Background General practitioners (GPs) care for a large number of patients with various diseases in very short timeframes under high uncertainty. Thus, systems enabled by artificial intelligence (AI) are promising and time-saving solutions that may increase the quality of care. Objective This study aims to understand GPs’ attitudes toward AI-enabled systems in medical diagnosis. Methods We interviewed 18 GPs from Germany between March 2020 and May 2020 to identify determinants of GPs’ attitudes toward AI-based systems in diagnosis. By analyzing the interview transcripts, we identified 307 open codes, which we then further structured to derive relevant attitude determinants. Results We merged the open codes into 21 concepts and finally into five categories: concerns, expectations, environmental influences, individual characteristics, and minimum requirements of AI-enabled systems. Concerns included all doubts and fears of the participants regarding AI-enabled systems. Expectations reflected GPs’ thoughts and beliefs about expected benefits and limitations of AI-enabled systems in terms of GP care. Environmental influences included influences resulting from an evolving working environment, key stakeholders’ perspectives and opinions, the available information technology hardware and software resources, and the media environment. Individual characteristics were determinants that describe a physician as a person, including character traits, demographic characteristics, and knowledge. In addition, the interviews also revealed the minimum requirements of AI-enabled systems, which were preconditions that must be met for GPs to contemplate using AI-enabled systems. Moreover, we identified relationships among these categories, which we conflate in our proposed model. Conclusions This study provides a thorough understanding of the perspective of future users of AI-enabled systems in primary care and lays the foundation for successful market penetration. We contribute to the research stream of analyzing and designing AI-enabled systems and the literature on attitudes toward technology and practice by fostering the understanding of GPs and their attitudes toward such systems. Our findings provide relevant information to technology developers, policymakers, and stakeholder institutions of GP care.

Artificial intelligence (AI) offers organizations much potential. Considering the manifold application areas, AI’s inherent complexity, and new organizational necessities, companies encounter pitfalls when adopting AI. An informed decision regarding an organization’s readiness increases the probability of successful AI adoption and is important to successfully leverage AI’s business value. Thus, companies need to assess whether their assets, capabilities, and commitment are ready for the individual AI adoption purpose. Research on AI readiness and AI adoption is still in its infancy. Consequently, researchers and practitioners lack guidance on the adoption of AI. The paper presents five categories of AI readiness factors and their illustrative actionable indicators. The AI readiness factors are deduced from an in-depth interview study with 25 AI experts and triangulated with both scientific and practitioner literature. Thus, the paper provides a sound set of organizational AI readiness factors, derives corresponding indicators for AI readiness assessments, and discusses the general implications for AI adoption. This is a first step toward conceptualizing relevant organizational AI readiness factors and guiding purposeful decisions in the entire AI adoption process for both research and practice.

An appropriate problem-solution-fit is essential to develop purposeful artificial intelligence (AI) applications. However, in domains with an unintuitive problem-solution-fit, such as project management (PM), organizations require methodological guidance. Hence, we propose a five-step method to develop organization-specific AI use cases: First, companies must consider the context factors technology, organization (in particular data and application domain), and environment. Second, companies must identify existing domain problems and AI solutions. Third, our method facilitates abstraction to understand the underlying nature of the identified problems and AI solutions. Fourth, our problem-solutionmatrix assists companies to match AI functions with the domain context. Fifth, companies derive necessary implications for the subsequent use case implementation. To construct and evaluate our method, we followed the design science research paradigm complemented by situational method engineering and based on 14 interviews. Our method addresses a relevant practical problem and contributes to identifying purposeful AI use cases in unintuitive application domains.

Currently, companies launch digital transformation initiatives (DTI) to cope with technological changes, challenging competitive environments, increasing customer demands, and other digitalization challenges. The DTI spectrum is broad and covers structural changes (e.g. dedicated digital units) as well as contextual changes (e.g. overarching cultural change programs). Often companies launch multiple concurrent DTIs resulting in considerable organizational complexity. However, research on how to manage the interplay of DTIs successfully is still scarce. Therefore, we distinguish three coordination aspects (i.e. strategic alignment, governance, communication & culture) to manage DTIs' interplay. Drawing on organizational and IS research as well as on a single case study with eight interviews, we conceptualize DTIs as manifestations of digital transformation. We show that multiple concurrent DTIs can foster structural and contextual ambidexterity, i.e. leading to hybrid ambidexterity in organizations. Thereby, we contribute to a better understanding of DTIs, their interplay, and their value to increase hybrid ambidexterity.

Incumbent companies are launching digital transformation initiatives (DTIs) to cope with technological changes, challenging competitive environments, increasing customer demands, and other digitalization challenges. The DTI spectrum is broad and covers structural and contextual changes. Companies often launch multiple. concurrent DTIs, resulting in considerable organizational complexity. However, there has been very little research into the successful management of the interplay between DTIs. Drawing on five management aspects (strategic alignment, governance, methods/IT, people, and culture) and insights from three case companies, we elucidate DTIs’ interplay, illustrating that beneficial DTI interplay management leads to a complementary duality instead of a competing dualism in organizational ambidexterity. We explicate that multiple concurrent DTIs can foster structural and contextual ambidexterity, which leads to hybrid ambidexterity, concluding that contextual ambidexterity coheres and balances exploration and exploitation efforts. Thereby, we contribute to a better understanding of DTIs, their interplay management, and their roles to foster hybrid ambidexterity.