Transforming Agentic AI: From Initial Trials to Successful Implementation

The transition from experimentation to enterprise-wide adoption of agentic AI is becoming more complex than what the early enthusiasm suggested. While many organizations have launched pilots to explore autonomous and semi-autonomous AI agents, very few have managed to operationalize them at scale.

Industry research underscores this gap between ambition and reality with only a small fraction of enterprises having embedded AI agents deeply into their operations, while a modest segment has achieved limited deployment. According to a study done by Capgemini, only 2% organizations have deployed AI agents at scale and 12% at partial scale. The remaining majority have either launched pilots or are exploring deployment.

Technology leaders often underestimate the non-technical work required, governance, risk ownership, change management, cost controls and incentives, assuming that scale is a linear extension of a PoC, when it’s an organizational redesign problem.

For most, agentic AI remains confined to proofs of concept (PoC). Industry projections indicate a significant share of the agentic AI initiatives being abandoned over the next few years, largely because costs spiral faster than expected, tangible business outcomes remain elusive or risk frameworks fail to keep pace with autonomy. Gartner estimates over 40% of agentic AI projects to be scrapped by the end of 2027.

Agentic AI Success Factors

Agentic AI success is largely determined by the use case selection, orchestration between humans and AI agents as well as integration feasibility, governance, trust and cultural adoption.

“The success of agentic AI initiatives ultimately depends on how deeply they are engineered into the core of the platform. “The transition from pilot to production requires trust built through transparency, deep integration with core workflows as well as robust governance around security, compliance, and continuous learning,” says Ramkumar Venkatesan, CTO, Cashfree Payments.

Deepak Gupta, Group Chief Technology & AI Officer, Automotive Manufacturers Pvt. Ltd. weighs on the probability of successfully scaling up right at the start based on whether the economics works at 100x scale. Calling this non-negotiable, he adds, “We model fully loaded costs, including inference, infrastructure, human oversight, exception handling as well as maintenance and compare against the realistic alternative. If the math doesn’t work at production volume, I don’t care how impressive the demo is. Demos don’t pay for themselves.”

Use Case Selection

A common reason for agentic AI initiatives failing is wrong use case selection, often driven by hype and peer pressure instead of business priorities. Organizations must be prudent in selecting the agentic AI use cases with early efforts limited to scenarios where returns are explicit and defensible.

Ajay Khanna, VP, VE Commercial Vehicles, finds value in the processes that are streamlined and with a defined action execution within the guardrails. “Areas which require considerable emotional intelligence for decision making may not classify as strong use cases. Good use cases could be in the areas of fraud detection and security intelligence,” he explains.

At Cashfree, the process involves evaluating and prioritizing agentic AI use cases at the intersection of high-frequency decision making, measurable business impact and system-level ownership. “Areas such as fraud detection, merchant onboarding, customer support and operational workflows lend themselves naturally to agentic systems because they require speed, accuracy and consistent judgment at scale,” adds Venkatesan.

Measuring Outcomes

For Khanna, a must do activity before initiating a POC is to identify the value against the use case and methodology for measurement of value or KPI’s need to be put in the plan to ensure return of investment.

With a slightly different take, Arvind Sivaramakrishnan, CTO, Asia Healthcare Holdings, insists that the main purpose of agentic AI initiatives is to ease the complex and sometimes intensive user actions to achieve a process outcome. Hence design, development and deployment must fundamentally engrain the purpose of the outcome and not isolated efficiency metrics.

To justify the investments, use cases must meet the following criteria:

• Solve a real business problem.
• Meet the company’s strategic objectives.
• Lead to measurable business outcome/impact.
• Bring value that outweighs the cost and complexity.

Human Oversight & Guardrails

Having discovered the right use case and clear success metrics determined, technology leaders stress on the importance of human-in-the-loop guardrails as AI agents must be as accountable and reliable as the systems they are embedded in. The success lies in designing human oversight into the system and not added as an afterthought.

Every human intervention, including every override, correction or escalation is a signal and marks the boundary of current agent competence. Gupta advises capturing these signals systematically, analyzing them and using them to improve agent capabilities and refine governance rules. The goal isn’t to eliminate human involvement but to make it more strategic and less of a routine.

“Care and consideration should be given and deployed for boundary conditions or rare events that might throw the agentic to go out of action and that failure has to be seamlessly resolved by humans for the rest of the value chain to not remain disrupted . This is what we will do with an all human deployment in a work process and the same will have to be replicated,” Sivaramakrishnan advises.

Foundational Elements

According to Harnath Babu, Partner & CIO, KPMG India, scaling agentic AI isn’t about better prompts or smarter agents; It’s about rethinking how decisions are made, owned, audited and improved at enterprise scale. And, that is where most organizations are still catching up.

Here are some fundamentals that organizations must have in place to ensure a smoother ‘pilot to production’ transition.

• As organizational structures and human accountability undergo change, the right blend of human and AI becomes extremely critical.
• Rethinking workflows with agentic AI from the ground up is an ideal path to successful implementation as integrating the agents into legacy systems can disrupt workflows and require costly modifications.
• As PoCs start to scale, for agents to execute tasks responsibly is as important as executing them autonomously. A strong governance framework that defines the guardrails becomes non-negotiable.
• Focus on delivering results every few weeks so that the interest stays and the sponsorship continues.