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Design Thinking for Artificial Intelligence: How Design Thinking Can Help Organizations to Address Common AI Project Challenges

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HCI International 2023 – Late Breaking Papers (HCII 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14059))

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Abstract

The last decade indicates a drastic upswing in the adoption of organizational artificial intelligence (AI). Companies increasingly seize the transformative potential AI entails to enhance the effectiveness and efficiency of various business functions. However, studies show that over 85% of all AI projects in organizations fail to be implemented. Therefore, this study investigates the most common AI project management challenges that prevent organizations from successfully deploying AI initiatives. It further explores how the human-centered innovation method design thinking (DT) can address these challenges. To do so, a multiple-case study with a single-unit of analysis was conducted, whereby twenty representatives from ten companies and startups were interviewed. Findings show that in practice, the six most frequently occurring AI project management challenges are: the lack of education around the topic and the resulting distrust in such technology; the missing user-centricity that leads to the development of undesired solutions; the fact that pre-defined solutions hinder project teams from adequately analyzing the business problem first; the insufficient cross-department collaboration and communication; and the absence of high-quality data. Moreover, it was found, that the four overarching DT elements which help organizations tackle these problems are the Needfinding phase, where relevant stakeholders are interviewed and questioned about their pain-points, wishes, and needs at the beginning of the process; the early Prototype Testing where end users can experience the prototypes themselves; the DT Mindset, which entails human-centered thinking, collaboration, integrity, diversity, and empathy, amongst others; and DT as a Process Structure along which AI-driven projects can be developed. Based on insights from this empirical research, suggestions are made which organizations can follow to directly address AI project management challenges and, thereby, increase their rate of successfully deployed AI projects.

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References

  • Ågerfalk, P.J., et al.: Artificial intelligence – beyond the hype. In: Forty-First International Conference on Information Systems (ICIS), India (2020)

    Google Scholar 

  • Agrawal, A., Gans, J., Goldfarb, A.: Prediction, judgment, and complexity: a theory of decision-making and artificial intelligence. In: The Economics of Artificial Intelligence, pp. 89–114. University of Chicago Press (2019)

    Google Scholar 

  • Boeckle, M., Kouris, I.: Design thinking and AI: a new frontier for designing human-centered AI solutions. In: Proceedings of the Academic Design Management Conference ADMC22 (2022). https://www.dmi.org/page/ADMC2022Proceedings

  • Bouschery, S.G., Blazevic, V., Piller, F.T.: Augmenting human innovation teams with artificial intelligence: exploring transformer-based language models. J. Prod. Innov. Manag. (2023). https://doi.org/10.1111/jpim.12656

    Article  Google Scholar 

  • Brenner, W., Uebernickel, F.: Design thinking as mindset, process, and toolbox. ResearchGate (2016). https://doi.org/10.1007/978-3-319-26100-3_1

    Article  Google Scholar 

  • Brenner, W., van Giffen, B., Koehler, J.: Management of artificial intelligence: feasibility, desirability and viability. In: Aier, S., Rohner, P., Schelp, J. (eds.) Engineering the Transformation of the Enterprise: A Design Science Research Perspective, pp. 15–36. Springer International Publishing (2021)

    Chapter  Google Scholar 

  • Brown, T.: Design thinking. Harv. Bus. Rev. 86(6), 84–92 (2008)

    Google Scholar 

  • Davenport, T.: The AI Advantage: How to Put the Artificial Intelligence Revolution to Work. MIT Press (2018)

    Google Scholar 

  • Eliabayec, U.: Process Planning - From Understanding of Goals to Deployment [Conference presentation]. SwissCognitive - How to Set Up an AI Centre of Excellence, Virtual (2021). https://www.youtube.com/watch?v=Fkl7my0av5U. Accessed 14 Mar 2023

  • Engel, C., van Giffen, B., Ebel, P.: Empirically Exploring the Cause-Effect Relationships of AI Characteristics, Project Management Challenges, and Organisational Change. ResearchGate (2021). https://www.researchgate.net/publication/349431010

  • Foster-Fletcher, R., Silverman, K.: (Hosts). Navigating Technology Beyond Our Understanding (No. 113). In: Boundless (2020)

    Google Scholar 

  • Gerstbach, I., Gerstbach, P.: Design Thinking in IT-Projekten: Agile Problemlösungskompetenz in einer digitalen Welt. Carl Hanser Verlag GmbH & Co, KG (2020)

    Book  Google Scholar 

  • Gioia, D.A., Corley, K.G., Hamilton, A.L.: Seeking qualitative rigor in inductive research: notes on the Gioia methodology. Organ. Res. Methods 16(1), 15–31 (2013)

    Article  Google Scholar 

  • Hagendorff, T., Wezel, K.: 15 challenges for AI: or what AI (currently) can’t do. AI Soc. 35(2), 355–365 (2019). https://doi.org/10.1007/s00146-019-00886-y

    Article  Google Scholar 

  • Howard, C., Rowsell-Jones, A.: CIO Survey: CIOs Have Awoken to the Importance of AI. Gartner Inc. (2019)

    Google Scholar 

  • Janiesch, C., Zschech, P., Heinrich, K.: Machine learning and deep learning. Electron. Mark. 31(3), 685–695 (2021)

    Article  Google Scholar 

  • Kumar, M.: Process planning - from understanding of goals to deployment. SwissCognitive - How to Set Up an AI Centre of Excellence, Virtual (2021)

    Google Scholar 

  • Leff, D., Chapo, C.: What the heck does it even mean to “Do AI”? [Conference presentation]. Venture Beat Transform 2019: Business AI Integration, San Francisco, CA, United States (2019, July 10–11). https://www.youtube.com/watch?v=EzmTZlho-EI. Accessed 14 Mar 2023

  • Lewrick, M.: Design Thinking: Radikale Innovationen in einer digitalisierten Welt (1st ed.). C.H.Beck (2018)

    Google Scholar 

  • Pietrzyk, M.: Process Planning - From Understanding of Goals to Deployment [Conference presentation]. SwissCognitive - How to Set Up an AI Centre of Excellence, Virtual (2021). https://www.youtube.com/watch?v=Fkl7my0av5U. Accessed 14 Mar 2023

  • Piorkowski, D., Park, S., Wang, A.Y., Wang, D., Muller, M., Portnoy, F.: How AI developers overcome communication challenges in a multidisciplinary team. In: Proceedings of the ACM on Human-Computer Interaction, vol. 5(CSCW1), pp. 1–25 (2021).https://doi.org/10.1145/3449205

  • Pumplun, L., Tauchert, C., Heidt, M.: A new organizational chassis for artificial intelligence - exploring organizational readiness factors. In:ECIS 2020 Proceedings, Association for Information Systems (2019)

    Google Scholar 

  • Rai, A., Constantinides, P., Sarker, S.: Next generation digital platforms: toward human-AI hybrids. Manag. Inf. Syst. Q. 43(1), iii–ix (2019)

    Google Scholar 

  • Reis, L., Maier, C., Mattke, J., Creutzenberg, M., Weitzel, T.: Addressing unser resistance would have prevented a healthcare project failure. MIS Quart. Execut. 19(4), 279–296 (2020). https://doi.org/10.17705/2msqe.00038

  • Riedl, M.O.: Human-Centered Artificial Intelligence and Machine Learning. School of Interactive Computing Georgia Institute of Technology (2019)

    Google Scholar 

  • Russell, S.J., Norvig, P., Chang, M., Devlin, J., Dragan, A.: Artificial Intelligence: A Modern Approach (Pearson Series in Artificial Intelligence) (4th ed.). Pearson (2020)

    Google Scholar 

  • Rzepka, C., Berger, B.: User interaction with AI-enabled systems: a systematic review of IS research (39 ICIS). In: International Conference of Information Systems (2018)

    Google Scholar 

  • Shollo, A., Hopf, K., Thiess, T., Müller, O.: Shifting ML value creation mechanisms: a process model of ML value creation. J. Strateg. Inf. Syst. 31(3), 101734 (2022)

    Article  Google Scholar 

  • Stackowiak, R., Kelly, T.: Design Thinking in Software and AI Projects: Proving Ideas Through Rapid Prototyping (1st ed.). Apress (2020)

    Google Scholar 

  • Strauss, A., Corbin, J.M.: Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory. SAGE Publications (1998)

    Google Scholar 

  • Sturm, T., Peters, F.: The Impact of Artificial Intelligence on Individual Performance: Exploring the Fit between Task, Data, and Technology (ECIS 2020 Proceedings). Association for Information Systems (2020)

    Google Scholar 

  • Vial, G., Jiang, J., Giannelia, T., Cameron, A.-F.: The Data Problem Stalling AI. MIT Sloan Management Review (2021)

    Google Scholar 

  • Verganti, R., Vendraminelli, L., Iansiti, M.: Innovation and design in the age of artificial intelligence. J. Prod. Innov. Manag. 37(3), 212–227 (2020)

    Article  Google Scholar 

  • von Krogh, G.: Artificial intelligence in organizations: new opportunities for phenomenon-based thinking. ETH Zürich Res. Collect. (2018). https://doi.org/10.3929/ethz-b-000320207

    Article  Google Scholar 

  • vom Brocke, J., et al.: Reconstructing the giant: On the importance of rigour in documenting the literature search process. aisel.aisnet.org (2009). https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1145&context=ecis2009

  • vom Brocke, J., Simons, A., Riemer, K., Niehaves, B., Plattfaut, R., Cleven, A.: Standing on the shoulders of giants: challenges and recommendations of literature search in information systems research. Commun. Assoc. Inf. Syst. 37(1), 9 (2015)

    Google Scholar 

  • Webster, J., Watson, R.T.: Analyzing the past to prepare for the future: writing a literature review. Manag. Inf. Syst. Q. 26(2), xiii–xxiii (2002)

    Google Scholar 

  • Weiner, J.: Why AI/Data Science Projects Fail (1st ed.). Morgan & Claypool Publishers (2021). https://doi.org/10.2200/S01070ED1V01Y202012CAN001

  • Westenberger, J., Schuler, K., Schlegel, D.: Failure of AI projects: understanding the critical factors. Procedia Comput. Sci. 196, 69–76 (2022)

    Article  Google Scholar 

  • Wiesche, M., Lang, M., Uebernickel, F., Bryler, E.: Teaching Innovation in Interdisciplinary Environments: Towards a Design Thinking Syllabus. ResearchGate (2018). https://www.researchgate.net/publication/328252662

  • Yoo, Y.: Design thinking for IS research, in: editor’s comments: diversity of design science research. MIS Q. 41(1), iii–xviii (2017)

    Google Scholar 

  • Ziegler, M., Rossmann, S., Steer, A., Danzer, S.: Leading the Way to an AI-driven Organization. Porsche Consulting (2019)

    Google Scholar 

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Authors and Affiliations

  1. Boston Consulting Group (BCG), Munich, Germany

    Laura Staub

  2. Institute of Information Management, University of St. Gallen, St. Gallen, Switzerland

    Benjamin van Giffen & Simon Sturm

  3. Institute for Digital Technology Management, Bern University of Applied Sciences, Bern, Switzerland

    Jennifer Hehn

Authors
  1. Laura Staub
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  2. Benjamin van Giffen
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  3. Jennifer Hehn
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  4. Simon Sturm
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Corresponding author

Correspondence to Simon Sturm .

Editor information

Editors and Affiliations

  1. Siemens Corporation, Princeton, NJ, USA

    Helmut Degen

  2. Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Stavroula Ntoa

  3. San Jose State University, San Jose, CA, USA

    Abbas Moallem

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Staub, L., van Giffen, B., Hehn, J., Sturm, S. (2023). Design Thinking for Artificial Intelligence: How Design Thinking Can Help Organizations to Address Common AI Project Challenges. In: Degen, H., Ntoa, S., Moallem, A. (eds) HCI International 2023 – Late Breaking Papers. HCII 2023. Lecture Notes in Computer Science, vol 14059. Springer, Cham. https://doi.org/10.1007/978-3-031-48057-7_16

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  • DOI: https://doi.org/10.1007/978-3-031-48057-7_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-48056-0

  • Online ISBN: 978-3-031-48057-7

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