People Analytics (PA) is increasingly, although not exponentially, becoming a strategic tool for companies to make data-driven decisions.

The integration of generative AI (GenAI) opens up new opportunities to analyze HR data more efficiently and translate it into valuable recommendations for action. GenAI not only improves automation and analysis, but also creates intuitive approaches that promote a data-driven corporate culture.

Another important driver is the EU reporting requirement under the Corporate Sustainability Reporting Directive (CSRD), which obliges companies to provide comprehensive information on social and environmental aspects. People Analytics plays a central role in this, as it provides data for the sustainable transformation of the workforce, making trends visible and progress measurable.

Combining PA with sustainability and AI technologies enables companies to align strategic goals such as profitability, employee retention or resource efficiency with regulatory requirements and societal expectations.

Added value of GenAI for People Analytics

GenAI can be used in many ways in the PA area, for example for data cleansing, analysis, visualization, documentation and as a supportive sparring partner in projects. In particular, GenAI makes analyzing and interacting with data more intuitive and efficient.

However, the added value is tied to prerequisites:

  • The breeding ground for GenAI, such as Copilot in Power BI, is a data-driven corporate culture. The use of such technologies requires changes in working methods and behavior patterns in order to develop their full potential.
  • Even precise ML models are of little use if the underlying problem does not exist in the company! The starting point must always be the question: What is the problem? GenAI is used to specifically solve problems or improve processes.
  • Hypothesis tests such as the Welch t-test, the correlation test and the chi-square test are essential to verify conclusions drawn from data. They help to check differences between groups, correlations or proportions in categories. Such tests enable well-founded, statistically sound decisions.
  • Data-driven decisions require a thorough examination of internal and external validity. Managers should not blindly accept or reject results, but analyze them systematically. Common mistakes such as confusing causality and correlation or overvaluing individual results must be avoided in order to make valid and actionable decisions.

High-quality HR data is, of course, a prerequisite for the successful use of GenAI. This is the only way to build trust and make informed decisions. Workforce360, IBM’s internal HR data platform, is an example of how first-class data can sustainably optimize business processes.

People Analytics as a lever for sustainable transformation

By integrating sustainability aspects – such as measuring diversity quotas, promoting health and providing training – companies can develop sustainable HR strategies based on ESG goals. Modern trends include the use of AI-supported analysis to identify behavioral patterns and respond to the needs of a sustainable workforce.

The Corporate Sustainability Reporting Directive (CSRD) requires companies to make their sustainability measures more transparent. The European Sustainability Reporting Standards (ESRS) specify the reporting requirements; including ESRS S1 “own workforce”, which focuses on social aspects. This includes data on employee satisfaction, working conditions and diversity.

The ESRS S1 standard

At the heart of HR reporting is the ESRS S1 standard, which requires data on the workforce and social issues, including:

  • Diversity and inclusion (S1-9): Information on gender distribution, diversity initiatives and inclusive corporate cultures.
  • Working conditions (S1-11, -14): Information on working hours, occupational safety and health management.
  • Social dialog (S1-8, -17) and training (S1-13): Measures to strengthen employee rights, further qualification and professional development.
  • Remuneration (S1-10, -16) and equal opportunities (S1-12, -15): Disclosure of income differences and fair payment practices.

One of the biggest challenges is integrating fragmented HR data into a central system that meets ESRS requirements. Companies need to standardize data collection processes and ensure that this data is traceable and auditable. In addition, governance requirements are increasing: an internal control system (ICS) for sustainability data is increasingly becoming a must. In addition to meeting regulatory requirements, companies can also strengthen trust among stakeholders and improve their reputation through transparent and high-quality reporting.

Company example: Gore Mutual Insurance

Gore Mutual Insurance was one of Canada’s first property and casualty insurers. Sonia Boyle, Chief People Officer, and her colleagues in leadership positions sought to improve the PA process using technology and introduced the platform Visier in 2020. She summarizes her findings as follows:

  • “Before working with Visier, most of our decisions were based on anecdotal evidence and historical trends.”
  • “People data analytics has transformed how we make decisions – from hiring to promoting – and has elevated the status of the HR team.”
  • “Having a greater sense of the data-driven trends, we can now plan out specific employee journeys.”

As a result, the PA measures have led to a better understanding of the workforce and helped to identify key issues in terms of engagement, productivity and turnover risk, see ESRS S1-6_11 and S1-6_12.

Rethinking sustainability with People Analytics and GenAI

In this context, “rethinking sustainability” means considering the term sustainability not only from the perspective of regulation and transparency, but also in terms of strategic decision-making; in concrete terms, this can mean, for example, reassessing the product portfolio or supply chain based on ESG value drivers. By combining people analytics (PA) and generative artificial intelligence (GenAI) new opportunities are opening up to pursue sustainability goals in companies in a data-driven, precise and innovative way.

The novelty here is the use of GenAI to analyze, model and manage complex personnel and sustainability data. This approach makes it possible not only to evaluate historical data, but also to simulate future developments and act proactively. Sustainability thus becomes more dynamic and better integrated into strategic corporate management.

Five key opportunities

The combination of PA and GenAI offers the following opportunities in the area of sustainability:

  1. Use HR data to promote social sustainability: PA and GenAI enable companies to evaluate and improve social aspects such as employee satisfaction, diversity, equal opportunities and working atmosphere. GenAI makes it possible to analyze qualitative data from surveys or interviews and to identify patterns that lead to more effective measures.
  2. More efficient use of resources in HR and beyond: By optimizing HR management processes, PA contributes to the sustainable use of resources. GenAI can, for example, predict fluctuation risks, automate succession planning or promote skills development in a targeted manner. This increase in efficiency helps to reduce the amount of resources required in HR and create a more sustainable corporate culture.
  3. Integration into CSRD- and ESRS-compliant reporting: The Corporate Sustainability Reporting Directive (CSRD) requires detailed reports on social and environmental issues. PA can underpin these reports with precise data on the workforce, diversity and skills. GenAI supports by combining and cleaning data from different sources and preparing it both textually and visually. This reduces the effort involved and improves the meaningfulness of the reports.
  4. Strategically manage sustainability: PA and GenAI make it possible to closely link sustainability goals with business objectives using dynamic dashboards and real-time data. For example, companies can evaluate the impact of measures to reduce CO₂ or employee development on performance and better prioritize them.
  5. Transforming corporate culture: GenAI promotes access to data and insights for all employees (democratization of data). This establishes a data-driven and sustainable corporate culture in which employees can participate more actively in the implementation of sustainability measures.

Conclusion

PA should be driven from different perspectives: The strategist designs and evaluates HR strategies, leads transformation programs and optimizes the HR operating model. The gatekeeper ensures compliance with data protection regulations, validates AI models and establishes clear frameworks for handling HR data. The specialist analyzes behavioral data, develops analytical models and dashboards, and conducts surveys and skills assessments. The designer focuses on designing user-friendly analytics products, dashboards, and questionnaires, and presents data through impactful stories.

A data-driven corporate culture is the most important factor for the success of PA. Technological investments alone are far from sufficient; they must be combined with human skills.

The combination of PA and GenAI opens up new ways for companies to make sustainability measurable, manageable and future-oriented in all dimensions – social, ecological and economic. Data-based insights and automated solutions can efficiently overcome complex challenges and integrate sustainability strategies more closely into daily practice. The key is to empower people and technology equally to create a resilient, sustainable organization.