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.
This Special Issue aims to advance empirical understanding of how, when, and why humans engage with Gen AI in innovation. The focus is on Gen AI as an innovation enabler, inviting submissions to explore innovation processes, human capital resources, and organizational capabilities. The call welcomes single- and multi-level studies that examine Gen AI's role in shaping innovation within and across organizations and encourages contributions that take an interdisciplinary perspective. Submissions should address managers' pressing challenges in finding new ways to support and improve innovation by investigating how managers can effectively enhance how employees and executives use Gen AI.
Technological advancements have made Gen AI more accessible and affordable across industries. Gen AI's impacts are not limited to what organizations innovate but also change how they innovate. Gen AI can be used to replace existing innovation processes, reinforce them, or reveal unforeseen ways of developing new products and services. It may assist in the identification of opportunities, idea generation, and evaluation of innovation potential, reshaping the front-end of innovation processes, and enable more rapid experimentation and improved customer understanding. Yet, experiences and use cases remain evolving and fragmented, creating uncertainty for managers.
Furthermore, Gen AI transforms innovation processes at multiple interrelated levels, whereas existing research has mostly examined them in isolation. Consequently, this Special Issue encourages a multi-level perspective, including individual, team, and organizational level processes.
Microfoundations research shows that innovation is fundamentally driven by people, not organizations, since it is individuals who identify opportunities, generate solutions, and implement them. To understand how Gen AI enhances innovation outcomes, such as faster development of better-quality products, services, and processes, it is necessary to examine how individual innovators use Gen AI, what influences its use, and what results emerge from these AI-augmented efforts. Team-level dynamics are equally important, as teams have become the core unit of modern organizations. Gen AI can support team innovation by sparking dynamic discussions and promoting diverse thinking in design processes, supporting ethical decision-making, and by helping overcome challenges in group decision-making. Finally, scholars have called for more research on how leadership can support AI adoption and digital transformation. Leaders must build new capabilities as AI implementation is often challenging, human-AI interaction may involve complications, and a new range of ethical concerns must be considered.
The rapid evolution of Gen AI demands not only technical adaptation but also new approaches to manage the associated processes, tensions, and frustrations. As Gen AI profoundly alters work design, skill requirements, and professional identities, employees face both new opportunities and psychological challenges. To ensure they can develop the AI-related skills needed for the evolving roles, organizational and managerial support mechanisms have to be developed. Yet, there is limited understanding of the organizational capabilities required to implement this change successfully. Effective support may involve building Gen AI literacy, fostering experimentation, and promoting continuous learning.
At its best, AI is strategically deployed to generate business value and competitive advantage, but without targeted support, organizations risk de-skilling, employee resistance, and ineffective adoption that undermines innovation goals. Organizational practices for managing innovation are also being challenged by Gen AI, especially in terms of ethical governance. While the benefits of Gen AI in innovation are promising, ethical concerns remain underexplored. Key principles include respect for intellectual property, truthfulness, robustness, recognition of malicious uses, sociocultural responsibility, and human-centric design.
Overall, the Special Issue seeks papers that provide empirical contributions on the evolving role of Gen AI in innovation to deepen psychological and social insights into management practices to support, improve, and offer practical guidance to help managers navigate Gen AI adoption and integration. The listed themes are neither exhaustive nor exclusive; authors are encouraged to explore other themes aligned with the aims of the Special Issue. The journal welcomes contributions from scholars of innovation, organizational behavior, management, HR, strategy, technology, and practitioners.