated assistants.
Peter Wind
Partner
These can create value, but they typically function as reactive systems: they answer questions, generate drafts, and support employees with specific tasks.
Agentic AI marks the next step in the AI journey. Agents can drive workflows and operate in a goal-driven or autonomous manner - planning, coordinating, and executing actions across applications and processes. This means AI is no longer just a support function but potentially becomes an active component in the organisation's core operational infrastructure.
This is strategically significant because value-creating digital processes in an organisation do not consist of a single isolated task, but of connected flows across systems. Data must move between applications, systems, and data silos - where progress often depends on manual handling.
This applies to everything from product and content flows to order, logistics, and production processes, as well as membership administration, application flows, and claims processing. This type of work is not necessarily complicated - but it is fragmented and time-consuming.
But AI agents are not a plug-and-play extension of existing AI initiatives. When AI is given the ability to participate in and support workflows across systems, it requires access to reliable data, clear integrations, and a system landscape that makes it possible to act safely and in a controlled manner.
Agentic AI should therefore not be viewed as yet another AI project, but as a question of enterprise architecture and digital maturity. When AI agents are given the ability to act, integration, data governance, and governance become central competitive differentiators.
When an AI agent can execute actions, the risk landscape changes significantly. Governance therefore becomes a critical prerequisite for being able to deploy the technology in practice. Questions of accountability, audit trails, compliance, and control framework become central because the organisation must be able to document what the agent has done. When is the agent permitted to act autonomously, and when must a human approve? How is the decision rationale documented?
Yet it is precisely the combination of autonomy and governance that makes Agentic AI interesting. When the framework is correctly defined, agent-based solutions can function as an operational accelerator: they can keep processes moving, respond to exceptions, and reduce waiting time in processes where progress would otherwise stall.
Organisations should therefore already be considering Agentic AI as a development that can affect both technology roadmaps and business priorities. The most important first step is not to build an agent, but to understand where autonomy creates real value in your organisation - and where it does not. Agentic AI is particularly relevant where processes span multiple systems, where decisions require context, and where coordination today depends on manual handoffs.
The question is therefore not only which models you choose. The central question is whether the organisation has identified the places where fragmented work, manual handoffs, and system transitions create unnecessary friction - and whether it has an architecture capable of implementing Agentic AI safely and at scale.
