Implementing Artificial Intelligence in Organizations and the Special Role of Trust

  • Johannes BruhnEmail author
  • Matthias Anderer


Companies increasingly consider adapting artificial intelligence and deep learning technologies, yet very little implementation knowledge is accessible from which managers in charge of AI implementation projects can draw. The authors of this chapter present an approach consisting of four straightforward paradigms of AI implementation, shedding light on essential technical, organizational and—most importantly—human factors. A special emphasis is put on the critical issue of employee trust in AI. At the end of the chapter, an array of measures is presented to help build the right amount of trust in AI systems early in the implementation process.


  1. Accenture UK (2018) Creating smarter artificial intelligence by eliminating bias. Retrieved from
  2. Amazon Robotics LLC (2018) Amazon robotics—vision. Retrieved from
  3. Bessen JE (2016) How computer automation affects occupations: technology, jobs, and skills (Law & economics working paper no. 15-49). Boston University School of LawGoogle Scholar
  4. Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. Retrieved from
  5. Devine WD Jr (1983) From shafts to wires: historical perspective on electrification. J Econ Hist 43(2):347–372CrossRefGoogle Scholar
  6. Dietvorst BJ, Simmons JP, Massey C (2014) Algorithm aversion: people erroneously avoid algorithms after seeing them err. J Exp Psychol Gen 144(1):114–126CrossRefGoogle Scholar
  7. Dihal K, Dillon S, Singler B (2018) From homer to HAL—3,000 years of AI narratives. University of Cambridge Research Horizons Magazine, vol 35, pp 28–29. Retrieved from
  8. Dodge S, Karam L (2017) A study and comparison of human and deep learning recognition performance under visual distortions. Retrieved from
  9. Frankenhaeuser M, Gardell B (2010) Underload and overload in working life: outline of a multidisciplinary approach. J Hum Stress 2(3):35–46CrossRefGoogle Scholar
  10. Grace K, Salvatier J, Dafoe A, Zhang B, Evans O (2017) When will AI exceed human performance? Evidence from AI experts. Retrieved from
  11. Johansson G (1989) Job demands and stress reactions in repetitive and uneventful monotony at work. Int J Health Serv 19(2):365–377CrossRefGoogle Scholar
  12. Lipton Z (2016) The mythos of model interpretability. Retrieved from
  13. Liu Y, Gadepalli K, Norouzi M, Dahl GE, Kohlberger T, Boyko A, Venugopalan S, Timofeev A, Nelson PQ, Corrado GS, Hipp JD, Peng L, Stumpe MC (2017) Detecting cancer metastases on gigapixel pathology images. Retrieved from
  14. Madhavan P, Wiegmann DA (2004) A new look at the dynamics of human-automation trust: is trust in humans comparable to trust in machines? In: Proceedings of the human factors and ergonomics society 48th annual meeting, Santa MonicaGoogle Scholar
  15. Nikkei (2017) Nvidia to help Komatsu automate construction machinery. Retrieved from
  16. Ross C, Swetlitz I (2017) IBM pitched its Watson supercomputer as a revolution in cancer care. It’s nowhere close. Retrieved from
  17. Stewart J (2018) Tesla’s autopilot was involved in another deadly car crash. Retrieved from
  18. Surowieki J (2017) Robocalypse not. Retrieved from
  19. Tesla Inc (2018) Full self-driving hardware on all cars. Retrieved from
  20. Thackray RI (1981) The stress of boredom and monotony: a consideration of the evidence. Psychosom Med 43(2). Retrieved from Scholar
  21. Thaler R, Sunstein C (2009) Nudge: improving decisions about health, wealth and happiness. Penguin, LondonGoogle Scholar
  22. Torre I, Goslin J, White L, Zanatto D (2018) Trust in artificial voices: a “congruency effect” of first impressions and behavioural experience. In: Proceedings of APAScience ’18: technology, mind, and society (TechMindSociety ’18), New YorkGoogle Scholar
  23. (2018) AI scheduled meetings for smarter work days. Retrieved from
  24. Xiong W, Wu L, Alleva F, Droppo J, Huang X, Stolcke A (2017) The Microsoft 2017 conversational speech recognition system. Retrieved from

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Hochschule FreseniusMunichGermany

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