The Data-Driven Dawn of Payroll: Insights from EY India’s transformation

In an era where artificial intelligence (AI) is no longer a futuristic buzzword but a force transforming every aspect of business, payroll and compensation have quietly become ground zero for digital innovation. At the forefront is Shobha Keni, Accounting Compliance, Reporting and Payroll Operate Services Leader at EY India, whose perspectives offer a rare glimpse into how technology is not just streamlining payroll operations but redefining fairness, transparency, and strategy in the modern workplace. In this People Matters exclusive interview, Keni shares her insights on the tangible impact of AI, the challenges of equitable pay, and how leaders should prepare for the next wave of transformation. Edited excerpts.

AI and automation have streamlined payroll by eliminating manual data entry, reducing errors, and enabling real-time compliance checks and predictive anomaly detection. Organisations are seeing 65 percent faster payroll cycles, up to 78 percent fewer processing errors, major compliance improvements, and significant ROI. These efficiencies free payroll teams to focus on higher-value work while improving accuracy and employee experience.

AI has strengthened payroll compliance through real-time regulatory monitoring and continuous anomaly detection, improved accuracy by automating data validation and reducing manual errors, and enhanced employee experience with AI chatbots that resolve queries instantly. AI-enabled payroll agents streamline payroll by ingesting unstructured data, validating it against controls, detecting anomalies, and generating proactive insights using the One Data model. They also identify input–output discrepancies and reconstruct payroll configurations from historical data to catch noncompliant tax treatments.

EY case studies currently in progress include using AI agents to ingest unstructured data, validate it against payroll controls, and map it to a payroll data model, enabling payroll processing with minimal human interaction. It also demonstrates the use of an AI smart agent for natural language report generation and AI-driven anomaly detection to improve compliance outcomes. In addition, an AI knowledge assistant and global payroll chatbots have reduced inquiry response times from days to seconds and boosted employee trust.

Looking ahead to 2026 and beyond, AI will shift payroll from automated processing to fully intelligent, self-updating systems. We expect autonomous compliance engines that interpret regulatory changes in real time, continuous payroll that recalculates pay instantly as events occur, and predictive AI that proactively identifies risks and cost patterns before they surface. Generative AI copilots will further transform operations by explaining pay outcomes, resolving complex queries, and automating reporting. To prepare, we’re investing in next-generation AI platforms, strengthening API-driven global integrations, and expanding AI-enabled controls, analytics, and employee experience tools, ensuring payroll becomes more accurate, resilient, and strategically valuable.

EY is strengthening pay transparency and fairness by reassessing compensation structures, providing clear salary frameworks, and aligning pay decisions with objective, gender-neutral criteria. The firm leverages its Global Labour & Employment Law Strategic Guide to navigate diverse regulatory requirements and support consistent compliance across jurisdictions. EY’s compensation policies emphasise fair, market-competitive, and inclusive pay practices, with a focus on eliminating disparities across gender, race, ethnicity, and other identity groups. These efforts help EY ensure equitable compensation while proactively addressing the legal and cultural challenges associated with the global shift toward pay transparency.

EY is using AI-driven benchmarking and predictive analytics to replace subjective pay decisions with data-backed insights, ensuring salaries align with market realities and internal equity standards. Tools like the EY Benchmarking Toolkit bring validated quantitative datasets into compensation planning, helping organisations make more consistent, defensible decisions. The impact is significant: with 60 percent of employers now adopting AI for salary benchmarking, equity analysis, and personalised rewards, organisations are already seeing more transparent pay structures, reduced bias, and faster, more accurate compensation cycles. Additionally, EY’s enterprise AI platforms (EY.ai and EY Fabric) integrate trusted data at scale, improving decision quality and supporting fair, objective, and future-ready compensation strategies.

EY is updating its compensation approach to reflect the realities of remote and hybrid work by building structured, compliant, and location-aware frameworks. With its Hybrid Work and Cross-Border Remote Work policies, EY supports flexible work while ensuring pay decisions appropriately consider tax, legal, and mobility risks tied to geography. Through its Work-from-Anywhere model, EY also evaluates what to pay remote employees and how geographic differences impact talent costs and internal equity.

Compensation is rapidly moving toward greater flexibility and personalisation, driven by the growth of gig work and new payment technologies. With gig workers projected to make up more than half of the workforce by 2030, rewards will increasingly need to be portable, individually tailored, easily accessible via mobile devices and detached from traditional employment models.

At the same time, employers are expanding rich, personalised benefits to attract diverse, distributed talent. Digital currencies and stable coin-based payments are expected to make compensation more real-time, secure, and cross-border friendly, reshaping how workers—especially freelancers—are paid. Talent leaders should begin preparing by building flexible total rewards frameworks, enabling portable benefits, modernising payroll for digital currency compatibility, and strengthening governance to ensure fairness and compliance across increasingly fluid work models.

For payroll leaders beginning their AI journey, the most important step is to build a strong foundation—clarify your AI strategy, ensure data quality, and invest in the right skills early. Start small with high-impact use cases and focus on change management, as common pitfalls include a lack of internal expertise, unclear ROI, integration challenges, and resistance to change. Embedding compliance, security, and responsible AI practices into existing processes while continuously reviewing and refining outcomes helps ensure a smooth, sustainable digital transformation.