DOI QR코드

DOI QR Code

Understanding the Artificial Intelligence Business Ecosystem for Digital Transformation: A Multi-actor Network Perspective

디지털 트랜스포메이션을 위한 인공지능 비즈니스 생태계 연구: 다행위자 네트워크 관점에서

  • Received : 2019.08.31
  • Accepted : 2019.11.20
  • Published : 2019.11.30

Abstract

With the advent of deep learning technology, which is represented by AlphaGo, artificial intelligence (A.I.) has quickly emerged as a key theme of digital transformation to secure competitive advantage for businesses. In order to understand the trends of A.I. based digital transformation, a clear comprehension of the A.I. business ecosystem should precede. Therefore, this study analyzed the A.I. business ecosystem from the multi-actor network perspective and identified the A.I. platform strategy type. Within internal three layers of A.I. business ecosystem (infrastructure & hardware, software & application, service & data layers), this study identified four types of A.I. platform strategy (Tech. vertical × Biz. horizontal, Tech. vertical × Biz. vertical, Tech. horizontal × Biz. horizontal, Tech. horizontal × Biz. vertical). Then, outside of A.I. platform, this study presented five actors (users, investors, policy makers, consortiums & innovators, CSOs/NGOs) and their roles to support sustainable A.I. business ecosystem in symbiosis with human. This study identified A.I. business ecosystem framework and platform strategy type. The roles of government and academia to create a sustainable A.I. business ecosystem were also suggested. These results will help to find proper strategy direction of A.I. business ecosystem and digital transformation.

알파고로 대변되는 딥러닝 기법의 등장으로 인공지능은 기업 경쟁우위 확보를 위한 디지털 트랜스포메이션의 핵심 주제로 급부상했다. 산업 내 인공지능 기반 디지털 트랜스포메이션 방향을 이해하기 위해서는 현재 진행 중인 인공지능 비즈니스 생태계 참여자들 유형 및 활동에 대한 명확한 이해가 선행되어야 한다. 따라서 본 연구는 다행위자 네트워크(Multi-actor network)관점에서 인공지능 비즈니스 생태계 내부와 외부 참여자들의 활동을 분석하고 플랫폼 전략 유형을 규명하였다. 인공지능 비즈니스 생태계 내부 세 개 계층(인프라스트럭처 & 하드웨어, 소프트웨어 & 애플리케이션, 서비스 & 데이터 계층)에서 사업자들은 네 가지 플랫폼 전략 유형으로(기술수직×비즈수평, 기술수직×비즈수직, 기술수평×비즈수평, 기술수평×비즈수직) 인공지능 비즈니스가 진행되고 있다. 인공지능 비즈니스 생태계 외부에는 다섯 행위자들이(사용자, 투자자, 정부 정책가, 학계 등 컨소시엄, 시민단체) 공존 및 지속가능한 인공지능 비즈니스 생태계를 지원하고 있다. 본 연구는 학술적으로 인공지능 비즈니스 생태계 분석 프레임워크 및 인공지능 플랫폼 전략 모델을 제시하였고, 실무적으로 플랫폼 관점의 인공지능 디지털 트랜스포메이션 전략 방향과 지속가능한 인공지능 비즈니스 생태계 조성을 위한 정부, 학계 등의 역할을 제시했다.

Keywords

Acknowledgement

이 논문은 2018학년도 충북대학교 학술연구지원사업의 연구비 지원에 의하여 연구되었음.

References

  1. 강영식, 이현우, 김병수, "프로세스 마이닝과 딥러닝을 활용한 구매 프로세스의 적기 입고예측에 관한 연구", Information Systems Review, 제20권, 제4호, 2018, pp. 25-41. https://doi.org/10.14329/isr.2018.20.4.025
  2. 김기태, 김종우, "클라우드 서비스 생태계 내의 협업 사례 연구: 클라우드 서비스 중개업을 중심으로", Information Systems Review, 제17권, 제1호, 2015, pp. 1-18. https://doi.org/10.14329/isr.2014.17.1.001
  3. 김정민, "인공지능 윤리 이슈와 교육 과정 동향", 월간SW중심사회, 소프트웨어정책연구소, 2019.
  4. 남충현, "오픈소스 AI: 인공지능 생태계와 오픈이노베이션", 정보통신정책연구원(KISDI), 2016.
  5. 신건호, 박규홍, 박용진, 안재현, "C2C 공유경제 서비스 참여자 간의 비대칭적 플랫폼 참여의도", Information Systems Review, 제19권, 제3호, 2017, pp. 47-67. https://doi.org/10.14329/isr.2017.19.3.047
  6. 이은광, "인공지능, 아직 통제․예측 어려워...국내외 산학연 정책 마련에 힘써", 2019, Available at http://www.dailybizon.com/news/articleView.html?idxno=12776.
  7. 전종홍, 이승윤, "개방적/인간친화적 인공지능 체계 기술 표준화 동향", TTA저널, 제169권, 2017, pp. 46-54.
  8. Acquisdata, "Artificial Intelligence Software", Global Industry Snapshot, GIS000175, ISSN 2208-4568, 2019.
  9. Adner, R., "Ecosystem as structure: an actionable construct for strategy", Journal of Management, Vol.43, No.1, 2017, pp. 39-58. https://doi.org/10.1177/0149206316678451
  10. Alberti, F. G. and E. Pizzurno, "Knowledge exchanges in innovation networks: Evidences from an Italian aerospace cluster", Competitiveness Review, Vol.25, No.3, 2015, pp. 258-287. https://doi.org/10.1108/CR-01-2015-0004
  11. Autio, E., M. Kenney, P. Mustar, D. Siegel, and M. Wright, "Entrepreneurial innovation: The importance of context", Research Policy, Vol.43, No.7, 2014, pp. 1097-1108. https://doi.org/10.1016/j.respol.2014.01.015
  12. Badger, E., "WiFi, hot tubs and big data: How Airbnb determines the price of a home", 2015, Available at https://www.washingtonpost.com/news/wonk/wp/2015/08/27/wifi-hot-tubs-and-bigdata-how-airbnb-determines-the-price-of-a-home/?noredi rect=on.
  13. Bostrom, N., "Strategic implications of openness in AI development", Global Policy, Vol.8, No.2, 2017, pp. 135-148. https://doi.org/10.1111/1758-5899.12403
  14. Brynjolfsson, E. and A. Mcafee, "The business of artificial intelligence", Harvard Business Review, 2017.
  15. Brynjolfsson, E., D. Rock, and C. Syverson, "Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics", In The economics of artificial intelligence: An agenda, National Bureau of Economic Research, 2018, pp. 23-57.
  16. Bulgaru, I., "10 Ways alexa is revolutionizing healthcare", healthcareweekly, 2019, Available at https://healthcareweekly.com/alexa-in-healthcare/.
  17. Cath, C., S. Wachter, B. Mittelstadt, M. Taddeo, and L. Floridi, "Artificial intelligence and the 'good society': The US, EU, and UK approach", Science and Engineering Ethics, Vol.24, No.2, 2018, pp. 505-528. https://doi.org/10.1007/s11948-017-9901-7
  18. Desai, A. M., Intel Create 2019 Event Reveals 'Master Plan' Involving Open-Source Software With High Performance Kernels For Ray Tracing, 2019, Available at https://appuals.com/intel-create-2019-event-reveals-master-plan-involving-open-source-software-with-high-performance-kernels-for-ray-tracing/.
  19. Edge, D., J. Larson, and C. White, "Bringing AI to BI: enabling visual analytics of unstructured data in a modern Business Intelligence platform", In Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems, 2018.
  20. Evans, D. S. and R. Schmalensee, "Matchmakers: The new economics of multisided platforms", Harvard Business Review Press, 2016
  21. Forrester, Industrial AI Development White Paper, Forrester Consulting, 2018.
  22. Glorot, X., A. Bordes, and Y. Bengio, "Domain adaptation for large-scale sentiment classification: A deep learning approach," Proceedings of the 28th international conference on machine learning (ICML-11), 2011, pp. 513-520.
  23. Graca, P. and L. M. Camarinha-Matos, "Performance indicators for collaborative business ecosystems-Literature review and trends," Technological Forecasting and Social Change, Vol.116, 2017, pp. 237-255. https://doi.org/10.1016/j.techfore.2016.10.012
  24. Hemant, T. and M. Kevin, "The end of scale", MIT Sloan Management Review, Vol.59, No.3, 2018.
  25. Helbing, D., "Societal, economic, ethical and legal challenges of the digital revolution: From big data to deep learning, artificial intelligence, and manipulative technologies", Towards Digital Enlightenment, Springer, Cham, 2019, pp. 47-72.
  26. Isckia, T., M. De Reuver, and D. Lescop, "Digital innovation in platform-based ecosystems: an evolutionary framework", Proceedings of the 10th International Conference on Management of Digital EcoSystems, ACM, 2018, pp. 149-156).
  27. Jaeger, H., "Artificial intelligence: Deep neural reasoning", Nature, Vol. 538, 2016, pp. 467-478. https://doi.org/10.1038/nature19477
  28. Jhonsa, E., Nvidia and Intel's Mobileye Both Continue Racking Up Autonomous Driving Deals, 2019, Available at https://www.thestreet.com/technology/nvidia-and-intel-s-mobileye-both-continue-racking-up-autonomous-driving-deals-14995698.
  29. Karcz, A., Google Home vs. Amazon Echo: Which Smart Speaker Is Better?, Forbes, 2019, Available at https://www.forbes.com/sites/anthonykarcz/2019/08/26/google-home-vs-amazon-echo-which-smart-speaker-is-better/#7078839c5591.
  30. Kim, J., "The platform business model and business ecosystem: Quality management and revenue structures", European Planning Studies, Vol.24, No.12, 2016, pp. 2113-2132. https://doi.org/10.1080/09654313.2016.1251882
  31. Kolbjornsrud, V., R. Amico, and R. J. Thomas, "How artificial intelligence will redefine management", Harvard Business Review, 2016.
  32. Korosec, K., SoftBank's Next Bet: $940M Into Autonomous Delivery Startup Nuro, 2019, Available at https://techcrunch.com/2019/02/11/softbanks-next-bet-940m-into -autonomous-delivery-startup-nuro/.
  33. LeCun, Y., Y. Bengio, and G. Hinton, "Deep learning," Nature, Vol.521, 2015, pp. 436-444. https://doi.org/10.1038/nature14539
  34. Lee, J. U., K. J. Seo, and H. W. Kim, "An exploratory study on the cloud computing services: issues and suggestion for the success", Asia Pacific Journal of Information Systems, Vol.24, No.4, pp. 473-491.
  35. Li, W., W. J. Wu, H. M. Wang, X. Q. Cheng, H. J. Chen, Z. H. Zhou, and R. Ding, "Crowd intelligence in AI 2.0 era", Frontiers of Information Technology & Electronic Engineering, Vol.18, No.1, 2017, pp. 15-43. https://doi.org/10.1631/FITEE.1601859
  36. Lindgren, P., P. Valter, and R. Prasad, "Advanced business model innovation supported by artificial intelligence, deep learning, multi business model patterns and a multi business model library," Wireless Personal Communications, 2019, pp. 1-15.
  37. Liu, D. Y., S. W. Chen, and T. C. Chou, "Resource fit in digital transformation: Lessons learned from the CBC Bank global e-banking project", Management Decision, Vol.49, No.10, 2011, pp. 1728-1742. https://doi.org/10.1108/00251741111183852
  38. Makridakis, S., "The forthcoming artificial intelligence (AI) revolution: Its impact on society and firms", Futures, Vol.90, 2017, pp. 46-60. https://doi.org/10.1016/j.futures.2017.03.006
  39. Metelskaia, I., O. Ignatyeva, S. Denef, and T. Samsononwa, "A business model template for AI solutions," Proceedings of the International Conference on Intelligent Science and Technology (ICIST), 2018, pp. 35-41.
  40. Nambisan, S., M. Wright, and M. Feldman, "The digital transformation of innovation and entrepreneurship: Progress, challenges and key themes", Research Policy, Vol.48, No.8, 2019.
  41. Ojasalo, J. and H. Kauppinen, "Collaborative innovation with external actors: an empirical study on open innovation platforms in smart cities", Technology Innovation Management Review, Vol.6, No.12, 2016.
  42. Osborne, C., IBM Brings Artificial Intelligence to the Heart of Cybersecurity Strategies, ZDNet, 2018, Available at https://www.zdnet.com/article/why-artificial-intelligence-is-at-the-core-of-ibm-cybersecurity-strategies/.
  43. Pappas, I. O., P. Mikalef, M. N. Giannakos, J. Krogstie, and G. Lekakos, "Big data and business analytics ecosystems: Paving the way towards digital transformation and sustainable societies", Information Systems and e-Business Management, Vol.16, 2018, pp. 479-491. https://doi.org/10.1007/s10257-018-0377-z
  44. Parmar, A., Butterfly Network Wants to do to Ultrasound what Digital Cameras did to Kodak film, 2019, Available at https://medcitynews.com/2019/01/butterfly-network-wants-to-do-to-ultrasound-what-digital-cameras-did-to-kodak-film/.
  45. Quan, X. I. and J. Sanderson, "Understanding the artificial intelligence business ecosystem", IEEE Engineering Management Review, Vol.46, No.4, 2018, pp. 22-25. https://doi.org/10.1109/EMR.2018.2882430
  46. Ransbotham, S., D. Kiron, P. Gerbert, and M. Reeves, "Reshaping business with artificial intelligence: Closing the gap between ambition and action", MIT Sloan Management Review, Vol.59, No.1, 2017
  47. Roberts, A., "Global IBM watson services market status by current trend and future plan 2019-2028", Westminster News Online, 2019, Available at https://westminsternewsonline.com/31893/global-ibm-watson-services-market-status-by-current-trend-and-future-plan-2019-2028.
  48. Rong, K., Y. Lin, B. Li, T. Burstrom, L. Butel, and J. Yu, "Business ecosystem research agenda: more dynamic, more embedded, and more inter-nationalized", Asian Business & Management, Vol.17, No.3, 2018, pp. 167-182. https://doi.org/10.1057/s41291-018-0038-6
  49. Russell, M. G. and N. V. Smorodinskaya, "Leveraging complexity for ecosystemic innovation", Technological Forecasting and Social Change, Vol.136, 2018, pp. 114-131. https://doi.org/10.1016/j.techfore.2017.11.024
  50. Schmidhuber, J., "Deep learning in neural networks: An overview", Neural Networks, Vol.61, 2015, pp. 85-117. https://doi.org/10.1016/j.neunet.2014.09.003
  51. Simon, J. P., "Artificial intelligence: Scope, players, markets and geography", Digital Policy, Regulation and Governance, Vol.22, No.3, 2019, pp. 208-237. https://doi.org/10.1108/DPRG-08-2018-0039
  52. Takaoka, K., K. Yamazaki, E. Sakurai, K. Yamashita, and Y. Motomura, "Development of an integrated AI platform and an ecosystem for daily life, business and social problems", In International Conference on Applied Human Factors and Ergonomics, 2018, pp. 300-309.
  53. Taneja, H. and K. Maney, "The end of scale", MIT Sloan Management Review, p. 59, 2018
  54. Tokui, S., K. Oono, S. Hido, and J. Clayton, "Chainer: A next-generation open source framework for deep learning", Proceedings of workshop on machine learning systems (LearningSys) in the twenty-ninth annual conference on neural information processing systems (NIPS), Vol.5, 2015, pp. 1-6.
  55. Tsujimoto, M., Y. Kajikawa, J. Tomita, and Y. Matsumoto, "A review of the ecosystem concept-Towards coherent ecosystem design", Technological Forecasting and Social Change, Vol.136, 2018, pp. 49-58. https://doi.org/10.1016/j.techfore.2017.06.032
  56. Westerman, G., C. Calmejane, D. Bonnet, P. Ferraris, and A. McAfee, "Digital transformation: A roadmap for billion-dollar organizations", MIT Center for Digital Business and Capgemini Consulting, Vol.1, 2011, pp. 1-68.
  57. Wiggers, K., Facebook founds AI Language Resea rch Consortium to Solve Challenges in Natural Language Processing, 2019, Available at https://venturebeat.com/2019/08/28/facebook-founds-ailanguage-research-consortium-tosolve-challenges-in-natural-language-processing/.
  58. Winnig, L., "GE's big bet on data and analytics", MIT Sloan Management Review, Vol.57, No.3, 2016.
  59. Zellinger, W., B. A. Moser, T. Grubinger, E. Lughofer, T. Natschlager, and S. Saminger-Platz, "Robust unsupervised domain adaptation for neural networks via moment alignment", Information Sciences, 2019.