44 | INNOVATION
powered by AI
Jaspal Randhawa ChMCIPPdip, Payroll Technology Consultant, OneAdvanced, discusses the power of artificial intelligence (AI) in payroll, providing pointers on how to ensure it’s used both safely and effectively
A I has moved from theoretical promise to practical reality in the payroll world at impressive speed. What began as the isolated automation of repetitive tasks has evolved into conversational co-pilots, predictive analytics, anomaly detection, automated query handling and even intelligent code-assist tools shaping the next generation of payroll platforms. For pay professionals, who are long accustomed to combining precision, compliance and large volumes of sensitive data, the rise of AI offers unprecedented opportunity, but also introduces a new landscape of risks and responsibilities. As the UK moves towards clearer AI regulation, and as HM Revenue and Customs (HMRC), the Information Commissioner’s Officer (ICO) and industry bodies continue to develop guidance on safe adoption, pay leaders must strike a careful balance, and embrace innovation while ensuring accuracy, accountability and resilience. This article explores the regulatory picture, the risks payroll teams must consider, how resilience can be built into AI-enabled processes and why human oversight remains the golden rule, no matter how sophisticated the tools become. Why AI matters for payroll Payroll sits at a unique intersection of finance, employment law, data governance, taxation and compliance. It’s also one of the most highly rule-driven functions in any organisation, making payroll an ideal candidate for responsible AI-enabled transformation. Today’s AI models can: l interpret payroll legislation into plain English l assist with policy drafting l extract key details from contracts and overtime agreements l help diagnose anomalies in timesheets, pay elements or tax codes
l support user access design, workflows, interfaces and configuration choices l improve query management with generative responses for employees l support product managers and technical teams designing the next generation of payroll platforms. But these same strengths can give a false sense of security. AI excels at producing coherent, confident answers even when the underlying reasoning is incomplete or incorrect. In payroll – where a single miscalculation or compliance error can result in underpayment, overpayment, fines, penalties from HMRC, Tribunal risk and reputational harm – there’s an obligation for organisations to use AI safely, legally and transparently. The regulatory landscape: what pay professionals need to watch While the UK doesn’t yet have a single overarching AI law, several regulatory and statutory frameworks already apply to the use of AI in payroll. Data protection and the ICO Payroll data is classified as high-risk as it includes salary, bank details, tax information, National Insurance (NI) numbers, sickness records and protected characteristics sometimes inferred through benefit schemes. Key obligations include: l data minimisation (including data anonymity) l purpose limitation l clear legal basis for processing l maintaining data accuracy l robust access controls l transparency and employee awareness. Employment law, guidance from the Advisory, Conciliation and Arbitration Service and Tribunal risk AI mustn’t be relied upon for legally binding
decisions. For example, if AI misinterprets a contract or mishandles a pay element related to the Transfer of Undertakings (Protection of Employment) rules, the employer remains fully liable. HMRC requirements If organisations use AI for things such as tax code classification, expense validation, IR35 interpretation, holiday pay calculations, attachment of earnings or salary sacrifice checks, they must ensure outputs are validated by experienced payroll staff. HMRC always holds the employer responsible. Emerging AI regulation The UK’s evolving AI governance is anchored in five principles: 1. Safety, security and robustness. 2. Transparency and explainability. 3. Fairness and avoidance of bias. 4. Accountability and governance. 5. Contestability and rights of redress. Understanding risk: where AI can go wrong Hallucinations AI can generate incorrect but confident statements. For example: l misquoting legislation l inventing HMRC rules l giving outdated case law l miscalculating NI contributions or holiday pay. Because payroll is highly regulated, hallucinations can lead directly to financial and legal risk. Outdated information AI tools may not automatically know the latest:
l tax thresholds l statutory rates l case law
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