Dominic Jainy is a seasoned IT professional at the forefront of the digital revolution, specializing in the intersection of artificial intelligence, machine learning, and blockchain technology. With a career dedicated to helping organizations navigate the complexities of digital transformation, he offers a unique perspective on how emerging tech is reshaping corporate finance. In this conversation, we explore the transition from monolithic ERP systems to modular, AI-driven ecosystems, the rise of autonomous financial agents, and the critical importance of data governance and security in an era of automated decision-making.
AI assistants are expected to accelerate the financial close process by 30% by 2028. How will this efficiency shift the daily priorities of a finance team, and what specific technical milestones must a department reach to hit that 30% target?
The shift toward a 30% faster close will fundamentally move finance teams from being “data gatherers” to “strategic advisors.” Instead of spending the first week of every month manually reconciling sub-ledgers, teams will focus on analyzing the variances and business drivers identified by the AI. To reach this milestone, the first technical step is migrating from on-premises systems to a subscription-based cloud ERP that supports embedded machine learning. Second, the department must achieve a “clean data” baseline where 90% or more of transactions flow through automated ingestion points without manual intervention. Finally, the organization must implement agentic AI capabilities that can autonomously handle multi-entity consolidations and currency conversions, which are often the primary bottlenecks in a global close.
Cloud ERP systems are moving away from rigid, monolithic designs toward “composable” modular ecosystems. What are the primary advantages of using low-code tools to extend these systems, and how can finance leaders ensure these interchangeable components don’t create new integration gaps?
The primary advantage is speed; low-code tools allow finance teams to build custom workflows or reporting modules in days rather than months, moving in lockstep with shifting market environments. This modularity breaks the “monolithic” trap where a single update could break the entire system. To prevent integration gaps, finance leaders must establish a central governance framework that mandates standardized APIs for every new “plug-and-play” component. By maintaining a strict integration layer, you ensure that even as you swap out specific modules for procurement or invoicing, the core financial data remains a single version of truth.
AI agents are now being deployed to handle complex tasks like predicting payment behavior and managing accounts receivable. How do these autonomous agents optimize working capital, and what specific metrics should a CFO track to verify that the AI is making sound financial decisions?
AI agents optimize working capital by moving from reactive to predictive collections, using historical patterns to identify which customers are likely to delay payment before the invoice is even due. This allows the team to prioritize high-risk accounts, effectively shortening the Days Sales Outstanding (DSO) and increasing cash on hand. CFOs should track “prediction accuracy” versus “actual payment dates” to ensure the AI isn’t being overly optimistic or aggressive. Additionally, monitoring the “autonomous resolution rate”—the percentage of collections handled without human intervention—will show whether the AI is actually freeing up staff for strategic priorities or just creating more work.
As anomaly detection and real-time audit logging become more common, managing trust and security for AI—often called AI TRiSM—is becoming a priority. What specific controls are necessary to ensure data integrity, and how should a firm handle an AI-driven error during a live audit?
To maintain integrity, firms must implement “AI TRiSM” controls, which include real-time audit logging of every decision the model makes and strict data access permissions. You need continuous controls monitoring to flag when an AI’s behavior drifts outside of predefined financial parameters or ethical guidelines. If an AI-driven error occurs during a live audit, the firm must have a “human-in-the-loop” protocol that allows for an immediate override and a transparent look-back at the model’s logic. This auditability is essential for explaining the “why” behind a transaction to regulators, turning a potential crisis into a documented learning event for the system.
Conversational analytics now allow teams to interact with financial data using plain language. How does this change the way a finance department handles complex reporting, such as ESG disclosures, and what are the risks of relying on narrative outputs generated by AI?
Conversational analytics democratize data, allowing a CFO to ask, “What was our carbon footprint per unit produced in the EMEA region last quarter?” and receive an immediate, plain-language answer. This is particularly transformative for ESG disclosures, where GenAI can synthesize disparate data points into a cohesive narrative report that meets regulatory standards. However, the risk lies in “hallucinations” or the AI generating a narrative that sounds confident but lacks a factual basis in the underlying ledger. To mitigate this, every narrative output must be tethered to verifiable data points in the cloud ERP, ensuring that the “story” the AI tells is always backed by hard numbers.
Investment in AI-enabled cloud ERP is projected to jump from 14% to 62% of total spending by 2027. Given barriers like data quality and multi-entity complexity, how should a global organization prioritize its budget to ensure these tools actually deliver a high return on investment?
With spending set to quadruple, organizations must resist the “vendor hype” and prioritize their budget toward data governance first. If you feed poor-quality data into a 62% more expensive AI system, you simply get bad decisions faster. A global organization should allocate its initial budget to standardizing its chart of accounts across all entities to overcome the friction of multi-currency and multi-legal entity reporting. Only after the data foundation is solid should they invest in high-end predictive scenario modeling and agentic AI, which provide the highest ROI by enabling faster responses to market volatility.
Many organizations struggle with skills gaps and data governance when adopting advanced automation. What practical training programs should a CFO implement to prepare a traditional accounting team for an AI-driven environment, and what historical data cleanup is required before going live?
A CFO should implement “data literacy” programs that move accountants away from spreadsheet manipulation and toward data storytelling and prompt engineering. The goal is to train the team to act as “AI supervisors” who can validate and interpret the machine’s output rather than performing the manual entries themselves. Before going live, a massive historical data cleanup is required, specifically targeting the last three to five years of transaction history to remove duplicates and ensure consistent tagging. Without this “historical hygiene,” the AI’s predictive models will be biased by old errors, leading to inaccurate forecasts and unreliable risk assessments.
What is your forecast for the evolution of the finance function as autonomous transaction processing and predictive scenario modeling become the standard in cloud ERP?
I forecast that by the end of the decade, the “traditional” accounting role will be almost entirely replaced by “Finance Technologists” who manage automated ecosystems. We will move toward a state of “Continuous Accounting,” where the books are essentially closed every single day because transactions are processed and reconciled autonomously in real-time. This will allow the finance function to pivot from reporting on the past to exclusively modeling the future, using predictive analytics to navigate economic shifts before they happen. Ultimately, the successful finance leader won’t be the one with the best spreadsheets, but the one who best manages the trust, risk, and security of their organization’s AI agents.