By Bharat Chadha, Partner – Tech Consulting, Uniqus Consultech
If you walked into a boardroom in late 2025, the mood surrounding AI was mixed. There was expectancy, but also exhaustion. The Copilot era had saturated the enterprise. Employees had sidebars summarizing emails. Developers worked alongside assistants who completed code. Almost every function had a GenAI champion and a dashboard to support it.
For many CFOs, though, the unit economics still felt slippery. A familiar pattern emerged, teams layered AI on top of broken workflows and celebrated because everything moved faster, even though the underlying problems remained the same. The hard reality is that AI does not fix a process. If your process is good, AI can improve it. If it is broken, AI simply helps the breakage happen faster.
That realization marks the close of the experimental chapter. In 2026, the conversation shifts from curiosity to ownership. The question is no longer, “What can this model do?” It is, “Which business outcome can this system reliably own, and who is accountable when it fails?”
From Tools to Autonomy: The Agentic Shift
In 2025, AI wrote, summarized, and searched. In 2026, AI executes.
We are moving from assistants to operators. These systems not only suggest the next word but also execute the next step. This is the first real step toward the Agentic Enterprise.
At its core, an AI agent is a digital worker designed for autonomous execution within a defined sandbox. It perceives a request, evaluates it against a playbook, and interacts directly with business systems through APIs to complete a task end-to-end.
The shift is perhaps most visible in large-scale customer experience operations. In a traditional model, a payment dispute might involve several manual steps and significant wait times. Today, enterprises are deploying agents that can verify policy eligibility, retrieve transaction history, trigger refunds within specified thresholds, and update the CRM and finance systems in real time. Anything that falls outside the playbook goes to a human. Every step is logged, and high-impact actions require explicit approval. What changes is not just speed. The KPI shifts from average handle time to resolution without rework. Over time, an organisation doesn’t just automate tasks; it redesigns the workflow, so humans focus on policy design, exception handling, and customer retention.
A similar shift is unfolding in finance operations. The objective has shifted from touchless finance to “finance by exception.” Early agentic deployments are not glamorous. They are practical, focusing on tasks such as invoice matching, cash application, vendor follow-ups for missing documentation, and assembling audit evidence. Agents handle the routine reconciliation steps and prepare audit-ready trails, while humans intervene only when confidence drops, rules collide, or controls are at risk. What changes most is latency. Teams stop burning human time on data collection and start focusing on the few issues that move results, improve collection strategy, dispute root causes, and improve controls.
In IT and security operations, the leap is one of speed and containment. The traditional model relies on alerting a human to a problem, while the agentic model relies on the system taking the first defensive steps. An agent correlates signals across logs and endpoints, drafts a diagnosis, executes a predefined containment playbook (like isolating a device or forcing credential resets), and escalates to a human when the situation deviates from the decision tree. This system is designed to reduce the “time to contain” while preserving accountability through a human in the loop. It ensures that speed never comes at the cost of oversight.
Successful adoption of digital operators requires a disciplined approach. Organisations should start with a limited use case, monitor outcomes closely, and scale only after thoroughly testing controls. These agents are not implemented merely as productivity tools; their effectiveness is evaluated based on throughput, error rates, cycle time, and their impact on profit and loss.
This will also develop the talent story of 2026, not that AI replaces humans, but that AI pushes humanity upwards. The organisations that win will view this as a redesign of roles, incentives, and accountability, rather than a tooling upgrade.
The New Risk Surface
As AI moves from analysis to execution, in 2026, the risk shifts from “hallucination” to transaction integrity: a flawed decision can be posted to the ledger, trigger approvals or payments, and only surface later as a control exception.
In this new agentic landscape, “confident but wrong” takes on a dangerous dimension. In a finance department, an autonomous agent could misapply cash, approve an illicit credit memo, or inadvertently violate the segregation of duties by both initiating and validating a transaction it was never meant to bridge. In customer operations, a logic error in a refund agent doesn’t stay small; it scales instantly, multiplying into mass churn or systemic regulatory non-compliance. In security, a miscalculated containment move by an autonomous bot does more than create noise; it can trigger catastrophic downtime for critical infrastructure.
Beyond internal errors lies a more calculated threat: instruction or prompt hijacking. Because these agents must read external data, such as emails, tickets, and web pages, to function, they are vulnerable to hidden prompts designed to override their core programming. This is uniquely dangerous compared to traditional software. A poisoned instruction in an agentic system doesn’t just produce a biased answer; it triggers a malicious action. In a system with the keys to the kingdom, a single hijacked prompt can authorize a fraudulent transfer or exfiltrate sensitive data under the guise of a routine task.
That’s why the winners in 2026 won’t be the firms that automate the most. They’ll be the firms that can audit automation, enforce hardcoded limits on what agents can do, maintain a trustworthy record of every action, secure approvals for high-impact decisions, and assign a named human owner.
Global Architecture and the Regulatory Mesh
The free trial is over. AI is no longer a side experiment; it’s entering core operations, which means compliance can’t be an afterthought. In India, this becomes especially important because many workflows executed here affect customers, financial outcomes, and controls in other markets.
In 2026, the real question isn’t whether regulation matters. It’s whether your operating model can keep up. Most large firms now operate within a complex regulatory web. So, companies using AI agents need to meet the strictest expected standard to avoid redesigning them country by country. Companies also need to turn data governance into engineering, where consent, security, logging, and user rights handling become part of the system and part of every release checklist.
Geopolitics is now a design constraint. Agentic AI scales on compute, and compute is no longer just cloud. It’s chips, power, location, and jurisdiction. In 2025, cloud choices were primarily driven by cost and performance considerations. In 2026, where your workloads run becomes a legal and risk decision. Certain workloads may remain in hyperscale environments where latency and service reliability are crucial, while sensitive workloads stay hosted in sovereignty-focused environments.
Hybrid is now a strategy for resilience. And once agents move to executing, the question of where they run becomes inseparable from whether they are allowed to run.
The Leadership Playbook for 2026
If 2025 rewards experimentation, 2026 rewards discipline. Leadership must pivot to an operational mindset.
1. Standardize Patterns, Not Just Tools
Leaders must stop letting departments assemble their own AI stacks. Standardized patterns: approved models, approved data pipelines, approved agent frameworks, and reusable guardrails. If a solution does not fit the platform, it should not ship.
2. Apply Capex Rigor
Agentic AI is operational infrastructure and demands capex-like rigor. Quantify throughput gains, error-rate tolerances, control costs, and failure modes. If an initiative cannot prove it will lower the cost of doing business or prevent expensive failures by 2027, it should not be scaled.
3. Design Accountability Before Autonomy
Boards and regulators will ask: “Who is responsible when the agent makes a mistake?” If you cannot point to a human owner and specific control processes, an organisation is gambling, not industrializing
Conclusion: Earning the Seat
2025 made AI feel ubiquitous; 2026 will make it consequential. As agents move from drafting to doing, competitive advantage will not come from who demos fastest. It will come from those who can industrialize safely with standardized patterns, enforceable boundaries, and architectures that withstand regulatory and audit scrutiny.
Most firms will not fail because agents cannot work; they will fail because they cannot prove to be safe.
The experiment is over. Now, AI must earn its seat on the P&L.
Here is the question every CEO and CFO should ask this year: What is your minimum viable governance to let an agent touch money, customers, or controls, and how quickly can you build it?