As tax season puts renewed pressure on finance teams to deliver accuracy, speed, and compliance under tight deadlines, many firms are reexamining the systems and workflows that underpin their accounting operations. While automation has long promised efficiency, today’s challenges demand more than faster data entry. They require real-time insight, stronger controls, and the ability to interpret financial data in a constantly changing business environment. This is where artificial intelligence is beginning to reshape accounting, not by replacing professionals, but by expanding what they are able to see, analyze, and advise on.
In this interview, we spoke with Rajeswaran Ayyadurai, an accounting and taxation professional with more than 17 years of experience spanning audits, taxation, and enterprise accounting systems across the US, Canada, and India. Drawing on deep hands-on work with platforms such as QuickBooks, SAP FICO, NetSuite, and Sage, he explains how AI is moving accounting beyond historical reporting toward real-time reasoning, anomaly detection, and strategic oversight, particularly in high-volume and multi-entity environments.
As firms navigate tax season and look ahead to scalable growth, Ayyadurai shares how AI-driven categorization, intelligent controls, and exception-based workflows are changing day-to-day accounting, strengthening audit readiness, and enabling teams to shift from reactive compliance work to proactive financial analysis. His perspective highlights why the future of accounting is less about doing more work and more about building systems that allow professionals to exercise better judgment, faster, with greater confidence.
With more than 17 years across accounting, taxation, audits, and ERP systems, how did you first see the opportunity for AI to meaningfully modernize bookkeeping and accounting workflows rather than simply automate tasks?
Initially, in accounting and bookkeeping, automation was defined to save time by avoiding manual processes, thereby speeding up reporting and support for decision-making based on reports and results. But over a period, we noticed that accounting was seen as mostly looking into the past and making decisions for the current challenges the business faces and for future prospects that are foreseen. Though this model can provide strong strategic insights from the past to inform decisions for the present and future, most decisions lacked the dynamics of the changing business environment and a broader analysis of the varied factors influencing them. That is when the need for AI was felt most strongly. AI is not for replacing the accountants, but it acts as a magic potion that gives the accountants a load of analytics, which bridges the time gap between accounting and reporting. This also acts as a Genie for business analytics, merging accounting into real-time strategic decision-making.
Many accounting teams are still burdened by manual classification and reconciliations. How are you applying AI to transaction categorization and matching in ways that improve both accuracy and confidence in the numbers?
Manual classifications and Traditional automations are based on rule-based mapping and keyword-matching principles that have proven successful in most cases. Still, when the situation changes, the existing methods lack standards to accommodate them. Given that account categorization is the basis for any reporting, incorrect categorization may mislead users and distort the reliance they intend to place on the reporting. On the other hand, AI-based account categorizations are based on broader contexts, giving importance to every detail captured in transactions before applying it to categorizing the current transactions in the best way, thereby accommodating the changes that everyday business transactions deal with. This will help accountants spend more time on critical analysis, strengthening the accuracy and confidence in numbers.
You work extensively with platforms like QuickBooks, SAP FICO, NetSuite, and Sage. How does AI change the way these systems are used day to day, particularly in high-volume, multi-entity environments?
Before AI, ERP, and accounting software were used to record each transaction that occurred in the business, as accounting started with recording the journal entries, followed by posting and reporting. Though most of these steps were all automated by using standard rules, now with AI, these platforms can be effectively used beyond the traditional recording, posting and reporting, as there is a shift of importance from transaction entry to validating the exceptions even in high volume environments and comparing the data across the entities in a multi-entity environment. ERPs are a system of records and AI acts as a system of reasoning on reporting, and the role of accountants shifts from reporting to advising
Anomaly detection is one of the most promising AI use cases in finance. Can you explain how intelligent systems help surface risks, errors, or unusual patterns earlier than traditional controls?
Traditional Controls are designed to detect anticipated anomalies, whereas AI detects the behavior and strengthens the existing controls with a multidimensional analysis that is not humanly possible at large volumes. AI enables early detection as there exists continuous real-time monitoring, exception analysis, and prioritizing them based on various factors that are relevant.
Compliance and audit readiness remain non-negotiable in accounting. How do you ensure that AI-driven automation strengthens transparency and control rather than creating new risks?
AI does not replace any system to create new risk, it instead reduces the risk with increased transparency and controls, as there exists a full audit trail for AI-assisted decisions with inputs considered, Confidence scores, user decisions, and overrides. This provides better documentation and thereby ensures audit readiness in accounting.
As routine work becomes automated, the role of accounting professionals is shifting. How have you seen AI enable teams to move from data entry toward higher-value financial analysis and strategic oversight?
AI has changed the role of accountants from just reactive reporting to more proactive advising. Routine work is removed, and more analytical work is included, adding value to the accountant’s role in any entity when making strategic decisions. Trust in the data enables more trust in the team. Moreover, instead of providing data on what happened in the past, the accountants are providing data on why it happened, the future implications of the same, and what should happen at present.
Looking ahead, what does scalable, AI-enabled accounting look like for firms serving multinational clients, and what mindset shift is required for teams to realize its benefits fully?
Scalability in the traditional model means more people per client, whereas in the AI-enabled model, it means more capability per person. Smaller, more capable teams manage larger clients rather than large junior teams. Teams have to design the systems rather than doing the work. To fully realize the benefits of AI, review and correct it until a strong system is built. An AI-based model enables scaling without proportional growth in headcount. Accountants have to understand that AI is here not to replace them but to equip them in making professional judgements.
