Ready or Not, AI Comes— An Interview Study of Organizational AI Readiness Factors

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 adop- tion 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

Opportunity-led ideation: How to convert corporate opportunities into innovative ideas

Opportunities, that is, action possibilities for innovative business models, goods, services and processes, particularly affect idea generation, which is vital for innovation success. Capitalizing on opportunities requires complementing predominating problem-centred innovation approaches. Despite mature knowledge on idea generation, there is still a limited understanding on how to leverage opportunities. Hence, there is a limited set of methods available that provide formalized guidance. To address this gap, we co-developed an opportunity-led ideation method in an action design research project with one of Australia's leading financial service providers. Thanks to this immersive collaboration, our method reflects not only the intent of researchers and existing knowledge but also the influence and needs of practitioners. Building on established opportunity sources from the literature, this method structures the idea generation stage of the innovation process into the activities initiation, immersion, investigation and integration. The method provides guidance on how to transform opportunities into ideas and presents activities, techniques, tools and roles that are important within the idea generation stage. Our research theoretically extends the understanding of opportunity identification within the front end of innovation. Moreover, it provides insights on balancing formalization and creativity within idea generation. Organizations can use the method as a blueprint to systematically and proactive sense, assess and translate opportunities into ideas.</p

Cooperation for innovativeness in SMEs : a taxonomy for cooperation design

Various resource constraints of small and medium-sized enterprises (SMEs) highlight the strategy of cooperation for innovation as it enhances organisations' options and breadth of knowledge sources. Nevertheless, research lacks guidance on why, with whom, and how to cooperate and has so far not provided a comprehensive overview of the characteristics of cooperation to foster SMEs' innovativeness. We build a taxonomy based on deductive and inductive iterations. The taxonomy incorporates insights from literature including information science, innovation management, and organisational science. Further it represents insights from practitioners on cooperation for innovation. Our taxonomy delineates the design options for practitioners and advises that one select organisation-specific parameters. With this taxonomy, we conceptually structure existing research and empower practitioners to analyse their current cooperation projects, reconsider them, and gain knowledge to design new ways of cooperation that best suit their aims.</p

How Digital is Social? Taking Advantage of Digital for Social Purposes

Today’s customers are highly aware of and sensitive to social topics. Thus, they expect organizations across all industries not only to avoid social inequalities but to react with distinct actions against social inequalities, i.e. to strive for social innovation. Moreover, digital technologies can help to leverage social innovation more easily. There are already first examples of incumbents fostering digital social innovation. Merck, for example, introduced a sticking plaster with sensors to support diabetes patients in analysing their intestinal fluids without injection. Although the potentials of digital technologies in addressing social issues seem to be obvious, research on digital social innovation is still in its infancy, and clear guidance on how to exploit the potential of digital social innovation is missing. As such, a common understanding in terms of theoretical and managerial implications is scarce. We propose a taxonomy in order to structure the research field and provide incumbents with a tool on how to address their social responsibility through digital social innovation. Thus, our study contributes to descriptive knowledge and delivers insights relevant to the practice of digital social innovation

Ex ante assessment of disruptive threats: Identifying relevant threats before one is disrupted

The shortening of product life-cycles accompanied by the rapid development of new products and dissolving industry boundaries are indicative of a multitude of potentially disruptive threats. The survival of incumbents depends on their capability to effectively anticipate and manage such threats. Thus, the early anticipation of disruptive threats to react or prepare for their impacts is a crucial topic in practice and academia. Although the current body of knowledge provides numerous approaches to disruption anticipation, a comprehensive conceptualisation of the evolution of disruptive threats is missing. Moreover, incumbents lack guidance on how to effectively anticipate disruptive threats. To address this gap, we propose the Disruption Evolution Framework (DEF), which conceptualises the course of disruptive threats along three phases (i.e. threat possible, apparent, and materialised) as well as distinguishes four interrelated categories of signals (i.e. context, catalyst, capability, and company signals) and threats (i.e. customer, competitor, product, and policy threats). Building on the DEF, we also propose the Disruptability Assessment Method (DAM), which enables incumbents to systematically assess disruptive threats via a step-by-step procedure. We evaluated the DAM in the Corporate Development and the Global Digital Partnerships departments of an insurance company. Overall, our work contributes to the descriptive and prescriptive knowledge on disruption anticipation