Beyond the Code: Understanding How, When and Why Humans Employ Generative AI in Innovation

generative ai innovation organizational capabilities human capital leadership

Journal of Managerial Psychology

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Éditeurs invités

  • William Y. Degbey, University of Vaasa
  • Maria Pajuoja, University of Vaasa
  • Matti Pihlajamaa, VTT Technical Research Centre of Finland
  • Baniyelme D. Zoogah, McMaster University
  • Waymond Rodgers, University of Texas

Synthèse

Artificial Intelligence (AI) is defined as the frontier of computational advancements that references human intelligence in addressing ever more complex decision-making problems. A key development is Generative Artificial Intelligence (Gen AI), particularly Large Language Models (LLMs) like ChatGPT, which can process and generate human-like language, extract insights, and produce creative outputs. Gen AI has enormous potential to significantly boost national economies. Its widespread adoption could increase GDP in the EU region by +8% (EUR 1.2–1.4 trillion) over the next ten years, provided innovators are equipped with the necessary skills and capabilities.

Yet, adoption remains uneven. For instance, European countries average a 54 percent adoption rate, below the global average of 61 percent. This uneven adoption is compounded by the global competition for skilled talent and persistent workforce shortages, particularly in digital and AI-related fields. McKinsey estimates that up to 12 million job transitions may be required due to AI, underscoring the urgent need for reskilling and entrepreneurial innovation.

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Thèmes proposés

  • How is Gen AI being used in innovation processes at different levels?
  • How do organizations apply Gen AI to replace, reinforce, or reveal innovation activities, and what are the resulting effects on, for example, process efficiency, novelty, and responsiveness to market needs?
  • How do varied patterns of Gen AI use across process phases and organizational levels influence the dynamics and management of innovation processes?
  • How do individual innovators use Gen AI in their work, what influences this usage, and what are the measurable outcomes of AI-augmented innovation efforts in terms of product, service, or process quality and speed?
  • In what ways does Gen AI influence innovation team dynamics, including collaboration, resilience, and decision-making, particularly in virtual or hybrid team environments?
  • What psychological and behavioral effects emerge from using Gen AI in innovation settings, such as changes in employee engagement, collaboration dynamics, or perceptions of autonomy?
  • How do managerial competencies influence the successful implementation of Gen AI in organizational innovation efforts?
  • What organizational capabilities need to be developed to successfully implement, integrate, and support the use of Gen AI in innovation efforts?
  • How are organizations supporting continuous learning and skill development for employees working with Gen AI, and what mechanisms are most effective in enabling the strategic use of Gen AI?
  • What ethical challenges do organizations face when deploying Gen AI in innovation activities, and how are they addressing issues such as intellectual property, transparency, and sociocultural responsibility?