English
Bibtex
Cite as text
@Select Types{,
Journal = "Band-1",
Title= "Developing Purposeful AI Use Cases – A Structured Method and Its Application in Project Management",
Author= "Peter Hofmann, Jan Jöhnk, Dominik Protschky, and Nils Urbach",
Doi= "https://doi.org/10.30844/wi_2020_a3-hofmann",
Abstract= "An appropriate problem-solution-fit is essential to develop purposeful artificial intelligence (AI) applications. However, in domains with an unintuitive problem-solution-fit, such as project management (PM), organizations require methodological guidance. Hence, we propose a five-step method to develop organization-specific AI use cases: First, companies must consider the context factors technology, organization (in particular data and application domain), and environment. Second, companies must identify existing domain problems and AI solutions. Third, our method facilitates abstraction to understand the underlying nature of the identified problems and AI solutions. Fourth, our problem-solution-matrix assists companies to match AI functions with the domain context. Fifth, companies derive necessary implications for the subsequent use case implementation. To construct and evaluate our method, we followed the design science research paradigm complemented by situational method engineering and based on 14 interviews. Our method addresses a relevant practical problem and contributes to identifying purposeful AI use cases in unintuitive application domains.",
Keywords= "Artificial Intelligence, Project Management, Use Case Development, Design Science Research, Situational Method Engineering.
",
}
Peter Hofmann, Jan Jöhnk, Dominik Protschky, and Nils Urbach: Developing Purposeful AI Use Cases – A Structured Method and Its Application in Project Management. Online: https://doi.org/10.30844/wi_2020_a3-hofmann (Abgerufen 18.04.24)
Open Access
Abstract
Abstract
An appropriate problem-solution-fit is essential to develop purposeful artificial intelligence (AI) applications. However, in domains with an unintuitive problem-solution-fit, such as project management (PM), organizations require methodological guidance. Hence, we propose a five-step method to develop organization-specific AI use cases: First, companies must consider the context factors technology, organization (in particular data and application domain), and environment. Second, companies must identify existing domain problems and AI solutions. Third, our method facilitates abstraction to understand the underlying nature of the identified problems and AI solutions. Fourth, our problem-solution-matrix assists companies to match AI functions with the domain context. Fifth, companies derive necessary implications for the subsequent use case implementation. To construct and evaluate our method, we followed the design science research paradigm complemented by situational method engineering and based on 14 interviews. Our method addresses a relevant practical problem and contributes to identifying purposeful AI use cases in unintuitive application domains.
Keywords
Schlüsselwörter
Artificial Intelligence, Project Management, Use Case Development, Design Science Research, Situational Method Engineering.
References
Referenzen
1. Hofmann, P., Oesterle, S., Rust, P., Urbach, N.: Machine Learning Approaches along the Radiology Value Chain – Rethinking Value Propositions. Proceedings of the 27th European Conference on Information Systems (ECIS) (2019)
2. Rzepka, C., Berger, B.: User Interaction with AI-enabled Systems: A Systematic Review of IS Research. Proceedings of the 39th International Conference on Information Systems (ICIS) (2018)
3. Pumplun, L., Tauchert, C., Heidt, M.: A New Organizational Chassis for Artificial Intelligence – Exploring Organizational Readiness Factors. Proceedings of the 27th European Conference on Information Systems (ECIS) (2019)
4. AlSheibani, S., Cheung, Y., Messom, C.: Artificial Intelligence Adoption: AI-readiness at Firm-Level. Proceedings of the Twenty-Second Pacific Asia Conference on Information Systems (PACIS) (2018)
5. Project Management Institute: Guide to the project management body of knowledge. The Stationery Office Ltd, London (2018)
6. Russell, S., Norvig, P.: Artificial Intelligence. A Modern Approach. Pearson Education UK, Edinburgh (2016)
7. Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE transactions on pattern analysis and machine intelligence 35, 1798– 1828 (2013)
8. Barcelos Tronto, I.F. de, da Silva, J.D.S., Sant’Anna, N.: An investigation of artificial neural networks based prediction systems in software project management. Journal of Systems and Software 81, 356–367 (2008)
9. Perini, A., Susi, A., Avesani, P.: A Machine Learning Approach to Software Requirements Prioritization. IEEE Transactions on Software Engineering 39, 445–461 (2013)
10. Chan, F.T.S., Chan, M.H., Tang, N.K.H.: Evaluation methodologies for technology selection. Journal of Materials Processing Technology 107, 330–337 (2000)
11. Collins, M., Williams, L.: A Three-Stage Filter for Effective Technology Selection. Research-Technology Management 57:3, 36–42 (2014)
12. Yap, C.M., Souder, W.E.: A filter system for technology evaluation and selection. Technovation 13, 449–469 (1993)
13. Brinkkemper, S.: Method engineering: engineering of information systems development methods and tools. Information and Software Technology 38, 275–280 (1996)
14. Gregor, S., Hevner, A.R.: Positioning and presenting design science research for maximum impact. MIS Quarterly 37, 337–355 (2013)
15. Vargas, R.: Applying Neural Networks and Analogous Estimating to Determine the Project Budget. PMI Global Congress (2015)
16. Nayebi, M., Kabeer, S.J., Ruhe, G., Carlson, C., Chew, F.: Hybrid Labels are the New Measure! IEEE Software 35, 54–57 (2018)
17. Fauser, J., Schmidthuysen, M., Scheffold, B.: The Prediction of Success in Project Management. Predictive Project Analytics. Projektmanagement aktuell 26, 66–74 (2015)
18. Shivaji, S., Whitehead, E.J., Akella, R., Kim, S.: Reducing Features to Improve Code Change-Based Bug Prediction. IEEE Transactions on Software Engineering 39, 552–569 (2013)
19. Auth, G., Jokisch, O., Dürk, C.: Revisiting automated project management in the digital age – a survey of AI approaches. Online Journal of Applied Knowledge Management 7, 27–39 (2019)
20. Stillman, H.M.: How ABB Decides on the Right Technology Investments. Research- Technology Management 40, 14–22 (1997)
21. Friedrich, T., Overhage, S., Schlauderer, S., Eggs, H.: Selecting Technologies for Social Commerce: Towards a Systematic Method. Proceedings of the 23rd European Conference on Information Systems (ECIS) (2015)
22. Shehabuddeen, N., Probert, D., Phaal, R.: From theory to practice: challenges in operationalising a technology selection framework. Technovation 26, 324–335 (2006)
23. Shen, Y.-C., Chang, S.-H., Lin, G.T.R., Yu, H.-C.: A hybrid selection model for emerging technology. Technological Forecasting and Social Change 77, 151–166 (2010)
24. Fridgen, G., Lockl, J., Radszuwill, S., Rieger, A., Schweizer, A., Urbach, N.: A Solution in Search of a Problem – A Method for the Development of Blockchain Use. 24th Americas Conference on Information Systems (AMCIS) (2018)
25. Bitkom: Digitalisierung gestalten mit dem Periodensystem der Künstlichen Intelligenz. Ein Navigationssystem für Entscheider (2018)
26. Hammond, K.J.: The Periodic Table of AI, https://www.datasciencecentral.com/profiles/ blogs/the-periodic-table-of-ai (2017)
27. Christensen, C.M.: The Innovator’s Dilemma. When New Technologies Cause Great Firms to Fail. Harvard Business Review Press, Boston (2013)
28. Hevner, A.R., March, S.T., Park, J., Ram, S.: Design Science in Information Systems Research. MIS Quarterly 28, 75–105 (2004)
29. March, S.T., Smith, G.F.: Design and natural science research on information technology. Decision Support Systems 15, 251–266 (1995)
30. Henderson-Sellers, B., Ralyté, J.: Situational Method Engineering: State-of-the-Art Review. Journal of Universal Computer Science 16, 424–478 (2009)
31. Denner, M.-S., Püschel, L.C., Röglinger, M.: How to Exploit the Digitalization Potential of Business Processes. Business & Information Systems Engineering 60, 331–349 (2018)
32. Hofmann, P., Keller, R., Urbach, N.: Inter-technology relationship networks: Arranging technologies through text mining. Technological Forecasting and Social Change 143, 202– 213 (2019)
33. Ralyté, J., Deneckère, R., Rolland, C.: Towards a Generic Model for Situational Method Engineering. In: Eder, J., Missikoff, M. (eds.) Advanced Information Systems Engineering. 15th International Conference, CAiSE 2003, 141, pp. 95–110. Springer, Berlin and Heidelberg, Germany (2003)
34. Myers, M.D., Newman, M.: The qualitative interview in IS research: Examining the craft. Information and Organization 17, 2–26 (2007)
35. Bhattacherjee, A.: Social Science Research: Principles, Methods, and Practices. Scholar Commons, University of South Florida, Tampa, Florida (2012)
36. Baker, J.: The Technology–Organization–Environment Framework. In: Dwivedi, Y.K., Wade, M.R., Schneberger, S.L. (eds.) Information Systems Theory, 28, pp. 231–245. Springer New York, New York, NY (2012)
37. Zhu, K., Kraemer, K.L.: Post-Adoption Variations in Usage and Value of E-Business by Organizations: Cross-Country Evidence from the Retail Industry. Information Systems Research 16, 61–84 (2005)
38. Neftci, E.O., Averbeck, B.B.: Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019)
39. High-Level Expert Group on Artificial Intelligence: Ethics Guidelines for Trustworthy AI (2019)
40. Nelson, R.R.: IT Project Management – Infamous Failures Classic Mistakes, and Best Practices. MIS Quarterly Executive 6, 67–78 (2007)
41. Basten, D., Pankratz, O., Joosten, D.: Assessing the Assessors – An Overview And Evaluation Of IT Project Success Reports. Proceedings of the 21st European Conference on Information Systems (ECIS) (2013)
42. Pankratz, O., Basten, D.: Ladder to success – eliciting project managers’ perceptions of IS project success criteria. International Journal of Informations Systems and Project Management 2, 5–24 (2014)
43. Belassi, W., Tukel, O.I.: A new framework for determining critical success/failure factors in projects. International Journal of Project Management 14, 141–151 (1996)
44. Pinto, J.K., Mantel, S.J.: The causes of project failure. IEEE Transactions on Engineering Management 37, 269–276 (1990)
45. Yeo, K.T.: Critical failure factors in information system projects. International Journal of Project Management 20, 241–246 (2002)
46. Zucker, J.-D.: A grounded theory of abstraction in artificial intelligence. Philosophical transactions of the Royal Society of London. Series B, Biological sciences 358, 1293–1309 (2003)
47. Saitta, L., Zucker, J.-D.: Abstraction in Artificial Intelligence and Complex Systems. Springer New York, New York, NY (2013)
48. Mair, C., Martincova, M., Shepperd, M.: An Empirical Study of Software Project Managers Using a Case-Based Reasoner. Proceedings of the 45th Hawaii International Conference on System Sciences (HICSS), 1030–1039 (2012)
49. Carroll, J.B.: Human cognitive abilities. A survey of factor-analytic studies. Cambridge Univ. Press, Cambridge (2004)
50. Gerrig, R., Zimbardo, P., Campbell, A., Cumming, S., Wilkes, F.: Psychology and Life. Pearson Education Australia, Melbourne (2012)
51. Lahmann, M., Keiser, P., Stierli, A.: AI will transform project management. Are you ready? (2018)