Q1. Which transformation or finance leadership roles gave you the greatest ownership over operational outcomes, cost optimization, or strategic decision-making, and what was the scale of impact involved?

As technology continues to transform finance, it’s more important than ever for finance leaders to understand how to use automation and AI to make operations run smoothly. When I started working on finance transformation at Genpact in 2015, many organizations were just experimenting with RPA. Now, intelligent automation is both scalable and widely adopted. At Genpact, Agilent, and now Accenture, I’ve focused on bringing together smart process design, the right technology choices, and hands-on delivery to help finance teams work faster, keep costs down, and strengthen controls.
Key responsibilities and approach
•    End-to-end ownership of finance process transformation, from opportunity identification through vendor selection, implementation, and post go live governance.
•    Technology-first decision making: prioritize solutions (RPA, ML OCR, ERP automation) based on volume, cost-to-serve, and ROI.
•    Delivery and Transformation mandate: run pilots quickly, measure outcomes, and scale proven solutions while embedding continuous improvement and control frameworks.
Representative initiatives and outcomes
•    Reduced manual journal entries by 33% through process redesign and targeted automation.
•    Implemented ML OCR for invoice processing to extract invoice data and feed ERP systems, cutting manual data entry and accelerating invoice-to-pay cycles.
•    Automated SAP cash application to match receipts to open items, improving cash application accuracy and shortening the working capital cycle.

Q2. What shift in enterprise finance and service delivery models is most fundamentally changing decision-making today, and why has it accelerated so rapidly in recent years?

Finance in the enterprise world is changing more than it has in decades. Instead of just focusing on following processes and meeting SLAs, the emphasis is now on delivering real results—driven by the fast growth of intelligent automation and new AI technologies.
Not long ago, finance’s success was measured mostly by how efficiently it could operate and how much money it could save—usually through strategies like offshoring or finding cheaper labor. But now, expectations are much higher. Organizations want finance leaders to deliver real business results, like helping drive revenue growth, understanding why customers leave, spotting fraud, getting ready for audits, and making the most of working capital.
The pace of change has been remarkable. Today’s technologies can handle not just traditional ERP data, but also things like bank statements, invoices, and even customer emails. This opens up new ways to make decisions. For instance, during my time at Agilent, cash application was always a tough problem. By combining machine learning with SAP automation, we managed to process 90% of receipts automatically—no manual work needed—which completely changed how efficiently we managed working capital.
This evolution explains the speed of change:
•    AI maturity has moved beyond rules to contextual intelligence.
•    Business demand has shifted from transactional efficiency to strategic impact.
•    Proven pilots scale rapidly, creating visible enterprise value.
The result is clear: finance service delivery is no longer about “doing the process right.” It is about using technology to deliver the right business results—a shift that is redefining the role of finance leaders and accelerating decision making across enterprises.

Q3. How are leading organizations redefining FP&A from a reporting function into a real-time strategic decision support capability?

For a long time, FP&A was viewed as a back-office role, mainly dealing with fixed budgets, quarterly forecasts, and explaining the differences in numbers. But that’s changing fast. Today’s leading organizations are turning FP&A into a real-time, strategic partner that helps guide decision-making across the business.
These days, FP&A isn’t just about reporting what happened—it’s about predicting what’s next and recommending the best actions. Forecasts are always evolving, updated in real time with new market signals and business results. Finance teams now work closely with other departments, helping guide decisions in sales, operations, and risk with deeper insights than ever before.
Tools like generative AI, agentic AI, and machine learning are giving FP&A teams the power to work with all kinds of data—not just spreadsheets, but also emails, PDFs, and other unstructured information. This means they can spot risks and find new opportunities as they happen. The pace of this change is picking up because companies need to be agile. Finance teams are expected to do more than just report the numbers—they need to help shape business results in real time.
In short, FP&A has become the strategic cockpit of the enterprise, delivering insights that drive growth, resilience, and competitive advantage.

Q4. Where are Gen AI, RPA, and machine learning delivering measurable gains in finance operations, and where is the ROI still falling short of expectations?

Gen AI, RPA, and machine learning are making a real difference in finance operations—especially in areas like accounts payable, accounts receivable, and record-to-report. With tools such as OCR for processing invoices, automated journal entries, smart reconciliations, cash application matching, and better tracking of the month-end close, teams are seeing obvious gains in efficiency.
But there’s a catch: those benefits don’t always show up if the data isn’t good. When companies have old ERP systems or scattered datasets, ML models and Gen AI tools just don’t work as well. Without a solid data foundation, it’s tough to turn raw information into insights you can actually use.
Change management is just as important. Too often, new tools are designed with executives in mind, but end up missing what analysts and frontline teams actually need. If teams aren’t brought on board or properly trained, these tools don’t get used to their full potential.
So, even though automation brings clear benefits, it’s really data quality and getting everyone to adopt the tools that make or break whether you see real returns.

Q5. As enterprises become more data-driven, how critical are governance, data quality, and compliance in enabling scalable AI adoption?

When companies grow through acquisitions, they end up with a patchwork of different ERP systems, which leads to messy, fragmented data. Meanwhile, finance leaders are eager to become more data-driven, rolling out machine learning and Gen AI tools to boost efficiency. In this environment, data governance isn’t just a nice-to-have—it’s absolutely essential.
To make AI work at scale, you need high-quality, complete, and compliant data. That means making sure information is accurate, available in real time, and accessible only to the right people with the right permissions. Good governance doesn’t just set the rules for who can use data—it also defines how to use it, building trust and transparency across the business.
Building a robust data layer is the enabler: it transforms fragmented inputs into a unified, reliable source that advanced tools can leverage. Without it, AI adoption stalls, ROI diminishes, and efficiency gains remain elusive.
In short, data governance is the strategic backbone of AI in finance operations—the discipline that turns ambition into scalable impact.

Q6. In an environment where most enterprises are investing in similar technologies, what ultimately differentiates organizations that achieve lasting transformation success?

These days, nearly every company is investing in the same kinds of technology—whether it’s Gen AI, RPA, or advanced analytics. But the real difference between organizations that achieve lasting transformation and those that fall behind isn’t the technology itself. It’s how disciplined they are about putting it into practice.
The differentiators are clear:
•    Data Quality & Governance: Building a strong data foundation ensures completeness, accuracy, and compliance, enabling AI models to scale effectively.
•    Change Management: Successful organizations embed transformation into the culture, engaging analysts and frontline teams—not just leadership.
•    Leadership Buy-In: Visible sponsorship from senior leaders drives alignment and accelerates adoption.
•    Clear Accountability: A single authority to approve and oversee transformation projects prevents fragmentation and ensures consistency.
Enterprises that combine robust governance, strong leadership, and disciplined execution turn technology investments into lasting impact, while others remain stuck in pilots and fragmented initiatives.

Q7. If evaluating a company’s transformation strategy today, what indicator would tell you whether the organization is genuinely building long-term capability versus simply digitizing existing inefficiencies?

When assessing a company’s transformation strategy, the clearest indicator of long-term capability is whether the organization is building a robust data and governance foundation—not just layering technology on top of existing inefficiencies.
Enterprises that are genuinely transforming focus on:
•    Data Quality & Completeness: Ensuring clean, consistent, and real-time data across systems.
•    Governance & Compliance: Defining clear rules on who can access data, how it is used, and ensuring accountability.
•    Change Management & Adoption: Embedding transformation into daily workflows, not just leadership dashboards.
•    Unified Authority & Leadership Buy-In: Driving projects with clear ownership and strategic sponsorship.
If the strategy emphasizes scalable data governance, cultural adoption, and disciplined execution, it signals the company is building enduring capability. If it merely digitizes broken processes without addressing these fundamentals, it is only automating inefficiency—and ROI will remain short-lived.