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Beyond Traditional Surveys: AI-Driven Approaches to Wellness Assessment

Presentation

Introduction

Employee wellness has evolved significantly over the past few decades (Marsburg, 2022; Martela & Sheldon, 2019). Traditionally, workplace wellness programs focused mainly on physical health, offering benefits like gym memberships and health screenings. As our understanding of wellness has expanded, mental, emotional, and even financial well-being are now considered essential components of holistic employee wellness. Today, organizations increasingly recognize wellness as a crucial driver of engagement, productivity, and retention.

In South Africa and around the world, well-being has become a strategic priority tied to broader business outcomes. Companies are moving beyond annual employee engagement surveys and yearly health assessments, as these tools—while informative—are often limited by their infrequency and subject to biases such as social desirability and response fatigue. Organizations now seek continuous, real-time insights that support dynamic wellness strategies responsive to the changing needs of the workforce. This shift also impacts the tools and methodologies needed by Assessment Centre Practitioners to stay aligned with evolving organizational requirements.

Traditional tools, like periodic surveys, capture only a limited view of employee well-being and fall short in providing the real-time data required for proactive intervention. This gap has created a demand for advanced methodologies, such as AI-driven wellness assessments, that leverage passive data from external sources. By continuously gathering and analysing data, these assessments provide objective and comprehensive insights into employee wellness.

Objectives

The session aims to explore the practical application of AI-driven wellness surveys that analyse passive data from external sources to assess employee well-being. Through real-world case studies, participants—particularly Assessment Centre Practitioners—will gain insight into how these AI-powered assessments are applied in various organizational contexts to measure and improve wellness.

Theoretical Foundation

AI-driven wellness assessments draw on an integrated theoretical framework that combines well-established perspectives, including Self-Determination Theory (Deci & Ryan, 2000), Subjective Well-being (Diener, 1984), psychological well-being (Jahoda, 1958; Ryff, 1989), and the PERMA model (Seligman, 2011). This framework captures both internal mindset factors—such as emotional well-being, motivation, and resilience—and external contextual factors, including organizational culture, work environment, and personal influences. Together, these dimensions create a holistic view of employee wellness, recognizing that both psychological states and situational factors shape workplace experiences. By integrating these theoretical foundations with AI technology and statistical methodologies, AI-driven wellness assessments provide a comprehensive and validated approach to measuring employee well-being.

Implications for Practitioners

For Assessment Centre Practitioners and HR professionals, AI-driven wellness tools offer a transformative approach to measuring and managing employee well-being. These tools use passive data collection from external sources, minimizing the need for intrusive surveys and enabling a more objective and continuous assessment of wellness. Real-time data insights empower practitioners and HR teams to make proactive, data-driven adjustments to wellness and engagement strategies tailored to meet the workforce's evolving needs. This session will introduce Assessment Centre Practitioners to innovative wellness measurement techniques that they can incorporate into their toolkit, supporting a more dynamic and responsive approach to employee well-being.

References

Deci, E. L., & Ryan, R. M. (2000). The “What” and “Why” of Goal Pursuits: Human Needs and the Self-Determination of Behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/S15327965PLI1104_01

Diener, E. (1984). Subjective well-being. Psychological Bulletin, 95(3), 542–575. https://doi.org/10.1037/0033-2909.95.3.542

Jahoda, M. (1958). Current concepts of positive mental health. Basic Books. https://doi.org/10.1037/11258-000

Marsburg, A. (2022). Development and evaluation of a longitudinal dynamic needs-action model of employee well-being: A psychological perspective. Unpublished PHD dissertation. Stellenbosch University. South Africa.

Martela, F., & Sheldon, K. M. (2019). Clarifying the concept of well-being: Psychological need satisfaction as the common core connecting eudaimonic and subjective well-being. Review of General Psychology, 23(4), 458–474. https://doi.org/10.1177/1089268019880886

Ryff, C. D. (1989). Happiness is everything, or is it? Explorations on the meaning of psychological well-being. Journal of Personality and Social Psychology, 57(6), 1069–1081.

Seligman, M. E. P. (2011). Flourish. A visionary new understanding of happiness and well-being. Wiley.

How will the delegates be able to apply your session content back on their job?

Delegates will be able to apply the session content by adopting AI-driven, continuous wellness assessments that allow for real-time insights into employee well-being. This approach helps them proactively identify and address workplace issues such as stress or burnout without relying on traditional, infrequent surveys. Equipped with data-backed insights, they can implement more targeted wellness interventions and support strategic decision-making to align wellness initiatives with broader business goals. Additionally, by integrating well-established wellness theories, delegates can offer a comprehensive view of employee well-being, reducing survey fatigue and enhancing the relevance and effectiveness of wellness strategies within their organizations.

What type of tip/tool (e.g., a template, framework, etc.) will you leave the delegates with?

AI-driven wellness tools which offers a transformative approach to measuring and managing employee well-being.

What do you want your audience to know at the end of your presentation and what will the three main points be?

By the end of the presentation, the audience will understand how AI-driven wellness assessments provide continuous, real-time insights into employee well-being, enabling organizations to adopt proactive wellness strategies. The three main points they’ll take away are:

Real-Time Insights for Proactive Wellness: Continuous wellness assessments with AI-driven tools allow organizations to identify and address issues like stress and burnout in real time, going beyond traditional, periodic surveys.
Targeted Wellness Interventions: Data-backed insights make it possible to tailor wellness initiatives to specific workforce needs, maximizing the effectiveness of interventions.
Strategic Alignment and Comprehensive Well-being: By integrating established wellness theories with AI data, organizations can reduce survey fatigue, enhance wellness relevance, and align wellness strategies with broader business goals for lasting impact.

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