Before AI agents can transform the enterprise, the enterprise has to transform itself
By Pritam Banerjee, Senior Director, The Hartford India
The enterprise conversation around Agentic AI is moving beyond the novelty of autonomous execution. The more important question now is whether organisations are structurally ready for agents to operate across real business processes.
That is a much harder problem than deploying copilots.
Most enterprise work is built around fragmented systems, informal judgement and organisational workarounds. Processes may appear structured, yet critical decisions still depend on people knowing which data to trust, when a policy has an exception, who needs to be consulted and which trade-offs matter in a given situation. As agents move deeper into workflows, organisations have to make much more of this institutional logic explicit.
This is why Agentic AI is increasingly becoming an operating-model challenge.
The first requirement is to redesign work around decisions rather than tasks. The easiest tasks have already been automated in many organisations. The larger opportunity now sits in functions where work spans multiple systems and involves judgement, such as procurement, claims, compliance, finance and other middle-office processes.
Here, the value of agents lies less in performing isolated activities and more in coordinating a sequence of decisions. A workflow may involve retrieving information, reconciling conflicting inputs, assessing risk, applying business rules and escalating exceptions. Breaking this work into clear decision modules allows organisations to determine what can be automated, what can be delegated within limits and where human judgement remains essential.
This also changes how enterprises should think about productivity. Saving 20 or 30 per cent of the time required to complete an individual task matters far less if the overall business process remains constrained by hand-offs, approvals and reconciliation. The more valuable metric is coordination latency: how long it takes an organisation to move from information to decision to execution.
The second requirement is better context architecture. Access to data is insufficient if agents cannot interpret its meaning consistently. Large organisations often carry multiple definitions of customers, products, revenue, risk and operational metrics across different systems. Humans compensate for these inconsistencies through experience. Autonomous systems require clearer semantics.
This places greater importance on metadata, knowledge graphs, ontologies, lineage and shared enterprise definitions. Agents increasingly need to understand where information originated, how current it is, whether it conflicts with other sources and whether they are authorised to use it.
In an agentic environment, data quality therefore becomes continuous rather than periodic. Information is constantly retrieved, transformed, combined and written back into enterprise systems. Without strong provenance and semantic consistency, automation can scale ambiguity as quickly as it scales productivity.
The third requirement is a more precise control architecture.
“Human in the loop” is too broad to function as an operating principle. Human oversight has a cost, and review capacity will become a constraint as agentic workloads expand. Organisations will need to determine where human attention genuinely adds value. Low-risk and reversible decisions can operate with greater autonomy and monitoring. Material, regulated, ambiguous or irreversible decisions require stronger approval thresholds. This demands explicit decision rights around what an agent may recommend, execute or escalate, along with clear accountability when something goes wrong.
In multi-agent environments, errors no longer remain confined to one model. They can travel through connected agents, tools and workflows. Governance therefore has to move beyond evaluating individual model outputs towards monitoring the behaviour of the entire system.
This has implications for organisational ownership
Business functions will need greater responsibility for agent-enabled workflows because they understand the underlying decisions, exceptions and risks. Technology teams will continue to own platforms, identity, security and observability. Risk and compliance functions will require visibility into automated actions and escalation logic.
Treating agents as “digital employees” can obscure this distinction. Agents may execute work, but they do not carry accountability. Responsibility remains with the organisation that defines their permissions, supervises their behaviour and accepts the consequences of their actions.
We can think of agents as a new, separate execution layer within the enterprise.
That shift should also change how value is measured. The success of Agentic AI should increasingly be tied to cycle time, service levels, working capital, risk reduction, customer outcomes, revenue impact and operational resilience. Metrics such as number of agents deployed or hours saved will reveal very little about whether the organisation itself is performing better.
As models and agent platforms become more accessible, technology alone will become less differentiating. The harder asset to replicate will be an organisation’s encoded understanding of how its decisions are made: its rules, context, institutional knowledge, exceptions, permissions and accountability structures. That may become the real competitive advantage of the agentic enterprise.
Before organisations can automate more of their work, they will have to understand and redesign that work with far greater precision.