R&D of Artificial Intelligence Role in Rostering & Payroll
In Payroll, AI will play a significant role in helping teams work more efficiently.
The Issues Being Addressed
- The difficulty is the complexity of the many award rules, conditions, and the amount of payment due to comply with the Award Instrument
- Adding to this, the requirement to satisfy the many enterprise agreements that are used across Australian Businesses.
- Payroll must be 100% correct to avoid later issues of underpayment and penalties legislated in Australia.
AI will also enable deep dives into payroll data for analysis. That’s Inzenius
The R&D Proposition
If refined rule-based and AI-driven parsing methods are applied to Fair Work Award documents, then it will be possible to achieve fully accurate extraction and structuring of payroll rules across all Award conditions.
If Fair Work Award rate data is systematically mapped and validated against internal payroll configurations, then discrepancies between calculated and published rates can be eliminated, enabling fully compliant automated payroll processing.
This requires aligning system-calculated payroll rates with published Fair Work Award rates, even if the coefficients are not maintained
Prior experimental results demonstrated that while rule-based calculations could achieve high accuracy, discrepancies persisted when published rates did not conform to standard calculation models.
The activity focused on developing and testing methods to automatically retrieve, map, and validate external Award rate data against internal payroll structures to ensure complete compliance.
The R&D Proposed Outcome
A plan is to automatically retrieve, map, and validate external Award rate data against internal payroll structures to ensure complete compliance. and create a BOOT test against the actual payroll, with simplified pay adjustment reporting and processing for any shortfall in each pay period for each employee.
This is an R&D Example of AI in Payroll
In HR and Payroll, AI will play a significant role in helping teams efficiently undertake their tasks, not replace all of the roles “It’s often said that those who use AI will replace those who don’t. But the reality is more complex: As the framework illustrates, some tasks are best done by AI alone, others through human-AI collaboration, and some still require purely human judgment. Rather than debating replacement versus complementarity, the key is understanding which tasks remain distinctly human.” Bharat N. Anand and Andy Wu HBR Contributors
At Inzenius, our R&D team has been engaged in using AI to aid Payroll teams in the compliant Award Interpretation of employees’ times worked as compared to the relevant award instrument to test their actual pay to what they would have received on the award. This process is the Better-Off-Overall-Test (Boot).
AI will also enable deep dives into payroll data for analysis. That’s Inzenius
AI Plybook Article Published in the HBR magazine.
https://hbr.org/2025/11/the-gen-ai-playbook-for-organizations
Summary.
Leaders can’t afford to take a “wait and see” approach to adopting generative AI. They need a plan for applying it differently than others in the value chain, say the authors. In this article they introduce a framework for thinking about gen AI strategically and offer practical advice on how to apply gen AI to the tasks composing jobs. The framework focuses on two factors: the cost of errors and the type of knowledge required. If an error in carrying out a task would lead to serious harm, financial loss, or reputational damage, firms must be cautious about employing gen AI to perform it without human oversight. Tasks that rely on explicit data (information that can be captured and processed) are well suited for gen AI. But other tasks are fundamentally harder for it to perform because they involve not just retrieving information but also applying tacit knowledge: empathy, ethical reasoning, intuition, and contextual judgment. Placing the tasks in the appropriate quadrant makes it clear which ones gen AI can handle faster, cheaper, or better.