Yeni Gözde Yapay Zekâ: Yapay Zekânın İş Dünyasına Etkileri

Yıl 2019, Cilt: 6 Sayı: 2, 238 - 258, 26.10.2019

İzzet Kılınç , Aslıhan Ünal

Öz

Yapay zekâ son dönemlerde
akademik ve sosyal çevrelerde popüler bir konudur ve etkileri de iş dünyasında
yankılanmaktadır. Yapay zekâ teknolojisi üstel bir hızla gelişmektedir ve bu
durum faydaları ve zorlukları beraberinde getirmektedir. “Yapay zekâ” terimi iş
dünyasını heyecanlandırmaktadır, çünkü hayati öneme sahip bir rekabet aracıdır,
‘yeni göze’ yapay zekâdır. Bunun yanı sıra, iş dünyası yapay zekâ konusunda
tedbirlidir, çünkü yapay zekâ yeni zorlukları da ortaya çıkarmaktadır. Bu
araştırmanın amacı, iş dünyasında yapay zekâ ediniminin genel durumunu
incelemektir. Bu amaçla, önde gelen araştırma kuruluşları tarafından yayınlanan
14 araştırma raporu örneklem olarak belirlenmiştir ve keşfedici bir desen
izlenerek veriye nitel içerik analizi uygulanmıştır. Sonuç olarak; dört ana
kategori ortaya çıkmıştır: 1) Mevcut durum: Yapay zekânın mevcut durumu, 2)
‘Gelecek zaman’ yapay zekânın gelecek etkileri, 3)’Zorluklar’ dönüşüm sürecinde
iş dünyasında karşılaşılan engeller ve endişeler, 4) ‘Yapılması gerekenler’
işletmelerin yapması gereken eylemler. Bu araştırmanın bulguları, dünya
genelinde yürütülen araştırma raporlarını esas alarak genel bir bakış açısı
sunmaktadır ve işletmelerin mevcut durumunu, yapay zekânın potansiyel
etkilerini, iş dünyasında karşılaşılan engelleri, endişeleri ve bir ‘yapılması
gerekenler’ listesi içermektedir. 

Anahtar Kelimeler

yapay zeka, teknolojik değişim, nitel içerik analizi

Kaynakça

• Abbas, N. B. (2006). Thinking Machines: Discourses of Artificial Intelligence. LIT Verlag: Münster.
• Ahn, B. S., Cho, S. S. and Kim, C. Y. (2000). The integrated methodology of rough set theory and artificial neural network for business failure prediction. Expert systems with applications, 18(2), 65-74.
• Armat, M., Assarroudi, A., Rad, M., Sharifi, H. and Heydari, A. (2018). Inductive and Deductive: Ambiguous Labels in Qualitative Content Analysis. The Qualitative Report,23(1), 219-221. http://nsuworks.nova.edu/tqr/vol23/iss1/16
• Bataller, C. and Harris, J. (2016). Turning Artificial Intelligence into Business Value. Today. Accenture,https://pdfs.semanticscholar.org/a710/a8d529bce6bdf75ba589f42721777bf54d3b.pdf Bosch, K. and Bronkhorst, A. (2018). Human-AI Cooperation to Benefit Military Decision Making. https://www.researchgate.net/publication/325718292_Human-AI_Cooperation_to_Benefit_Military_Decision_Making
• Brynjolfsson, E. and McAfee, A. (2014). The Second Machine Age. İstanbul: Optimist Yayın Grubu.
• Čapek, K. (2013). R.U.R. Rossum'un Evrensel Robotları. (Çev.: Patricia Öztürk) Ankara: Elips Kitap.
• Dabholkar, P. A. and Bagozzi, R. P. (2002). An attitudinal model of technology-based selfservice: moderating effects of consumer traits and situational factors. Journal of The Academy of Marketing Science, 30(3), 184-201.
• Deloitte Human Capital Trends (2018). The rise of the social enterprise (Deloitte Global Human Capital Trends Research Report). Deloitte Insight. https://www2.deloitte.com/content/dam/insights/us/articles/HCTrends2018/2018- HCtrends_Rise-of-the-social-enterprise.pdf
• Dreyfus, H.L. (1972). What Computers Can't Do: A Critique of Artificial Reason. USA: Harper & Row, Publishers, Inc.
• Erlingsson C and Brysiewicz P. (2017). A hands-on guide to doing content analysis. African Journal Emergency Medicine, 7(3), 93-99. http://dx.doi.org/10.1016/j.afjem.2017.08.001.
• Fletcher, D. and Goss, E. (1993). Forecasting with neural networks: An application using bankruptcy data, Information & Management, 24(3), 159-167.
• Geraci, R. M. (2007). Robots and the sacred in science and science fiction. Zygon, 42(4), 961-980.
• Given, L.M. (2008). The SAGE Encyclopedia of Qualitative research methods, Volumes 1&2, (Ed. Lisa M. Given), USA:SAGE.
• Halper, F. (2017). Advanced Analytics: Moving Toward AI, Machine Learning, and Natural Language Processing (Best practices report, Q3). TDWI. https://www.sas.com/content/dam/SAS/en_us/doc/whitepaper2/tdwi-advanced-analytics-aiml-nlp-109090.pdf
• Hsieh, H. F. and Shannon, S. E. (2005). Three approaches to qualitative content analysis. Qualitative Health Research, 15(9), 1277-1288. https://www.researchgate.net/profile/Sarah_Shannon/publication/7561647_Three_Approaches_to_Qualitative_Content_Analysis/links/0fcfd50804371472d8000000/Three-Approaches-to-Qualitative-Content-Analysis.pdf Huang, M. H. and Rust, R. T. (2018). Artificial intelligence in service. Journal of ServiceResearch, 21(2), 155-172.
• Huang, Z., Chen, H., Hsu, C. J., Chen, W. H. and Wu, S. (2004). Credit rating analysis with support vector machines and neural networks: a market comparative study. Decision support systems, 37(4), 543-558.
• Infosys (2017). Amplifying Human Potential: Towards Purposeful Artifıcial Intelligence-A Perspective for CIO’s (Infosys Research Report). https://www.infosys.com/aimaturity/Documents/amplifying-human-potential-CIO-report.pdf
• Infosys (2018). Leadership in the Age of AI (Infosys Research Report). Infosys. https://www.infosys.com/age-of-ai/Documents/age-of-ai-infosys-research-report.pdf
• Jennings, N.R, Faratin, Johnson, M.J., O’Brien, P. and Wiegand, M.E. (1996). Using intelligent agents to manage business processes. Using intelligent agents to manage business processes. In Intelligent Agents and Their Applications, IEE Colloquium on (Digest No: 1996/101) (pp. 5-1). IET. https://eprints.soton.ac.uk/252150/1/PAAM96.pdf
• Kaastra, I. and Boyd, M. (1996). Designing a neural network for forecasting financial and economic time series”, Neurocomputing, 10(3), 215-236.
• Kim, K. J. and Han, I. (2000). Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index. Expert systems with Applications, 19(2), 125-132.
• Kolbjørnsrud, V., Thomas, R. J. and Amico, R. (2016). The Promise of Artificial İntelligence: Redefining Management in the Workforce of the Future (Accenture Institute for High Performance and Accenture Strategy Research Report May, 19). Accenture. https://www.accenture.com/_acnmedia/PDF-19/AI_in_Management_Report.pdf
• Li, E. Y. (1994). Artificial neural networks and their business applications. Information & Management, 27(5), 303-313.
• Li, S., Duan, Y., Kinman, R. and Edwards, J. S. (1999). A framework for a hybrid intelligent system in support of marketing strategy development. Marketing Intelligence & Planning, 17(2), 70-79.
• Li, S., Davies, B., Edwards, J., Kinman, R. and Duan, Y. (2002). Integrating group Delphi, fuzzy logic and expert systems for marketing strategy development: the hybridisation and its effectiveness. Marketing Intelligence & Planning, 20(5), 273-284. Liebowitz, J. (2006). Strategic intelligence: Business intelligence, competitive intelligence, and knowledge management. : Taylor & Francis, Inc.
• Lincoln, Y.S. and Guba, E.G. (2013). The Constructivist Credo. USA: Left Coast Press, Inc.Lincoln, Y.S. and Guba, E.G. (1988). Criteria for assessing naturalistic inquiries as reports. Paper presented at the Annual Meeting of the American Educational Research Association (New Orleans, LA, April 5-9, 1988).
• McCorduck, P. (1977). History of Artificial Intelligence, IJCAI, August, 951-954.
• McCorduck, P. (2004). Machines who think. Massachusetts: A K Peters, Ltd.
• McKinsey Global Institute (2017). Artıfıcıal ıntellıgence the next dıgıtal frontıer? (Discussion Paper). McKinsey&Company. https://www.mckinsey.com/~/media/McKinsey/Industries/Advanced%20Electronics/Our%20Insights/How%20artificial%20intelligence%20can%20deliver%20real%20value%20to%20companies/MGI-Artificial-Intelligence-Discussion-paper.ashx
• Martínez-López, F. J. and Casillas, J. (2013). Artificial intelligence-based systems applied in industrial marketing: An historical overview, current and future insights. Industrial Marketing Management, 42(4), 489-495.
• Mayring, P. (2014). Qualitative Content Analysis: Theoretical Foundation, Basic Procedures and Software Solution. Klagenfurt. https://nbn-resolving.org/urn:nbn:de:0168-ssoar-395173
• McCarthy, J. Minsky, M. L. and Shannon, C. E. (2006). A proposal for the Dartmouth Summer Research Project on artificial intelligence-August 31, 1955. AI Magazine, 27(4), 12-14.
• McCorduck, P. (1977). History of Artificial Intelligence, IJCAI, August: 951-954.
• Metaxiotis , K., Ergazakis, K., Samouilidis, E. and Psarras, J. (2003). Decision support through knowledge management: the role of the artificial intelligence. Information Management & Computer Security, 11(5),. 216 – 221, http://dx.doi.org/10.1108/09685220310500126
• Nilsson, N. J. (2010). The Quest for Artifıcial Intelligence a History of Ideas and Achievements. UK: Cambridge University Press.
• PWC (2018). The Anxious Optimist in the Corner Office (ceosurvey.pwc Research Report). PWC. https://www.pwc.com/gx/en/ceo-survey/2018/pwc-ceo-survey-report-2018.pdf
• PWC (2017). 20 years inside the mind of the CEO ...What's next? (ceosurvey.pwc Research report, March). PricewaterhouseCoopers LLP. https://www.pwc.com/gx/en/ceosurvey/2017/industries/20th-ceo-survey-pharma.pdf.
• Ransbotham, S., Kiron, D., Gerbert, P. and Reeves, M. (2018). Reshaping Business With Artificial Intelligence Closing the Gap Between Ambition and Action (MIT Sloan Management Review and The Boston Consulting Group Research Report, Fall). MIT. https://www.bcg.com/Images/Reshaping%20Business%20with%20Artificial%20Intelligence_tcm9-177882.pdf Reitman, W. (1986). Artificial intelligence applications for business: Getting acquainted. In Artificial Intelligence Applications for Business (Third printing) (Ed: Walter Reitman) USA: Ablex Publishing Coıperation.
• Rhines, W. (1985). Artificial intelligence: out of the lab and into business. The Journal of Business Strategy, 6(1), 50-57.
• Russell, S. and Norvig, P. (1995). Artificial Intelligence: A modern approach. Englewood Cliffs, New Jersey: Prentice-Hall.
• SAS (2017). The Enterprise AI Promise: Path to Value (SAS Research Report). https://www.sas.com/content/dam/SAS/el_gr/doc/research1/ai-survey-2017.pdf
• Say, C. (2018). 50 Soruda Yapay Zekâ (7.Baskı). İstanbul: 7 Renk Basım Yayın ve Filmcilik Ltd. Şti.Shanks, R., Sinha, S. and Thomas, R.J. (2015). Managers and machines, unite! (Accenture Institute for High Performance and Accenture Strategy Research Report). Accenture Strategy. https://www.accenture.com/_acnmedia/PDF-19/Accenture-Strategy-Manager-Machine-Unite-V2.pdf
• Shanks, R., Sinha, S. and Thomas, R.J. (2016). Judgment calls: Preparing leaders to thrive in the age of intelligent machines (Accenture Institute for High Performance Research Report). Accenture. https://www.accenture.com/t20170411T174032Z__w__/us-en/_acnmedia/PDF-19/Accenture-Strategy-Workforce-Judgment-Calls-V2.pdf
• Sharma, A. and Chopra, A. (2013). Artificial neural networks: Applications in management, IOSR Journal of Business and Management, 12(3), 32-40.
• Shook, E. and Knickrehm, M. (2018). Reworking the Revolution (Accenture Strategy Research Report). Accenture Strategy. https://www.accenture.com/t20180613T062119Z__w__/us-en/_acnmedia/PDF-69/Accenture-Reworking-the-Revolution-Jan-2018-POV.pdf#zoom=50
• Schrempf, O. C., Hanebeck, U. D., Schmid, A. J. and Worn, H. (2005, August). A novel approach to proactive human-robot cooperation. In Robot and Human Interactive Communication, 2005. ROMAN 2005. IEEE International Workshop on (pp. 555-560). IEEE.
• Silver, D. and Hassabis, D. (2016). Mastering the ancient game of Go, https://research.googleblog.com/2016/01/alphago-mastering-ancient-game-of-go.html
• Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., van den Driessche, G.,Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T. and Hassabis, D. (2015). Mastering the game of Go with deep neural networks and tree search. Nature 529, 484–489.
• Thomas, R.J., Fuchs R. and Silverstone, Y. (2016). A machine in the C-suite (AIHP and Acenture Strategy Research Report), Accenture. https://www.accenture.com/t00010101T000000Z__w__/br-pt/_acnmedia/PDF-13/Accenture-Strategy-WotF-Machine-CSuite.pdf
• Turing, A. M. (1950). Computing Machinery and Intelligence. Mind, 49, 433-460.
• Von Krogh, G. (2018). Artifıcial intelligence in organizations: new opportunities for phenomenon-based theorizing. Academy of Management Discoveries, 4(4), 404–409, https://doi.org/10.5465/amd.2018.0084
• Yadav, M. S. and Pavlou, P. A. (2014). Marketing in computer-mediated environments: Research synthesis and new directions. Journal of Marketing, 78(1), 20-40.
• Zambak, A. F. (2014). Artificial intelligence as a new metaphysical project. In R. Hagengruber & U. Riss (Eds.), Philosophy, Computing and Information Science (pp. 67-74). Pickering & Chatto.