Decision making and problem solving: How does predictive AI augment managers’ cognitive processes and decision-making strategies? In what ways does generative AI alter the nature of problem solving in organizations? Can AI help identify and formulate problems? How does the presence of AI affect the biases and heuristics observed in human decision-making processes within organizations? To what extent does the generation of new data solve AI’s traditional challenges of data availability and quality in decision making?
Work roles, employee skills, and managerial practices: How does the emergence of predictive and generative AI change the nature of work? What new work roles do organizations create and which employee skills gain or lose importance? In what ways can the use of AI enhance or hinder employee productivity, motivation, job satisfaction, and skill development? How can AI reduce barriers and contribute to more inclusive organizations? What managerial practices should be put in place to prevent potential negative consequences? How do these practices influence employees’ trust and reliance on AI systems?
Creativity and innovation: What is predictive and generative AI’s impact on creativity and innovation? How do organizations manage the interplay between AI-generated ideas and the inherently unpredictable nature of breakthrough innovation? How can organizations foster an environment where generative AI enhances human creative thinking and entrepreneurial spirit?
Teams, communication, and coordination: How does the inclusion of artificial agents change team structures, processes, and dynamics? What role does generative AI play in enabling communication and coordination within and across organizations? How does the increasing presence of chatbots in the managerial domain influence communication patterns and knowledge transfer? What is the role of AI in fostering or impeding interpersonal relationships at work?
Management of social evaluations: How do predictive and generative AI affect the processes through which organizational reputations are built, damaged, and repaired? How has AI changed the ways through which organizations become stigmatized or destigmatized? What are the pathways through which AI allows organizations to gain celebrity (or become infamous) and how do these pathways differ from those in the pre-AI era? How has AI affected perceptions of third-party certifications’ credibility and rankings’ validity, and with what consequences for organizations’ social evaluations?
Strategy, business models, and capabilities: In what ways do AI-based prediction and generation transform traditional business models and enable new ones? How do organizations strategically reposition themselves in response to AI-driven disruption in their markets? How can firms develop unique capabilities and create competitive advantages using predictive and generative AI? Does the joint deployment of predictive and generative AI help organizations balance the dual needs for efficiency gains and strategic innovation?
Organizational structures, practices, and routines: What organizational designs facilitate the integration of AI into organizational practices and routines? How does the integration of predictive and/or generative AI technologies, in turn, influence the design and evolution of organizational structures, hierarchies, and reporting relationships? How could organizations effectively manage the transition from traditional practices and routines to AI-enabled ones?
Societal implications: What theoretical perspectives can illuminate predictive and generative AI adoption’s ethical and social implications, such as equality, intellectual property, privacy, and security concerns? How do organizations navigate the ethical dilemmas related to AI technologies? How does AI support or hinder the ability of corporations, international organizations, and social movements to address grand challenges and other social problems? What are the positive and negative implications of AI adoption on the planet, such as, for example, its impacts on water consumption, carbon emissions, and deforestation?
Governance, stakeholders, and institutions: How can organizations develop and implement AI governance frameworks that ensure transparency, fairness, and compliance with legal and ethical standards? In what ways do organizations establish effective mechanisms for stakeholder engagement in the development of AI governance policies, particularly in contexts where technology outpaces regulatory frameworks? How do institutional arrangements change through organizations’ increasingly widespread use of predictive and generative AI?
