Q1. Could you start by giving us a brief overview of your professional background, particularly focusing on your expertise in the industry?

I have over 30 years of experience in finance transformation, shared services, strategic operations, and business finance, working with both multinational and Indian organizations.

My expertise includes driving finance transformation, establishing world-class financial controls, and leading end-to-end process enhancements across P2P, O2C, and R2R, with a strong focus on digitisation, automation, and governance.

I have held leadership roles at Walmart India, Whirlpool, Hindustan Unilever, Wadhawan Retail, and Quintes Global. In addition to my consulting work in business transformation, I founded CXO Emerge Pvt. Ltd., which provides mentoring and coaching for mid-career professionals, and WOW CFO Services, a virtual CFO platform that supports organizations with finance strategy, compliance, and performance management.

I have led strategic initiatives to improve profitability, reduce costs, and strengthen business resilience. This includes deploying RPA and automation programs that deliver significant annual savings and transforming finance operating models to prepare for future needs.

My approach combines finance expertise, strategic planning, and change leadership, with a focus on measurable value, compliance, and sustainable operational excellence.

 

Q2. How rapidly are finance workloads moving from labor-led execution to AI-first delivery, and what percentage of total F&A activities do you expect to be automated by 2027?

By 2027, I believe we’ll see about 25 to 35 percent of Finance and Accounting tasks in mid-to-large companies handled automatically. The most repetitive work—like accounts payable, accounts receivable, reconciliations, invoice processing, and routine period closes—will be even more impacted, with 40 to 60 percent of those activities running straight through or supported by AI. This shift will free up finance teams to focus more on strategic analysis and business partnering, rather than manual processes.

This outlook shows just how quickly organizations are embracing new technology. To put it in perspective, a recent Gartner survey found that 59 percent of finance teams were using AI in 2025—up from only 37 percent in 2023. That’s a remarkable jump in just two years.

Actual adoption will depend on factors such as data quality, the maturity of legacy ERP and finance systems, regulatory and compliance requirements, and the organization's approach to automation.

 

Q3. How is GenAI reshaping managed services economics—pricing per FTE, SLA structures, staffing pyramids, and vendor margin expectations—and which delivery models appear most financially resilient over the long term?

GenAI is changing managed services economics. Vendors are shifting from headcount-based pricing to platform-led and outcome or subscription pricing, with smaller digital FTE overlays. This is reducing traditional FTE rates.

SLA structures are moving from time and response metrics to outcome and value-based SLAs. New SLAs now include model accuracy, error rates, and data governance.

Staffing models are changing, with fewer entry-level batch processors and more roles focused on AI operations, data engineering, and exception management. The workforce is shifting toward supervising higher-value exceptions and managing AI agents that learn continuously.

Vendor margins are under pressure from reduced labor arbitrage, but there is opportunity for higher margins in IP, platforms, and consumption-based AI products. Vendors that focus on platform value or outcome pricing, rather than competing on FTE, are best positioned for success.

The most financially resilient delivery models combine platform-led managed services, co-managed or GCC partnerships, and outcome or subscription pricing. This approach reduces variable labor exposure, secures recurring revenue, and preserves margin potential from automation and IP.

 

Q4. Which delivery geographies are experiencing the steepest GenAI-driven productivity jumps, and how is this altering cost arbitrage, contract pricing, and the viability of traditional offshore delivery centers?

We’re seeing the biggest gains from generative AI in regions with deep talent pools, mature outsourcing ecosystems, and strong readiness for AI and data—most notably in India, and more and more in parts of Southeast Asia and Eastern Europe. These areas have the right mix of skills and infrastructure to tap into AI’s potential faster than others.

As AI automates repetitive manual work (data entry, invoice processing, reporting, basic customer service tasks, etc.), the old “cheap labour arbitrage” model, which pays per headcount, is losing its value.

Clients and vendors are moving toward hybrid or outcome-based pricing models, with fewer people, more automation, and pricing based on output, quality, speed, and accuracy rather than staff numbers.

This shift means many traditional offshore delivery centers could struggle to stay relevant unless they evolve. To stay competitive, these centers need to embrace AI-driven delivery, build up their expertise in automation and analytics, and concentrate on handling exceptions and more valuable work—instead of just processing large volumes of routine tasks.

 

Q5. Which vendors are best positioned to emerge as long-term leaders in finance automation, and what defensible moats—model performance, integrations, data assets, or cross-process depth—give them a durable advantage?

Some vendors are well positioned to lead in finance automation, including BlackLine, UiPath, Automation Anywhere, and new AI-native platforms in AP, AR, and close automation.

Their advantages include deep ERP integration, broad coverage of finance processes, proprietary data that improves model accuracy, and strong governance and control frameworks.

The vendors most likely to stay ahead are those that go beyond basic task automation. By bringing together automation, GenAI, and seamless, end-to-end orchestration of finance workflows, they’re able to offer real value as clients shift from FTE-based outsourcing to more modern, platform- and outcome-focused delivery. This holistic approach gives them a clear edge in a rapidly changing market.

 

Q6. What measurable ROI and cycle-time improvements are you seeing specifically from GenAI in AP/AR, reconciliations, and close processes, and what portion of these gains are directly attributable to AI vs. process redesign?

GenAI is delivering measurable improvements in finance.

In AP and AR, processing is two to three times faster, with a 20 to 30 percent reduction in cost per transaction and fewer payment or entry errors.

For reconciliations and close, close cycles are 25 to 40 percent faster, with less rework due to automated matching and variance explanations.

Roughly a third to half of these gains come straight from GenAI. The rest are thanks to process standardization and classic automation tools like workflow redesign, OCR, and rules-based matching. In other words, it’s the combination of smart new technology and solid process improvements that really moves the needle.

In summary, GenAI accelerates improvements, but the greatest ROI is achieved by combining AI with strong process transformation.

 

Q7. If you were an investor looking at companies within the space, what critical question would you pose to their senior management?

“How much of your efficiency and margin expansion comes from your own automation IP and AI platforms versus temporary labor arbitrage?”

This question helps determine whether the business is building scalable, defensible technology or relying on short-term cost savings from reducing headcount.