On the Journey to AI Maturity
Understanding the Role of Enterprise Artificial Intelligence Service
DOI:
https://doi.org/10.30844/aistes.v6i1.26Keywords:
AI, Enterprise AI, Enterprise System, Implementation Challenges, Natural Lan-guage Processing, Topic ModellingAbstract
Artificial Intelligence (AI) has recently become pivotal in day-to-day business. However, surveys show that underlying, systemic issues – aside from AI-related aspects – hinder its enterprise-wide adoption. In this study, we aim to understand the role of this new breed of providers on the path to AI maturity and enterprise-wide adoption. We collect secondary data (i.e., surveys) from 154 white papers published by companies implementing AI solutions and apply descriptive and thematic analysis to understand the current challenges and opportunities of AI implementation. The thematic analysis involves topic modelling using natural language-processing algorithms. Our results demonstrate that, despite AI service providers addressing – at least in part – the major challenges faced by clients, there is still a gap between the skills demanded by end-users, and skills possessed by and focused on AI service providers.
References
Statista: Global total corporate artificial intelligence (AI) investment from 2015 to 2021. (2022). https://www.statista.com/statistics/941137/ai-investment-and-funding-worldwide/, last accessed 24 October 2022.
PWC: Sizing the prize What’s the real value of AI for your business and how can you capitalise? (2017). https://www.pwc.com/gx/en/issues/analytics/assets/pwc-ai-analysis-sizing-the-prize-report.pdf, last accessed 24 October 2022.
Janakiram, M.: Gartner's 2020 Magic Quadrant for data science and machine learning platforms has many surprises. Forbes (2020, February 20). https://www.forbes.com/sites/janakirammsv/2020/02/20/gartners-2020-magic-quadrant-for-data-science-and-machine-learning-platforms-has-many-surprises/?sh=6a08bd0b3f55, last accessed 5 September 2021.
EY: The growing impact of AI on business. (2018, April 30). https://www.technologyreview.com/2018/04/30/143136/the-growing-impact-of-ai-on-business, last accessed 5 September 2021.
Sharma, M.: Navigating the new landscape of AI platforms. Harvard Business Review (2020, March 10). https://hbr.org/2020/03/navigating-the-new-landscape-of-ai-platforms, last accessed 5 September 2021.
Accenture: Unleashing exponential evolution - 2019 ERP Trends. Accenture (2019). https://www.accenture.com/_acnmedia/PDF-90/Accenture-Unleashing-Exponential-Evolution-PDF.pdf, last accessed 5 September 2021.
Odusote, A., Tiwari, A., Arora, G.: The Internet of Things: Moving from cost savings to revenue generation. (2020). https://www2.deloitte.com/us/en/pages/finance/articles/cfo-insights-internet-of-things-cost-savings-revenue-generation.html, last accessed 5 September 2021.
Allied Analytics LLP: Enterprise artificial intelligence market: Global opportunity analysis and industry forecast, 2019-2026. Allied Analytics LLP (2019). https://www.researchandmarkets.com/reports/4989422/enterprise-artificial-intelligence-market-global, last accessed 5 September 2021.
Renno, P., Sinha, V.: Will the pandemic accelerate adoption of artificial intelligence? Bain & Company (2020, May 26). https://www.bain.com/insights/will-the-pandemic-accelerate-adoption-of-artificial-intelligence/, last accessed 5 September 2021.
Gartner, I.: Drive Strategic Mandates for AI in the Enterprise. (2020). https://www.gartner.com/en/webinars/3988416/drive-strategic-mandates-for-ai-in-the-enterprise, last accessed 25 October 2022.
Fountaine, T., McCarthy, B., Saleh, T.: Building the AI-powered organization. Harvard Business Review July–August, 62–73 (2019).
Ammanath, B., Hupfer, S., Jarvis, D.: Thriving in the era of pervasive AI. Deloitte's State of AI in the Enterprise, 3rd Edition. (2020). https://www2.deloitte.com/content/dam/Deloitte/cn/Documents/about-deloitte/deloitte-cn-dtt-thriving-in-the-era-of-persuasive-ai-en-200819.pdf, last accessed 5 September 2021.
Dataiku: Making enterprise AI an organizational asset. (2021). https://www.dataiku.com/stories/enterprise-ai/, last accessed 5 September 2021.
Nkhoma, M., Dang, D.: Contributing factors of cloud computing adoption: A technology-organization-environment framework approach. International Journal of Information Systems and Engineering 1(1), 38–49 (2013).
Awa, H.O., Ukoha, O., Igwe, S.R.: Revisiting technology-organization-environment (T-O-E) theory for enriched applicability. The Bottom Line 30(1), 2–22 (2017). doi: 10.1108/BL-12-2016-0044.
McCarthy, J., Minsky, M., Rochester, N., Shannon, C.: A proposal for the dartmouth summer research project on artificial intelligence. (1955, August 31). http://jmc.stanford.edu/articles/dartmouth/dartmouth.pdf, last accessed 5 September 2021.
Buchanan, B.G.: A (very) brief history of artificial intelligence. AI Magazine 26(4), 53 (2005). doi: 10.1609/aimag.v26i4.1848.
Ardila, D., Kiraly, A.P., Bharadwaj, S., Choi, B., Reicher, J.J., Peng, L., et al.: End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nature Medicine 25(6), 954–961 (2019). doi: 10.1038/s41591-019-0447-x.
Magnin, C.: How AI can unlock a $127B opportunity by reducing food waste. McKinsey Sustainability. McKinsey (2019, March 27). https://www.mckinsey.com/business-functions/sustainability/our-insights/sustainability-blog/how-ai-can-unlock-a-127b-opportunity-by-reducing-food-waste#, last accessed 5 September 2021.
Joshi, N.: How AI can transform the transportation industry. Forbes (2019, July 26). https://www.forbes.com/sites/cognitiveworld/2019/07/26/how-ai-can-transform-the-transportation-industry/#205087e74964, last accessed 5 September 2021.
Hand, D.: Introduction. In: Hand D, (ed.). Artificial intelligence and psychiatry. p. 1–20. Cambridge University Press, Cambridge (1985).
Russell, S., Norvig, P., (eds.): Artificial intelligence: A modern approach. Prentice Hall, Englewood Cliffs (1994).
Gartner, I.: Artificial intelligence (AI). (2021). https://www.gartner.com/en/information-technology/glossary/artificial-intelligence, last accessed 5 September 2021.
Agrawal, A., Gans, J., Goldfarb, A.: Prediction machines: The simple economics of artificial intelligence. Harvard Business Review Press, Boston, MA, United States (2018).
Brady, M., Gerhardt, L.A., Davidson, H.F., (eds.): Robotics and artificial intelligence. NATO ASI Series F: Computer and System Sciences. Springer-Verlag, Berlin (1983).
Liu, J., Kong, X., Xia, F., Bai, X., Lei, W., Qing, Q., et al.: Artificial intelligence in the 21st century. IEEE Access 6, 34403–34421 (2018). doi: 10.1109/ACCESS.2018.2819688.
Walch, K.: Rethinking weak vs. strong AI. Forbes (2019, October 04). https://www.forbes.com/sites/cognitiveworld/2019/10/04/rethinking-weak-vs-strong-ai/#60ad4b796da3, last accessed 5 September 2021.
Robert Jacobs, F., Weston, F.C.: Enterprise resource planning (ERP)—A brief history. Journal of Operations Management 25(2), 357–363 (2007). doi: 10.1016/j.jom.2006.11.005.
Gartner, I.: Enterprise resource planning (ERP). (2021). https://www.gartner.com/en/information-technology/glossary/enterprise-resource-planning-erp, last accessed 5 September 2021.
Umble, E.J., Haft, R.R., Umble, M.M.: Enterprise resource planning: Implementation procedures and critical success factors. European Journal of Operational Research 146(2), 241–257 (2003). doi: 10.1016/S0377-2217(02)00547-7.
Lynne, M., Tanis, C., van Fenema, P.: Enterprise resource planning: Multisite ERP implementations. Communications of the ACM 43(4), 42–46 (2000). doi: 10.1145/332051.332068.
Loizos, C.: ERP: Is it the ultimate software solution. Industry Week 7(33), 33–48 (1998).
Al-Mashari, M., Al-Mudimigh, A., Zairi, M.: Enterprise resource planning: A taxonomy of critical factors. European Journal of Operational Research 146(2), 352–364 (2003). doi: 10.1016/S0377-2217(02)00554-4.
Matolcsy, Z.P., Booth, P., Wieder, B.: Economic benefits of enterprise resource planning systems: Some empirical evidence. Accounting & Finance 45(3), 439–456 (2005). doi: 10.1111/j.1467-629X.2005.00149.x.
Stratman, J.K.: Realizing benefits from enterprise resource planning: Does strategic focus matter? Production and Operations Management 16(2), 203–216 (2007). doi: 10.1111/j.1937-5956.2007.tb00176.x.
Gartner, I.: Gartner Magic Quadrant & critical capabilities. (2021). https://www.gartner.com/en/research/magic-quadrant?utm_expid=.KduDEZhhQCu-BkCJKlBIqg.0&utm_referrer=https%3A%2F%2Fwww.google.com%2F, last accessed 5 September 2021.
C3.ai: 10 core principles of enterprise AI. C3.ai (2020). https://c3.ai/wp-content/uploads/2020/12/Principles-of-Enterprise-AI.pdf, last accessed 5 September 2021.
Kureishy, A.: Why enterprise AI will be highly differentiating. Teradata (2018, January 24). https://www.teradata.com/Blogs/Why-Enterprise-AI-Will-Be-Highly-Differentiating, last accessed 5 September 2021.
Databricks: Unified data analytics platform. (2021). https://databricks.com/product/unified-data-analytics-platform, last accessed 5 September 2021.
Bisson, P., Hall, B., McCarthy, B., Rifai, K.: Breaking away: The secrets to scaling analytics. (2018, May 22). https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/breaking-away-the-secrets-to-scaling-analytics#, last accessed 5 September 2021.
Hill, S., Evans, P., Don, R., Zaman, N., Congalton, A.: AI transforming the enterprise—Eight key AI adoption trends. (2019). https://advisory.kpmg.us/articles/2019/ai-transforming-enterprise.html, last accessed 5 September 2021.
Kar, S., Kar, A.K., Gupta, M.P.: Modeling Drivers and Barriers of Artificial Intelligence Adoption: Insights from a Strategic Management Perspective. Intelligent Systems in Accounting, Finance and Management 28(4), 217–238 (2021). doi: 10.1002/isaf.1503.
Alsheiabni, S., Cheung, Y., Messom, C.: Factors inhibiting the adoption of artificial intelligence at organizational-level: A preliminary investigation. AMCIS 2019 Proceedings 2, (2019). doi: https://aisel.aisnet.org/amcis2019/adoption_diffusion_IT/adoption_diffusion_IT/2/.
Kushwaha, A.K., Kar, A.K.: Micro-foundations of Artificial Intelligence Adoption in Business: Making the Shift. In: Sharma SK, Dwivedi YK, Metri B, Rana NP, (eds.) Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation. p. 249–260. Springer International, Cham, CH (2020).
Shinyama, Y.: PDFminer. Python Software Foundation (2019, November 25). https://pypi.org/project/pdfminer/, last accessed 5 September 2021.
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to information retrieval. Cambridge University Press, Cambridge (2012).
Martin, F., Johnson, M.: More efficient topic modelling through a noun only approach. Proceedings of Australasian Language Technology Association Workshop. p. 111–115. ALTA, Parramatta (2015).
Vayansky, I., Kumar, S.A.: A review of topic modeling methods. Information Systems 94(101582), (2020). doi: 10.1016/j.is.2020.101582.
Aggarwal, C.C., Zhai, C.: Mining Text Data. Springer US, Boston, MA (2012).
Blei, D.M.: Probabilistic topic models. Communications of the ACM 55(4), 77–84 (2012). doi: 10.1145/2133806.2133826.
Pietsch, A.-S., Lessmann, S.: Topic modeling for analyzing open-ended survey responses. Journal of Business Analytics 1(2), 93–116 (2018). doi: 10.1080/2573234X.2019.1590131.
ten Kleij, F., Musters, P.A.: Text analysis of open-ended survey responses: A complementary method to preference mapping. Food Quality and Preference 14(1), 43–52 (2003). doi: 10.1016/S0950-3293(02)00011-3.
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of Machine Learning Research 3, 993–1022 (2003).
Blei, D., Mcauliffe, J.: Supervised Topic Models. Neural Information Processing Systems, (2007).
Mimno, D., Wallach, H.M., Talley, E., Leenders, M., McCallum, A.: Optimizing Semantic Coherence in Topic Models. Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing. p. 262–272. Association for Computational Linguistics, Edinburgh, UK (2011).
Lau, J.H., Newman, D., Baldwin, T.: Machine Reading Tea Leaves: Automatically Evaluating Topic Coherence and Topic Model Quality. Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics. p. 530–539. Association for Computational Linguistics, Gothenburg, Sweden (2014).
Zuo, Y., Zhao, J., Xu, K.: Word Network Topic Model: A Simple but General Solution for Short and Imbalanced Texts. arXiv arXiv:1412.5404v1, (2014, December 17). doi: 10.48550/arXiv.1412.5404.
Schwabe, D.: Proceedings of the 22nd International Conference on World Wide Web. Association for Computing Machinery, New York, NY (2013).
Cao, J., Xia, T., Li, J., Zhang, Y., Tang, S.: A density-based method for adaptive LDA model selection. Neurocomputing 72(7-9), 1775–1781 (2009). doi: 10.1016/j.neucom.2008.06.011.
Tornatzky, L.G.: Processes of technological innovation. Lexington Books, Lexington, MA (1990).
Fleming, O., Fountaine, T., Henke, N., Saleh, T.: Ten red flags signaling your analytics program will fail. (2018, May 14). https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/ten-red-flags-signaling-your-analytics-program-will-fail, last accessed 5 September 2021.
Allas, T., Bughin, J., Chui, M., Dahlström, P., Hazan, E., Henke, N., et al.: Artificial intelligence is getting ready for business, but are businesses ready for AI? In: McKinsey & Company, (ed.). Crossing the frontier: How to apply AI for impact. p. 7–22. McKinsey Analytics, (2018). https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Analytics/Our%20Insights/Crossing%20the%20frontier%20How%20to%20apply%20AI%20for%20impact/Crossing-the-frontier-collection.ashx, last accessed 25 October 2022.
Cloudera: Limitless: The positive power of AI. (2021). https://www.cloudera.com/content/dam/www/marketing/resources/whitepapers/cloudera-global-ai-report.pdf?daqp=true, last accessed 15 July 2022.
McKinsey: The state of AI in 2021. (2021). https://www.McKinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Analytics/Our%20Insights/Global%20survey%20The%20state%20of%20AI%20in%202021/Global-survey-The-state-of-AI-in-2021.pdf, last accessed
Loucks, J., Davenport, T., Schatsky, D.: State of AI in the enterprise. Deloitte (2018). https://www2.deloitte.com/content/dam/Deloitte/de/Documents/technology-media-telecommunications/DELO-6418_State%20of%20AI%202020_KS4.pdf, last accessed 5 September 2021.
McKinsey: AI adoption advances, but foundational barriers remain. (2018, November 13). https://www.mckinsey.com/featured-insights/artificial-intelligence/ai-adoption-advances-but-foundational-barriers-remain#, last accessed 5 September 2021.
Cam, A., Chui, M., Hall, B.: Global AI Survey: AI proves its worth, but few scale impact. McKinsey (2019, November 22). https://www.mckinsey.com/featured-insights/artificial-intelligence/global-ai-survey-ai-proves-its-worth-but-few-scale-impact#, last accessed 5 September 2021.
Lacovou, C.L., Benbasat, I., Dexter, A.S.: Electronic Data Interchange and Small Organizations: Adoption and Impact of Technology. MIS Quarterly 19, 465–485 (1995).
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, 1661–1684 (2005).
Chui, M., Manyika, J., Miremadi, M., Henke, N., Chung, R., Nel, P., et al.: Notes from the AI frontier insights from hundreds of use cases. Discussion paper. McKinsey Global Institute (2018, April). https://www.mckinsey.com/~/media/mckinsey/featured%20insights/artificial%20intelligence/notes%20from%20the%20ai%20frontier%20applications%20and%20value%20of%20deep%20learning/notes-from-the-ai-frontier-insights-from-hundreds-of-use-cases-discussion-paper, last accessed 5 September 2021.
Porter, M.: Strategy and the Internet. Harvard Business Review 79, 63-78 (2001).
Hart, P.J., Saunders, C.S.: Emerging Electronic Partnerships: Antecedents and Dimensions of EDI Use from the Supplier’s Perspective. Journal of Management Information Systems 14(4), 87–111 (1998). doi: 10.1080/07421222.1998.11518187.
Ransbotham, S., Khodabandeh, S., Fehling, R., Lafountain, B., Kiron, D.: Winning with AI — Pioneers combine strategy, organizational behavior, and technology. MITSloan (2019, October 15). https://sloanreview.mit.edu/projects/winning-with-ai/, last accessed 5 September 2021.
