Leveraging AI-based Decision Support for Opportunity Analysis

Abstract

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”.

Keywords

• front-end of innovation; environmental scanning; information processing; opportunity; innovation search field; information retrieval; artificial intelligence; decision-making; latent semantic indexing; design-science