In an exclusive interaction with Adlin Pertishya Jebaraj, correspondent of Finance Outlook India, Animesh Aggarwal, Group Chief Financial Officer at Covasant Technologies, shares his insights on leading digital finance transformation through GenAI and automation across enterprise operations. He highlights the fundamental evolution of the CFO role from historian to strategist, the critical importance of data governance in AI-driven decision-making, and the strategic approaches to evaluating ROI in finance automation. With two decades of finance leadership spanning Cognizant, HCL Tech, Tech Mahindra, Infosys, and Cyient, where he managed portfolios up to $1 Billion and delivered transformative achievements including PE investments, multiple M&A including $250Mn divestment, $100 Mn+ of cost savings and various finance automation and transformation initiatives, he offers a roadmap for building AI-native, future-ready finance organizations that shift from recording history to driving proactive intelligence and autonomous action across the digital finance landscape.

How does your role as CFO developed with the growth of digital finance tools like GenAI and automation?

The CFO's role has fundamentally evolved from historian to strategist, shifting the finance function from reporting "what happened" to using Generative AI to predict "what happens next." This transformation is driven by two critical realities. First, automation and GenAI haven't merely automated tasks—they've augmented the team's intellect, transforming the FP&A function from a modeling team into a "research team" that uses GenAI to ask complex, plain-English questions and synthesize financial results with unstructured data like sales notes to uncover the why behind the what.

However, this creates a profound new risk: "garbage in, garbage out" takes on new meaning when a flawed data pipeline doesn't just create a bad report but generates flawed strategy and new liabilities. This reality expands the CFO's remit from financial stewardship to data governance stewardship, making the finance leader fundamentally responsible for the integrity of data that trains models, including the Covasant AI Fabric, and drives predictive forecasts.

Second, as the CFO of a GenAI company, there exists a dual role where the finance team acts as "Customer Zero"—the primary testbed for our own platforms, providing real-world, ground-truth understanding of ROI and risks.

Externally, the finance function becomes a strategic partner in product and go-to-market strategy, helping architect entirely new pricing models while managing a complex P&L where the most strategic cost driver isn't just headcount but the massive, volatile cost of compute. In summary, the role is no longer just about managing capital; it's about managing the data assets and compute resources that create our predictive advantage.

What measures you take to evaluate the ROI of finance automation?

Evaluating the ROI of finance automation requires treating it not as a simple cost-saving exercise but as a strategic investment in core data assets and a force multiplier for resilience. We employ a layered approach that ties investment directly to quantifiable business outcomes, efficiency gains in an accelerated timeframe, and critical risk mitigation. The foundation begins with hard ROI and Time-to-Value (TTV), focusing on rapid operational throughput through metrics like close acceleration—enabling clients to achieve up to 40% faster financial close—and compliance cost reduction of 30-50% in monitoring expenses, while prioritizing FinOps principles to optimize cloud data spend, a critical lever in a GenAI-intensive P&L.

The second layer measures strategic value creation by quantifying the percentage of time senior finance analysts now spend on activities like pricing model development, market analysis, and M&A due diligence rather than routine reporting, tracking the diminishing variance between AI-driven forecasts and actual results for better capital allocation, and ensuring R&D expenditure and IP creation costs are precisely tracked to provide auditable support for higher asset valuation arguments.

The third and highest strategic layer addresses risk and governance value, measuring financial returns from preventing fraud, waste, and error through Enterprise Risk Management (ERM) services that consistently deliver 3-5x ROI within the first year, utilizing platforms like konaAI to flag anomalous and high-risk transactions—having already tagged hundreds of millions in risky payments across 20+ Fortune 500 customers and processed over $1.6 trillion in transactions—while assigning quantifiable quality scores to key data sources to mitigate the exponentially magnified risk of "Garbage In, Flawed Strategy Out" when feeding sophisticated AI models.

In what specific ways has GenAI improved the accuracy or timeliness of your rolling forecasts and budgeting cycles?

GenAI has revolutionized our rolling forecasts by introducing probabilistic accuracy and real-time responsiveness through agentic systems, moving us beyond static modeling into three specific areas of improvement. First, continuous, dynamic Smart Forecasting powered by machine learning allows our finance organization to dynamically predict and adjust needs in real-time through our Investment Agent, which connects both structured (ERP) and unstructured data (emails, PDFs) to provide a complete, auditable financial picture based on operational reality rather than past trends alone, with this AI-native architecture ensuring continuous monitoring that allows organizations to close books up to 40% faster.

Second, accuracy is significantly enhanced through granular demand sensing and market context, where we ingest volumes of unstructured data—including sentiment from customer support logs, product usage telemetry, and sales pipeline transcripts—and translate them into quantified business signals, with GenAI assigning weighted propensity to each revenue line item to predict churn risk or service expansion potential based on real-time qualitative signals that dynamically adjust the rolling forecast within the cycle. Third, the speed and volume of GenAI enable accelerated scenario modeling and compliance, allowing our FP&A team to generate dozens of plausible simulations—such as the impact of significant shifts in GPU compute costs versus strategic increases in global service adoption—in minutes, enabling executive leadership to stress-test the budget and proactively define trigger points for capital redeployment while the AI-native architecture enables automatic tracking of department budget compliance with daily financial summaries that flag any deviation requiring management attention.

What role does GenAI play in dynamic financial modeling or capital allocation?

As Group CFO overseeing two distinct entities—a commercial GenAI Services/Platforms business and an AI-led Global Capability Center (GCC) enablement service—GenAI provides continuous, risk-adjusted optimization that transforms capital allocation from a periodic, discrete event into an autonomous, proactive process. We approach this through two distinct perspectives that ensure balanced investment strategy. For our clients—large global enterprises—GenAI enables highly complex, dynamic modeling through our platform leveraging Agentic AI systems that act as "intelligent entities" which perceive, reason, plan, and take action, supporting autonomous execution of financial workflows we design for customer GCCs while running robust, complex scenarios with thousands of daily Monte Carlo simulations that project the full range of possible financial outcomes and their associated probabilities, all while our risk agents like konaAI continuously monitor 100% of transactions for customers to ensure proactive capital preservation.

Internally, as a high-growth but small, cloud-native firm, we deliberately maintain pragmatic simplicity where GenAI's core value lies in data integration and speed for optimizing our Cloud Service Consumption Costs (COGS) and managing platform scale cash flow, using GenAI for rapid cycle reduction and compliance monitoring within our own operations while quickly modeling the risk-adjusted ROI of competing internal projects to dynamically reallocate budget and ensure every dollar chases the highest-probability strategic outcome with the fastest Time-to-Value.

Also Read: Agentic AI & Predictive Analytics Driving Finance Automation

How are you managing cost optimization and risk appetite in such a rapidly evolving digital finance landscape?

Managing the cost-risk trade-off in the rapidly evolving digital finance landscape requires a strategy of Dynamic FinOps and Continuous Risk Assessment, approached with pragmatic duality: selling complex AI-led GCC solutions to large clients while running our own small, high-growth, cloud-native business. For cost optimization, we treat our cloud service consumption—our primary COGS driver—as an investment portfolio, using AI-powered FinOps agents from Covasant's solutions to analyze API usage and model consumption in real-time, predicting demand peaks and adjusting consumption commitments to ensure cost efficiency ties directly to platform output and business value.

Internally, we apply the Zero-Based Process Design methodology we use for client GCCs to our own core business functions, creating a real-world validation lab where GenAI enforces simplicity and strips out redundant steps to drive structural cost savings, while measuring the shift in employee time toward Strategic Capacity to ensure cost savings translate into higher strategic output. For risk appetite management, our high-growth posture necessitates well-governed risk management using our own Enterprise Risk Management framework, relying on the Covasant Agent Management Suite (CAMS) and its Agent Control Tower to keep our autonomous financial agents auditable with necessary human-in-the-loop controls, applying konaAI's capabilities to our own transactions to achieve 100% real-time monitoring for anomalies, and investing in automated routines to continuously cleanse and validate our data as the non-negotiable component of capital preservation that directly mitigates the "Garbage In, Flawed Strategy Out" risk.

What is your vision of the "ideal" tech stack that would enable a future-ready, insight-driven finance organization?

The ideal tech stack for a future-ready, insight-driven finance organization is an AI-native, integrated ecosystem structured in four interconnected layers that moves the finance function from merely recording history to driving proactive intelligence and autonomous action. The Data Foundation Layer provides the non-negotiable base through a scalable Data Lakehouse architecture that unifies structured and unstructured data to ensure real-time data quality and compliance, featuring seamless integration with legacy ERP systems via low-code connectors to provide Agents with a single, unified source of truth.

The Intelligent Core Layer serves as the engine of intelligence through the Covasant AI Fabric, our proprietary intelligence layer that enables specialized AI Agents to think, decide, and execute, providing low-code development for building financial applications with modularity for both complex client solutions and internal pragmatic needs while delivering simple, consumable decisions rather than raw data dumps.

The Agentic Execution Layer represents where the finance team shifts from manual processing to strategic oversight, consisting of specialized agents for specific financial workflows that deliver autonomous execution for everything from dynamic financial planning to automated contracting—capability that directly enables the streamlined, low-latency financial operations we design for customer GCCs—including essential systems like konaAI that provide continuous fraud detection by monitoring 100% of transactions in real-time, replacing reactive auditing with proactive control.

Finally, the Control & Governance Layer provides the crucial centralized vigilance that makes the stack truly future-ready through an AI Agent Control Tower that monitors Agent performance, provides human-in-the-loop controls, and ensures the financial system adopts agentic AI without sacrificing control, while integrating FinOps capabilities to prevent cost overruns by continuously monitoring the COGS of specialized cloud AI services. This end-to-end structure transforms the finance function into an autonomous, insight-driven engine for the business.