%0 Journal Article %J Technology Innovation Management Review %D 2019 %T Leveraging AI-based Decision Support for Opportunity Analysis %A Wolfgang Groher %A Friedrich-Wilhelm Rademacher %A AndrĂ© Csillaghy %K front-end of innovation; environmental scanning; information processing; opportunity; innovation search field; information retrieval; artificial intelligence; decision-making; latent semantic indexing; design-science %X The dynamics and speed of change in corporate environments have increased. At the front-end of innovation, firms are challenged to evaluate growing amounts of information within shorter time frames in order to stay competitive. Either they spend significant time on structured data analysis, at the risk of delayed market launch, or they follow their intuition, at the risk of not meeting market trends. Both scenarios constitute a significant risk for a firm’s continued existence. Motivated by this, a conceptual model is presented in this paper that aims at remediating these risks. Grounded on design science methodology, it concentrates on previous assessments of innovation search fields. These innovation search fields assist in environmental scanning and lay the foundation for deciding which opportunities to pursue. The model applies a novel AI-based approach, which draws on natural language processing and information retrieval. To provide decision support, the approach includes market-, technology-, and firm-related criteria. This allows us to replace intuitive decision-making by fact-based considerations. In addition, an often-iterative approach for environmental scanning is replaced by a more straightforward process. Early testing of the conceptual model has shown results of increased quality and speed of decision-making. Further testing and feedback is still required to enhance and calibrate the AI-functionality. Applied in business environments, the approach can contribute to remediate fuzziness in early front-end activities, thus helping direct innovation managers to “do the right things”. %B Technology Innovation Management Review %I Talent First Network %C Ottawa %V 9 %P 29-35 %8 12/2019 %G eng %U timreview.ca/article/1289 %N 12 %1 University of Applied Sciences, St. Gallen Wolfgang Groher holds a position as lecturer and researcher for business informatics at the University of Applied Sciences St. Gallen, Switzerland. His primary research interest lies in the front-end of innovation and supporting it with data science-based approaches. This includes the topic of identifying weak signals for strategic foresight. He holds a diploma as business engineer from the University of Karlsruhe and has many years of international industry experience in IT-, SCM- and consulting positions at Siemens. Within the Swiss association VNL for logistics professionals he is heading the expert group for logistics innovation. %2 University of Applied Sciences FHNW Friedrich-W. Rademacher is a lecturer and professor for production and logistics systems at the University of Applied Sciences FHNW Northwestern Switzerland in Windisch, Switzerland. His scientific focus lies on innovation of logistics processes. He was awarded a PhD at the TU Dortmund and holds an engineering diploma from the Ruhr University Bochum. He has extensive industrial experience as a managing director in the telecommunications and public transport sectors. %3 University of Applied Sciences FHNW AndrĂ© Csillaghy is the head of the Institute for Data Science at the University of Applied Sciences FHNW Northwestern Switzerland. He has been working on data systems from diverse origins for the last two decades. His primary interests are data pipelines, machine learning, and applications on very large data sets. He graduated in Computer Science at ETH Zurich, moved to the University of California, Berkeley, before joining the faculty at FHNW. %& 29 %R http://doi.org/10.22215/timreview/1289