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Payroll & AI: What does the future look like?

June 9, 2025

6 mins approx

Payroll and AI

AI is doing what Excel once did for payroll number crunching — turning hours of manual work into seconds of automated precision. But when we look to the future of payroll and how AI fits in, there’s more coming! 

The goal is not just to make payroll faster, but smarter – AI is transforming the way businesses manage salaries, compliance, and risk. 

Join us as we take a closer look with Multiplier experts Ashish Verma, Director of Product Operations and Strategy, Hiraash Thawfeek, CTO, and Nimit Kumar, Senior Vice President of Product.

How does payroll in AI work?

Artificial intelligence is transforming payroll from a back-office function into automated processes that minimize errors and maximize efficiency. But how exactly does it work?

Automating manual work

Gone are the days of manually entering hours worked, calculating deductions, and checking tax rates. AI-powered payroll systems can extract, verify, and process data with minimal human intervention. 

By analyzing historical payroll data, AI supports automated payroll calculation verification, spotting errors in areas like duplicate payments, overtime payments, and social security contributions. 

AI-driven payroll automation not only ensures your compliance with payroll regulations but also strengthens fraud detection by analyzing transactions in real-time and flagging anomalies before they escalate.

Supporting employee experience

No employee likes waiting days for an answer to a question about their paycheck. Whether it’s an error in their payment or a question about tax deductions or benefits, employees want quick, accurate responses on a 24/7 basis. 

AI makes that possible with intelligent chatbots and self-service portals, giving employees access to their payroll info 24/7 without them needing to chase down HR.

The level of support AI can provide often goes beyond what payroll teams have capacity to offer – especially during a staff shortage. Verma explains how Multiplier’s AI chatbot, Ask Multiplier, enhances the employee experience: “It’s comprehensively trained not just on externally facing documents, but our internal SOPs and documents.” 

Mitigating the risks of global hiring 

AI plays a large role in mitigating risks associated with payroll discrepancies and compliance violations. And while the dynamics of single-country payroll are a lot more straightforward, Kumar explains how important AI is as companies expand. “When companies process payroll in multiple countries, the variability of data grows exponentially. It becomes almost impossible for a human to actually audit and monitor it.” 

AI, on the other hand, can monitor the information flowing in from different countries. It tracks all the different inputs — like salaries, tax rules, and benefits — even when those inputs change in complex or unexpected ways. The system understands patterns, such as seasonal variations, and remembers any exceptions that have been made in the past. As Kumar says, “Essentially it works like a risk management search model.”

AI also helps businesses prevent unauthorized transactions and payroll fraud and introduces new layers of security to global processes. Kumar explains how Multiplier validates payments, saying, “To validate payments, we use Optical Character Recognition (OCR) and automated AI security checks.”  

Taking the work out of compliance

Payroll compliance is complicated — different countries have different tax laws, labor regulations, and reporting requirements. AI makes it easier to stay on top of these by automating compliance tracking and managing regulatory updates.

Recognizing the limits of manually tracking compliance requirements, Verma explains how AI improves compliance efficiency, saying “bots can be trained on labor laws to answer questions more efficiently than a human or support guide can.” 

Optimizing payroll software

On the back-end AI, is detecting inefficiencies in payroll algorithms, suggesting optimizations, and even assisting in coding improvements and debugging, helping software to run smoother and adapt to changing business needs. As Thawfeek explains, “We have an AI agent that reviews code that’s written by engineers before it is peer reviewed and another checks for vulnerabilities in the code.”

As payroll systems continue to evolve, AI for payroll is playing a pivotal role in shaping their future – offering real-time insights, optimizing processes and workflows, and even helping prevent compliance risks before they arise. As AI technology progresses, here’s a closer look at some of the ways payroll AI is likely to evolve in the near future.

Predictive compliance 

Today, many payroll teams are stuck in a reactive mode — fixing errors only after they’ve been discovered. But with AI-powered payroll software, the future looks different. These tools will anticipate compliance issues before they occur, continuously scanning a wide array of data points — including historical payroll records, regulatory changes, and evolving market conditions — to flag potential risks early and recommend preventive measures.

Verma emphasizes the importance of training AI models on a company’s specific context to make compliance not just a checkbox, but a strategic advantage. By understanding industry-specific nuances, AI can tailor payroll processes to reflect the legal and operational realities of each sector. As Verma puts it, “AI is dynamic, adjusting payroll processes to meet shifting legal requirements and business realities.”

Real-time insights

AI is poised to transform payroll decision-making by delivering real-time insights that empower businesses to act instantly. As Kumar explains, AI doesn’t just process payroll data — it continuously monitors key compliance indicators, tracks changes in organizational structures, and adapts to legal updates as they unfold.

“By combining payroll data with contextual compliance intelligence,” Kumar says, “businesses gain real-time visibility into their compliance status. They can immediately see their compliance score, understand how closely they’re aligned with regulations, and quickly identify any gaps that need attention.”

Smarter payroll workflows

Traditional payroll systems often force companies to conform to the software’s limitations — shaping their workflows around rigid input-output models. But AI is flipping that model. As Kumar explains, “AI empowers teams to create self-serve workflows across the HR lifecycle, offering far greater flexibility in how payroll processes are managed.”

Most legacy systems operate mechanically: if the inputs are wrong, the outputs will be too — with no built-in ability to detect or correct errors. If there’s no way to catch mistakes at the input stage and tell the system something went wrong, errors simply carry through the entire payroll process unchecked.

Take a worker who’s consistently logged nine-hour shifts for a year and received a corresponding shift allowance, for example. If that allowance suddenly disappears, a traditional system might blindly accept the data and respond, ‘You gave me a ten-hour input, here’s the output.

AI, on the other hand, can recognize this as an anomaly. It can be programmed to flag the issue, explain what might be wrong, and in some cases, even correct it automatically — making payroll smarter, more proactive, and less prone to human error.

Get ready for the future of payroll AI with Multiplier 

AI is helping businesses take payroll from a rigid, manual process into an intelligent, strategic function that adapts to business needs in real-time. From automating workflows and ensuring compliance to providing real-time insights and preventing costly errors, AI is making payroll smarter, faster, and more reliable.

Ready to see how Multiplier can futureproof your global payroll operations? Book a demo today.

Picture of Rhiân Davies
Rhiân Davies

Rhiân Davies is a tech writer and strategist. As a former Gartner analyst and local government officer, she brings a unique blend of industry insight and public-sector experience to her work and research

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