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  3. AI at Work: Turning Organizational Data Into Personal Growth

AI at Work: Turning Organizational Data Into Personal Growth

In page navigation: Research
  • AI at Work: Turning Organizational Data Into Personal Growth
  • Digital Interventions for Healthy and Sustainable Consumer Behavior
  • Digital Transformation of the Energy Sector
  • Human–AI Decision-Making and Data Sharing
  • Technology-enabled behavior change
  • Techno-economic analyses of energy systems and technologies
  • Persuasive technologies
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AI at Work: Turning Organizational Data Into Personal Growth

We explore how digital coaches—powered by organizational data—can provide trustworthy and helpful personalized feedback not only enhance employee performance but also foster personal fulfillment and support effective change processes.

Traditional workplace feedback tends to be infrequent, limited in scope, and heavily dependent on specific managers or peers. When feedback is negative, employees may dismiss it, perceiving bias or even discrimination. This undermines both performance improvement and personal growth.

Digital coaching systems—drawing on company data sources such as CRM and ERP systems— can deliver timely, detailed, and personalized feedback that aligns with employees’ individual preferences. Such systems may also benefit from perceptions of greater objectivity and trustworthiness compared to human managers, while providing up-to-date insights that employees can readily act upon.

Our research investigates the design and effectiveness of digital coaches in organizational settings, focusing on whether they can facilitate change processes more successfully than traditional approaches. We place particular emphasis on creating systems that not only drive higher performance but also foster personal fulfillment and well-being at work.

Current projects

  • Data-driven personal digital coach for the workplace

Completed projects

  • Beyond adoption: Drivers of continued CRM use
  • Saving energy at work: Train drivers’ perceptions of energy-efficient driving and feedback technologies

  • Schneider, L., Tiefenbeck, V., (2025), Interdependence within Collective CRM Continuance. A Systematic Literature Review and Integrated Framework. European Conference on Information Systems (ECIS 2025), Amman, Jordan
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