In the big data era, managers are exposed to an increasing amount of structured and unstructured information that they must process daily to make decisions. In this context, artificial intelligence (AI) functionalities can support managerial information processing (IP), which forms the basis of managers' decision-making. To date, little is known about the themes that managers face when integrating AI into their IP and decision-making. The present paper identifies these through three focus group interviews with managers from the financial industry, validates them through a survey and derives organizational implications. The results imply that organizations should (1) evaluate managerial IP tasks and matching AI systems, (2) (re)define roles for managers and AI systems, and (3) redesign management processes for sustainable human-AI interaction.

PurposeAI-based robo-advisory (RA) represents a FinTech application that is already replacing retail investment advisors. In private banking (PB), clients also increasingly expect service provision across different digital channels, but with a higher degree of personalization. Hence, the present study investigates the impact of intelligent RA on the PB investment advisory process to derive both process (re)design knowledge and strategic guidance for artificial intelligence (AI) usage for PB investment advisory.Design/methodology/approachThe present study applies an AI process impact analysis approach by decomposing AI-based RA into three AI application types: conversational agent, customer segmentation and predictive analytics. The analysis results along a reference PB investment advisory process reveal sub-process transformations which are applied for process redesign integrating AI.FindingsThe study results imply that AI systems (1) enable seamless client journeys, (2) increase advisor flexibility, (3) support the client–advisor relationship by applying an omnichannel approach and (4) demand advisor skills to be augmented with technical and statistical knowledge.Originality/valueThe research study contributes (1) an AI process impact analysis approach, (2) derives process (re)design knowledge for AI deployment and (3) develops strategic guidance for AI usage in PB investment advisory.

This research introduces two artefacts that contribute to the common understanding of Artificial Intelligence (AI) and aim to provide guidance for designing AI applications. On the one hand, the periodic table of AI structures the broad spectrum of AI technologies and an AI application design model supports the business-oriented conception of AI technologies. Both artefacts are key for the development of an AI impact analysis model to evaluate further organizational impacts and potentials for redesign. The research was motivated by the findings of a survey on AI application examples in a research consortium consisting of German, Swiss and Austrian bank and IT provider managers and a business user group of a Swiss private bank. Both artefacts showed to be helpful tools for change management and IT/business architects.

No abstract

No abstract