HR-AI helps solving wicked management problems

Companies are reinventing the performance management in their organizations (Bersin 2018). The HR-AI helps achieving this aim.


Traditional business management makes decisions with simplified iteration and using mental shortcuts called cognitive biases. Cognitive biases are assumptions of how the world works. Humans substitute complex issues with biases. Human performance management is too difficult to make sense of as there are just too many ifs involved: if a key person leaves, if strategy implementation fails, if customer satisfaction drops, if employee performance declines, if absence increases, etc. Therefore, cognitive biases drive leadership behaviors. However, what happens if these cognitive biases are wrong and/or harmful?

New management game theory and artificial intelligence (AI) algorithms make it possible to predict leadership behavior’s effect on business. The architecture consists of a human capital production function, motivation theories, and several evidence-based rules. For AI, management decision-making is a prediction problem, and solving it is possible through the use of an augmented reality simulation game. The simulation game predicts the future outcome according to management behaviors. Managers will learn to make better decisions from the simulation. Artificial intelligence (AI) will help to optimize human resource management decisions.

Artificial intelligence plays several rounds of simulations in milliseconds, and remembers the most valuable management practices for long-term success. AI also suggests actions to manage the decision-making process. A manager uses human judgment, because some of the the AI-suggested actions may not be reasonable in a real-life situation. Humans are good at estimating which actions are best for a specific situation, but humans are poor at predictions. Humans have several cognitive biases, which are based on wrong assumptions, and that harm long-term success. While AI can see into the future and can predict the long-term result, it does not take into consideration all situational realities. Thus, the best results are achieved when AI and human beings work in collaboration.

Human resources management AI is an intelligent prediction machine. Its prediction accuracy can be increased for each specific organization. AI has the ability to learn, and this learning is not limited by harmful biases. Prediction accuracy improves with more up-to-date data, listening to employee feedback continuously, and comparing the simulation prediction to the real-life realization.


Figure 1. HR-AI architecture

One problem is the cognitive illusion that management competence is in order, and performance problems are due to other reasons (plenty of ifs). Supervisors’ leadership practice skills may be very poor and, therefore, there may be a tendency to neglect necessary leadership activities. The team-leader may justify omitting performing HR-practices, because it seems to be more important to use precious work time to maximize profits than to invest time into soft, human leadership practices. However, this is a wrong assumption. Management problems are serious, because behavioral cognitive biases are difficult to overcome and require practice-based learning to substitute these biases with better behavior. AI-based simulation learning may solve this problem.



Bersin, J. (2018). HR technology disruptions for 2018: Productivity, design, and intelligence reign. New York, NY: Deloitte. Retrieved from /rs/976-LMP-699/images/ HRTechDisruptions2018-Report-100517.pdf

Agrawal, A. (2018). The economics of artificial intelligence. Commentary McKinsey, April 2018.

Kahneman, D. (2012). Thinking, fast and slow. Location: Penguin Books.

Kesti, M. (2018).  Architecture of Management Game for Reinforced Deep Learning, Intelligent Systems Conference 2018 6-7 September 2018 | London, UK. (conference paper, not yet published)

About markokesti

Marko Kesti Dr. (admin), M.Sc. (tech.) Adjunct Professor, HRM-Performance University of Lapland CEO, PlayGain Inc. EVP, Mcompetence Inc. Non-fiction writer Married with 2 kids

Posted on May 17, 2018, in Human Capital, Human Capital Performance, Human Resource Development, Human Resource Development, HRD, Quality of Working Life, Uncategorized and tagged , , , , , , , , . Bookmark the permalink. 1 Comment.

  1. Moi!

    Mietin termiä Prediction. Tulevaisuustutkimuksessa aina puhuttiin, että ennustaminen ei ole tulevaisuuteen katsomista. Ennakointi sen sijaan on. Englanninkielessä käytetään paljon termiä Foresight. Myös termi Foresee voisi tässä toimia tuoden asiaan aavistamista, ja ongelman ounastelua: Leader might not foresee problems of his behavior.

    Hyvä juttu ja kuvakulma johtamispäätöksen tekoon. Toinen esimerkki olettamisesta, jota aina kursseillani painotan, että jos jossain pitää olettaa, pitää heti herätä ja olla varovainen. Englanninkielen ASSUME samana kun jakaa osiin tulee siitä ASS U, ASS ME Eli aina kun oletat, teet itsestäsi ja toisesta aasin.

    Terv. Terhi

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