Article
From reactive to proactive: How agentic AI is transforming data quality management
Businesses thrive on data. The operational efficiency of any organization is dependent on the flow of fast, accurate, and actionable information. As the business landscape continues to evolve at an ever more rapid rate, Data Quality Management (DQM) is becoming a critical priority—essential to operational efficiency, innovation, and maintaining a competitive edge.
This is especially true in HR and payroll, where data provides the foundation for their most basic business functions.
The importance of data quality
Poor data quality in HR and payroll can have immediate real-world consequences: overpayments, underpayments, security issues, and compliance risks—not to mention erosion of employee trust and potential reputational damage.
Poor data quality costs organizations an average of $12.9 million per year, and payroll errors can cost an estimated 1–8% of total payroll.
This makes ensuring data quality one of the highest priorities for HR and payroll teams, who often spend hours double-checking records and chasing down errors.
The impact of agentic AI on HR and Payroll data quality management for expanded potential applications
As AI continues to make its presence felt across the business landscape, its impact on HR and payroll has been especially noteworthy: automation, enhanced decision-making, and personalization have reshaped operations, helping to optimize workflows and improve efficiency.
The arrival of agentic AI—AI systems capable of autonomous, goal-driven action—has expanded these potential applications and opened up new possibilities for ensuring and enhancing data quality.
Agentic AI platforms deliver:
Speed
Process automation has already accelerated HR and payroll operations in a variety of ways. Agentic AI expands automation to include data checking and correction, making end-to-end data processing significantly faster.
Accuracy
Agentic AI is well-suited to anomaly and error detection. By applying intelligent agents to validate and correct employee and third-party data, organizations can significantly improve accuracy.
Productivity
Improved speed and accuracy save HR and payroll teams hours of manual labor. Far from replacing human workers, agentic AI empowers teams to shift away from repetitive calculations and working endless error reports to focus their energies on more strategic, value-driven tasks.
Cost reduction
Data errors can be expensive to correct and may even result in fines. Fewer errors mean fewer error-related expenditures. By improving productivity, HR and payroll professionals can also use their time more cost-effectively and strategically, delivering insights upwards to drive organizational value.
Security
Agentic AI enhances visibility and oversight across the data ecosystem. By integrating across multiple platforms or deploying specialized agents within a single system, organizations can monitor, flag, and protect sensitive data more effectively.
From reactive to proactive
As businesses across the globe consolidate their data systems, data quality becomes even more important. Consolidation allows for greater visibility and control, but it also means that because a single source of record feeds into multiple downstream systems, any upstream inaccuracies will flow through the entire ecosystem—unless they’re caught early.
Most DQM services are reactive, meaning teams scramble to fix issues after they’ve already happened. If, for example, an employee has been paid incorrectly, the error often isn’t identified until payroll has been run.
Payroll teams must then determine the source of the problem:
- Was it a calculation error, resulting from incorrect withholdings or deductions?
- Or did the error occur further upstream in HR, where employee data might have been entered incorrectly?
The process is made longer and more complicated by HR and payroll’s differing areas of focus when it comes to data quality.
Agentic AI enables a fundamental shift in DQM—from reacting to downstream issues to proactively preventing errors at the source.
AI-enabled anomaly detection is already making a difference on the payroll side, often catching inaccuracies before payroll is run. With the advent of agentic AI, companies can deploy agents further upstream to catch and correct mismatches even sooner. By performing ongoing audits, these AI agents can identify and fix errors before they become costly issues.
The future of data quality management
As technology gets smarter, faster, and more accurate, the pace of business will continue to accelerate. Companies wanting to stay competitive need to keep up with the evolving landscape and rising expectations.
Agentic AI is still a relatively new technology, but it is already becoming a fixture in the business world, with an estimated 79% of businesses exploring or deploying AI solutions.
As organizations increasingly consolidate platforms and processes into single systems, companies like Strada are already incorporating agentic AI into the in-tenant experience to deliver enhanced capabilities, greater visibility, and increased control.
With time, agentic AI will transform DQM into a continuous, automated safeguard with agents embedded at critical points across consolidated systems. Eventually, AI agents may even evolve into a strategic partner, working side-by-side with colleagues to keep systems and operations at peak efficiency, productivity, and accuracy.
Even in these early stages, the benefits of agentic AI are real—and nowhere is this more evident than in its impact on data. Investing now not only delivers immediate value in HR and payroll operations but also positions businesses to capitalize on future developments and maintain their competitive edge.
