Agentic AI is moving enterprise AI into a more consequential phase. Earlier tools largely supported people with recommendations, summaries, drafts, and analysis. Agentic AI goes further. It plans steps, calls tools, accesses systems, and triggers workflows with limited human involvement.
For leaders, this raises a sharper question: which processes should be given autonomy, which should remain AI-assisted, and which need redesign before agents are introduced?
The pace of change adds urgency. Frontier labs continue to release models with stronger reasoning, larger context windows, and faster execution. Enterprises need to keep experimenting while applying discipline to how AI diffuses across the organisation, with clear principles for governance, architecture, cost, performance, and trust. Adoption intent is high, but enterprises are brownfield environments with years of legacy data, infrastructure, and processes. You cannot simply flip a switch.
Deloitte's State of AI research captures the gap. By 2027, 74% of respondents expect their companies to use AI agents at least moderately. Yet only 21% report having a mature governance model for autonomous agents. That distance between adoption intent and governance maturity deserves close leadership attention.
Automation Readiness Is Not Agentic Readiness
Many organisations still assess AI opportunities through an automation lens, looking for high-volume, rules-led, repeatable work. That lens served traditional automation well, but agentic AI requires a broader test.
Automation readiness asks whether a task can be repeated efficiently. Agentic readiness asks whether delegated action can happen safely within defined boundaries. An agent given a task without a clear goal may optimise for the wrong outcome. Unclear accountability leaves ownership of consequences contested. Vague decision boundaries allow the agent to act beyond the enterprise's intended risk appetite.
Many processes with high manual effort, weak data, frequent exceptions, and informal decision-making look like obvious candidates for agentic AI because people spend significant time on them. Deploying an agent in such an environment often shifts the burden rather than removing it, into monitoring, exception handling, audit, customer explanation, and remediation. Human oversight is essential, but it cannot compensate for poor process or weak solution design.
The Hidden Costs
The first hidden cost is operational complexity. Agents added to poorly designed processes often create extra work around the existing process. Teams still need to check outputs, fix data issues, validate actions, manage exceptions, and explain decisions. Manual effort may reduce in one part of the process, but oversight effort increases elsewhere.
The second hidden cost is architecture debt. Agentic AI depends on strong underlying systems. Agents access data, call APIs, trigger workflows, and act based on permissions and business rules. If the data is inconsistent, integrations are weak, or access controls are unclear, the agent will struggle to perform reliably. The organisation then has to invest later in cleaner data flows, governed APIs, identity controls, audit trails, and escalation paths.
The third hidden cost is poor cost-performance choices. Enterprises need to decide which use cases require frontier models, which can use smaller models, which are better suited to traditional automation, and which should remain human-led. More powerful models can improve reasoning, but they also increase cost, latency, and monitoring effort. Leaders need to understand the ‘Tokenomics' and match the model to the value and risk of the process. Do not use a Rolls Royce where a cab will do.
The fourth hidden cost is governance burden. Agentic AI needs controls over decisions and actions beyond the underlying model. Leaders must be clear on what the agent is allowed to do, which systems it can access, when it must stop, when it must escalate, and who remains accountable for the outcome. Deloitte's Trustworthy AI framework reinforces the same principle: AI systems should be private, transparent and explainable, fair and impartial, responsible, accountable, robust and reliable, safe and secure. For agentic AI, these principles need to translate into practical operating controls such as decision boundaries, access rights, audit trails, human escalation, continuous monitoring, and accountability for every material action. Without these guardrails, governance gets added after deployment, making control design slower, costlier, and harder to manage.
The fifth hidden cost is trust erosion. Employees, customers, and regulators judge agentic AI through real experience. Employees lose confidence when agents evaluate work without transparency. Customers lose confidence when service outcomes change without explanation. Regulators lose confidence when the enterprise cannot explain who approved the decision logic, what data was used, and how human intervention was enabled.
For Indian enterprises, this carries added significance. Digital adoption is accelerating, regulation is maturing, and questions of data sovereignty, consent, and sector-specific rules are becoming central to AI adoption.
A Readiness Check Before Autonomy
Early warning signs usually show up before deployment. A use case should remain AI-assisted when the outcome is unclear, accountability is diffused, decision boundaries are vague, data and integrations are weak, model cost is disproportionate to value, or the cost of error is high.
In these situations, AI can still play a valuable role. It can support judgement, surface insights, prepare recommendations, and accelerate execution, while humans retain control over decisions and outcomes. Greater autonomy can come later, once the process is cleaner, better governed, and more accountable.
Agentic AI can improve speed, productivity, customer experience, and operating leverage. It can also become expensive and risky when deployed into processes that lack goals, ownership, boundaries, or trust controls. With agentic AI, it is not about being first. It is about being right. That is how enterprises can capture the value of agentic AI while ensuring autonomy scales with readiness.
Ashvin Vellody is the Chief Strategy & Innovation Officer for Consulting in Deloitte South Asia.
Disclaimer: The views expressed in this article are solely those of the author and do not necessarily reflect the opinion of NDTV Profit or its affiliates. Readers are advised to conduct their own research or consult a qualified professional before making any investment or business decisions. NDTV Profit does not guarantee the accuracy, completeness, or reliability of the information presented in this article.
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