The business process services industry built its empire on a straightforward premise: concentrate talent, compress costs, and let geography do the rest. For decades, it worked. Then the world stopped being predictable.
Geopolitical fault lines, data sovereignty mandates, and AI-driven disruption have quietly rewritten the rules — and the enterprises still optimizing for stability are the ones most exposed. Anand Sampath, India Head & CEO of BPS at Visionet Systems, operates precisely at this inflection point. With AI-powered platforms reshaping how mortgage and financial services operations are structured and delivered, he argues the real question is no longer where work gets done — but whether you’re operating model was designed to adapt. We find out what that actually means in practice.
India Head & CEO of BPS
Visionet Systems,
CIO&Leader: For decades, the BPS industry was built on a simple proposition: labor arbitrage through geographic concentration. India wins on talent and cost, and everyone’s happy. Is that model now structurally broken? And if the new mandate is resilience-first rather than cost-first, what does that actually cost the enterprise client?
Anand Sampath: I would not say the traditional model is broken, but it is clearly evolving.
For decades, the BPS industry was built around labor arbitrage—leveraging talent concentration, scale, and cost efficiency. Those advantages remain important, particularly in markets like India, which continue to offer deep expertise and strong delivery capabilities.
What has changed is the operating environment. Enterprises are now balancing cost considerations with resilience, compliance, agility, and business continuity. The question is no longer, “Where can work be done most cost-effectively?” but also, “How can operations continue seamlessly in the face of disruption?”
A resilience-first model does require investment in diversified delivery networks, automation, governance, and digital infrastructure. However, enterprises increasingly recognize that the cost of disruption, whether from geopolitical events, regulatory changes, cyber incidents, or operational bottlenecks, is often far greater than the cost of preparedness.
This is where AI and intelligent automation become powerful enablers. Rather than forcing organizations to choose between cost and resilience, technologies such as intelligent document processing, workflow orchestration, and AI-driven operations help deliver both. They reduce dependency on manual intervention, improve consistency, and create operating models that are more adaptable by design.
The future of BPS is not about replacing labor arbitrage; it is about augmenting it with intelligence, automation, and resilience.
CIO&Leader: You work at the intersection of AI and business process services at a time when external forces are reshaping both. In practical terms, how is AI enabling what I’d call “border-light” delivery — the ability to keep operations running when a shipping lane is blocked, a visa regime tightens, or a regional conflict disrupts workforce mobility?
Anand Sampath: AI is enabling a meaningful shift in business process services from geography-bound execution to continuity-led, distributed operations.
In mortgage lending, this is particularly important because the process is highly document-intensive, workflow-driven, and dependent on coordination across multiple stakeholders. Historically, maintaining continuity required significant dependence on specific locations and teams. AI is changing that dynamic.
Three developments are driving this transition.
● First, intelligent document processing platforms such as DocVu.AI are reducing dependency on manual document review by automating the extraction, classification, and validation of information from complex mortgage files. This enables greater consistency, speed, and operational continuity regardless of where work is performed.
● Second, AI-powered workflows are making operations more location-agnostic. Work can be routed, prioritized, and managed across distributed teams without disrupting service levels or loan timelines.
● Third, platforms such as AtClose are bringing greater structure and visibility to the closing process, improving coordination across lenders, title agents, settlement providers, and borrowers. This reduces friction in one of the most stakeholder-intensive stages of the mortgage lifecycle.
The broader outcome is what I would describe as “border-light” delivery. In this operating model, continuity is driven less by physical location and more by intelligent workflows, automation, and digital orchestration. In an increasingly uncertain world, that shift is becoming a strategic advantage.
CIO&Leader: Data sovereignty is becoming one of the most contentious fault lines in global business. With the EU tightening its data localization norms, the US scrutinizing data flows to certain geographies, and India building its own regulatory framework under the DPDP Act, how is Visionet’s BPS practice navigating this regulatory fragmentation in real time, and where is AI helping versus where is it complicating things?
Anand Sampath: Data sovereignty is rapidly becoming one of the defining challenges of global business services. Organizations today operate in an environment where regulations are evolving simultaneously across multiple jurisdictions, each with its own expectations for data residency, privacy, access controls, and governance.
For us, the key is to build compliance into the operating model rather than treating it as a separate layer. Whether supporting mortgage operations, financial services, or other document-intensive processes, we design workflows, controls, and technology architectures that can adapt to regional regulatory requirements while maintaining operational consistency.
AI is helping in several important ways. It improves data classification, enhances traceability, strengthens audit readiness, and enables more consistent governance across complex workflows. For example, platforms such as DocVu.AI help transform unstructured documents into structured, traceable data, making it easier to manage compliance obligations and maintain visibility throughout the process lifecycle.
At the same time, AI introduces new considerations. Questions around model governance, data residency, explainability, and cross-border data flows become more complex when AI systems operate across multiple jurisdictions. The challenge is ensuring that AI does not outpace the governance frameworks required to manage it responsibly.
Ultimately, we see AI as a powerful enabler of both compliance and operational resilience, but only when it is deployed within a robust framework of governance, transparency, and accountability.
CIO&Leader: There’s a version of this story where AI is the great de-risker — it automates processes, reduces human dependency, and makes operations geography-agnostic. But there’s another version in which AI itself becomes a geopolitical vulnerability: model provenance, cloud dependency, and algorithmic bias across jurisdictions. Which version keeps you up at night?
Anand Sampath: The reality is that both versions deserve attention.
AI is already proving to be a powerful de-risking force across business process services and mortgage operations. It improves consistency, reduces manual dependency, accelerates decision support, and helps organizations maintain continuity at scale. We see this every day through platforms such as DocVu.AI, which help automate document-intensive workflows, and AtClose, which brings greater structure and coordination to complex closing processes.
However, what keeps me up at night is not the technology itself—it is the governance surrounding it.
As AI becomes more deeply embedded in business operations, questions around model provenance, data residency, cloud dependency, explainability, and regulatory alignment become increasingly important. Enterprises need confidence not only in the outputs of AI systems, but also in how those systems are trained, governed, and monitored.
In highly regulated industries such as mortgage and financial services, trust is critical. Decisions must remain transparent, auditable, and defensible. That means organizations cannot treat AI simply as an automation layer; they must treat it as part of their risk management framework.
So if I had to choose, the greater concern is not AI itself becoming a vulnerability. Still, organizations are adopting AI faster than they build the governance structures required to use it responsibly. The winners will be those who combine innovation with accountability.
CIO&Leader: India is being positioned — by government, by industry, and increasingly by global enterprises — as the anchor of a geopolitically resilient services ecosystem. But “India as a safe harbor” is a narrative that needs to be earned, not assumed. What does India still need to get right — in infrastructure, regulation, or talent — to truly capitalize on this moment?
Anand Sampath: India has already established itself as one of the world’s most important destinations for business process services, technology, and digital operations. The opportunity now is to evolve from being viewed primarily as a delivery hub to being recognized as a strategic hub for innovation and resilience.
To fully capitalize on this moment, India needs to continue advancing in three areas.
● First, world-class digital infrastructure. Global enterprises increasingly expect always-on operations, secure cloud ecosystems, and resilient technology environments that support mission-critical business processes at scale.
● Second, regulatory clarity and predictability. As AI adoption accelerates and data sovereignty requirements become more complex, organizations need confidence that regulatory frameworks will support innovation while maintaining strong standards for privacy, security, and governance.
● Third, talent transformation. India has an exceptional talent base, but the next phase of growth will require deeper expertise at the intersection of domain knowledge, AI, data engineering, and business operations. The future belongs to organizations that can combine human expertise with intelligent automation.
We see this evolution firsthand in industries such as mortgage and financial services, where AI-powered platforms like DocVu.AI are helping transform document-intensive processes into more intelligent, scalable, and resilient operations.
If India continues to invest in infrastructure, regulatory maturity, and next-generation skills, it has the opportunity not only to support global enterprises but also to shape the future of global service delivery.
CIO&Leader: Supply chain disruptions and energy market volatility are forcing CFOs to re-examine every line of their operating cost structure. In that environment, how do you make the business case for investing in AI-augmented BPS? Is this a conversation about long-term resilience, or are there hard near-term ROI numbers you’re putting on the table?
Anand Sampath: The conversation starts with efficiency, but it doesn’t end there.
In mortgage operations, AI is already delivering measurable near-term benefits. Organizations are seeing reductions in processing cycle times, improvements in document accuracy, lower manual effort, and greater consistency across critical workflows. Platforms such as DocVu.AI help automate document-intensive stages of origination and underwriting, while AtClose helps bring greater coordination and predictability to the closing process.
Those improvements translate into tangible business outcomes—reduced cost per loan, faster turnaround times, improved workforce productivity, and fewer operational exceptions.
However, the strategic value lies in reducing variability across the mortgage lifecycle.
In lending, unpredictability has a cost. Delays affect funding timelines, rework increases operational expense, and inconsistencies affect both borrower experience and business performance. AI helps create a more stable and predictable operating environment, enabling organizations to scale efficiently while maintaining service quality.
So, for CFOs, this is not an either-or conversation between short-term ROI and long-term resilience. The most successful AI investments are delivering measurable operational gains today while simultaneously building a more resilient and scalable operating model for the future.
CIO&Leader: If you were advising a Fortune 500 CXO today who is building their global operations strategy for the next five years — assuming continued geopolitical instability, accelerating AI adoption, and rising regulatory fragmentation — what is the single most important structural decision they need to make about their BPS model, and why are most enterprises still getting it wrong?
Anand Sampath: The single most important structural decision is to build an operating model around adaptability rather than optimization.
For decades, enterprises designed BPS models for maximum efficiency under relatively stable conditions. Work was concentrated in specific geographies, processes were optimized around predictable demand patterns, and success was largely measured by cost reduction.
That environment no longer exists.
Today, organizations must operate in a world shaped by geopolitical uncertainty, accelerating AI adoption, evolving regulations, cybersecurity risks, and rapidly changing customer expectations. In that environment, the most valuable operating model is not necessarily the most efficient; it is the one that can adapt fastest.
This is where many enterprises are still getting it wrong. They are trying to layer AI onto operating models that were designed for a different era. They view AI as a productivity tool rather than a catalyst for rethinking how work is structured, governed, and delivered.
The organizations that will lead over the next five years will combine distributed delivery networks, AI-enabled workflows, strong governance, and domain expertise into a single operating framework. Technologies such as DocVu.AI and At Close demonstrate how intelligent automation can reduce operational dependence on manual processes while improving consistency and resilience across critical workflows.
The future of BPS is not about choosing between efficiency and resilience. It is about building operating models that can deliver both simultaneously. The enterprises that succeed will be those that design for change, rather than optimize for stability.