Agentic AI Rewrites the Fundamentals of BPO, But Risks are Lurking

When ChatGPT burst onto the scene in 2022, alarm bells rang across the BPO industry. Millions feared job losses to automation — but most believed only low-skill, repetitive roles were at risk. Today, Agentic AI appears poised to deliver a far more devastating blow — to the very foundation of the BPO business: labor arbitrage.

Late last year, Mumbai-based BPO giant WNS Global Services offered a glimpse of what that future looks like. It built an agentic AI platform for its UK client, Animal Friends Insurance (AFI) — a move that was both transformative and threatening to traditional outsourcing.

The platform almost automated the insurance processing, reducing the time by more than 65%. Considering a YouTube video posted by NASSCOM, India’s IT lobby group, the platform reduced the processing time dramatically.

That is because Agentic AIs of this kind read customer claims, interpret the fine print hidden in dense policy documents, check historical records, evaluate validity, and make the final call — approve or reject. And they don’t stop there. They also trigger payments and send customer notifications on their own. From start to finish, the Agentic AI functions like a fully autonomous worker — a self-driving claim processor.

How Agentic AI Deals Differently with BPOs?

Agentic AI is not Gen AI platform like ChatGPT, which waits for prompts. Nor is it robotic process automation, which mindlessly follows pre-coded scripts. Agentic AI thinks. decides. and acts on it own. In other words, it is like a smart, experienced employee—one that never sleeps or gets tired.

“Agentic AI has redrawn the blueprint for BPOs; what was once labor-intensive, tiered support is now increasingly autonomous,” says Vinod Goje, an AI expert at Bank of America.

“Entire tiers of human agents are being replaced, not augmented,” Goje confirmed, dismissing the notion that AI is simply helping humans do their jobs better.

“In voice-based centers, we’re not just seeing call deflection; we’re seeing full-resolution conversations handled end-to-end by AI agents that can think, decide, and act.”

Dave Trier, VP of Product at ModelOp, offers a glimpse of how human roles might evolve: “Contact center agents may evolve into supervisors of AI agents — handling edge cases, refining responses, and ensuring quality.”

Chicago, Illinois-based ModelOp provides AI lifecycle automation and AI governance software for large organizations.

When asked whether Agentic AI might make future BPO offices look like a Chinese factory run by robots, Trier replied, “Not quite.”

“Agentic AI is an application — not hardware — so the change will be more operational than visual.”

“The ‘robots’ will be invisible and they’ll increasingly handle tier-1 support, simple claims, and transaction processing without ever alerting a human.”

An Unprecedented Transformation

There’s no doubt that the arrival of Agentic AI has triggered a paradigm shift in the BPO industry. What was once outsourced to human agents abroad may soon be “insourced” through AI within companies’ own walls.

“It is shifting from outsourcing to AI-driven insourcing,” wrote Souren Sarkar, CEO of Miami-based BPO firm Nexval Group, in a LinkedIn post.

“(Agentic) AI can scale operations exponentially without requiring additional workforce hiring, training, or management—an area where BPO providers traditionally thrived.”

Sarkar adds another advantage: compliance. Traditional outsourcing often involved sharing sensitive data across borders. Agentic AI, on the other hand, can be deployed locally, within the client’s infrastructure — minimizing risk.

Considering his argument, large firms will likely develop and deploy their own agentic AI platforms. But smaller firms — those unable to afford or build such tools — may still rely on conventional outsourcing.

The Hidden Risks of AI Agents

Despite all the promise, Agentic AI comes with serious risks — especially because it acts on what it learns and thinks.

“Agentic AI can take action based on what it learns, which means a flawed instruction or misinterpreted feedback could create a cascade of errors,” Trier said.

“For example, if an agent learns that escalating fewer tickets is ‘better,’ it might begin suppressing real issues — hurting customers and SLAs.”

“In layman’s terms: giving an Agentic AI the wrong rule is like sending a new employee to do a job with bad training — and letting them make decisions unsupervised. Without oversight, you may not catch the mistake until hundreds of customers are affected.”