HR TRENDS
AI, payroll and time and attendance: what gains in productivity can be expected?
ByChristophe Bertrand
27 October 2026
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Payroll, HR administration and time and attendance (T&A) include the data, rules and interactions that are the basis for the employment relationship. These functions must handle large volumes, incorporate frequent regulatory changes and maintain a high level of reliability. A single error can affect an employee's payslip, entitlements, or the organization's ability to manage and monitor its operations.
AI is now firmly embedded in the roadmaps of software vendors and HR departments alike. Conversational AI assistants, intelligent document management solutions, and error-detection agents, among other technologies, are being deployed at an accelerating pace, driven by advances in agentic AI. While this rapid growth creates significant opportunities, it can also result in a lack of focus.
Three core beliefs guide our approach. First, the most immediate gains are achieved in internal operations, where data, business rules, and approval processes are controlled and well understood. Second, the performance of a model matters less than its impact on the end-to-end process. Finally, value must be assessed before selecting a solution: without an established baseline, a pilot project may demonstrate technical feasibility, but it rarely provides evidence of the productivity gains that can realistically be expected.
Areas where AI has already demonstrated its value
Intelligent processing of documents and requests
Many employee administrative requests require supporting documents, such as sick leave certificates, public transport reimbursement claims or changes to bank details. AI interprets the documents and automatically enters the required information into the HR information system (HRIS). It not only acquires data, but it can also approve certain HR transactions based on predefined business rules.
Beyond administrative processes, including employee support services, AI can handle request triage by performing first-level support activities, enriching service tickets with relevant information and routing them to the appropriate teams.
AI therefore reduces manual data entry, improves data quality, supports approvals and shortens processing times. Its level of autonomy can be calibrated to ensure that HR professionals retain control over specific decisions.
Improving payroll compliance
Traditional rule engines verify conditions that have already been defined. AI can enhance the insights provided by these conventional tools and help payroll administrators prioritize their control and review activities. Statistical and probabilistic models build on this foundation by identifying uncommon errors or unusual combinations of data. They can analyze variable pay components, payslips and submissions to the DSN (France’s monthly social reporting declaration).
AI helps target the reviews that require the attention of HR professionals, optimizing the time spent on this activity while improving the reliability of payroll processing. More advanced models can help detect possible fraud.
The employee experience
Conversational AI agents are the most visible and immediately accessible face of AI. They genuinely enhance the user experience by identifying a user's intent and either providing a direct response to employees without human intervention (the concept of a "tier-zero" service) or guiding them to the appropriate HRIS process.
Conversational AI agents designed for HR professionals go even further. They can answer more complex questions, provide context-specific responses, automatically convert requests into support tickets, and natively support multilingual interactions through built-in translation capabilities.
When properly designed, conversational AI agents reduce the workload of support teams, allow HR professionals to focus on complex cases, and improve overall service quality through more accurate responses and faster response times.
Our approach to implementing AI use cases
Define the need and assess the current situation
The market often starts with an available feature and then looks for a problem it could solve. We recommend the opposite: start with the desired business outcome, map the end-to-end process, assess data quality and establish a few baseline indicators. This approach builds the case for investment and supports decisions between different solutions, including conventional IT, robotic process automation (RPA) and AI models.
Choose the technology: AI embedded in business applications or a general-purpose AI solution
Platforms with built-in AI and general-purpose AI solutions address complementary needs. An integrated platform provides consistency, process continuity and ease of maintenance. An integrated platform provides consistency, process continuity, and ease of maintenance. Its openness makes it possible to incorporate third-party services whenever their expertise or deeper functionality offers a clear advantage. The challenge is therefore to build an architecture suited to the organization’s context, assessing interoperability, data flows, reversibility, oversight, total cost and the allocation of responsibilities.
Run a pilot implementation with a representative scope
The pilot should cover a representative scope that includes both routine and exceptional cases within an organization, a process, or a target population. HR specialists should be involved in its design, the analysis of results, and any subsequent adjustments. Running the solution simultaneously with existing processes may be appropriate where the level of risk warrants it; the duration of running it along with existing processes will depend on the business cycle and the volume of activity required to generate meaningful results. The solution's limitations should be clearly documented, as these will determine the residual workload and the feasibility of scaling the solution across the organization.
Regulation and ethics: Incorporating compliance, security and human oversight
Payroll, HR administration and time and attendance handle personal data that is sensitive either by its nature or context. In addition to AI Act requirements, the use of AI remains subject to GDPR obligations: purposes must be defined, data minimized, access rights controlled and conditions for reuse verified. Data must be protected, roles determined and every recommendation traceable. Rules handle deterministic cases, while AI identifies, classifies, flags and recommends. HR professionals oversee the actions performed by AI, approve or reject certain decisions and handle those within their scope.
People: Supporting the teams and helping them move into more analytical and advisory roles
The earlier people are involved, the easier it is to achieve successful AI adoption. Change management should focus on the realities of day-to-day work, including new responsibilities, the interpretation of alerts, decision-making in complex cases, and the ability to challenge or override a system-generated outcome. Training should address the system's limitations, data protection requirements, and escalation procedures.
The central challenge lies not only in adopting the new solution but also in making effective use of the time it frees up. Without the corresponding changes to roles and skills, these gains in productivity are quickly absorbed by new demands and requests. The time saved can instead be reinvested in higher-value activities.
How should productivity be measured?
The market tends to focus on a model's speed and accuracy. However, these technical criteria alone are not sufficient grounds for an investment decision. Evaluation begins before the pilot phase, by establishing a baseline covering volumes, processing times, errors, rework, requests for support, and operating costs. Without this point of reference, it is difficult to attribute any gains directly to AI.
The most meaningful metrics are those that are tied to value.
Three aspects need to be assessed:
- Efficiency: measured through processing times, the user experience (including that of HR professionals), the added value of the service delivered and unit costs
- Quality : measured through error rates, false positives, incidents avoided, and explainability
- Adoption: measured through usage levels, acceptance of recommendations, and the satisfaction of both administrators and end users
Four sources of value should then be distinguished: increased operational capacity, reduced quality-related deficiencies, lower risk exposure, and improved service delivery. A case that is processed more quickly may allow for additional verification checks, while a solution that delivers only modest time savings may significantly reduce financial risk. Any business case should therefore take into account the solution itself, integration costs, monitoring and oversight, training, maintenance, and the value of incidents avoided.
Making AI a driver of sustainable operational performance
Four sources of value should then be distinguished: increased operational capacity, reduced quality-related deficiencies, lower risk exposure, and improved service delivery. A case that is processed more quickly may allow for additional verification checks, while a solution that delivers only modest time savings may significantly reduce financial risk. Any business case should therefore take into account the solution itself, integration costs, monitoring and oversight, training, maintenance, and the value of incidents avoided.
The market is entering a consolidation phase. AI capabilities will increasingly become standard features within software solutions, and their presence alone will cease to be a differentiating factor. The competitive advantage will belong to organizations that can identify the right processes to enhance with AI, orchestrate multiple technologies and human–machine interactions and continuously adapt their ways of working.
Christophe Bertrand
HR Transformation Director at Sopra HR Software
