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Johtamisen tekoälyllä kilpailuetua

Seuraavassa avaan lyhyesti tekoälyn toimintaperiaatteita ja selitän, miksi johtamisen tekoäly luo merkittävän kilpailuedun.

Tekoälyn soveltaminen johtamisessa mahdollistaa viisaampien päätöksien tekemisen ja ennakoivan johtamisen. Kyse ei ole pelkästään ongelmien välttämisestä, vaan ennen kaikkea ihmisten voimavarojen paremmasta hyödyntämisestä työntekijöiden ja organisaation parhaaksi.

Tekoäly (artificial intelligence) voidaan jakaa kahteen yläluokkaan ja kolmeen osa-alueeseen. Tekoälyn kaksi yläluokkaa ovat data- ja malliohjattu tekoäly. Dataohjatussa tekoälyssä käytetään algoritmeja datan syy-seurausvaikutusten tulkintaan sekä ennustamiseen. Malliohjatussa tekoälyssä tavoitteena on kontekstin syvällisen ymmärryksen (mallin) avulla muuttaa käyttäytymistä, jotta voidaan saavuttaa parempi tulevaisuus. Malliohjattu tekoäly perustuu vahvistetun oppimisen algoritmeihin (reinforcement learning). Dataohjatussa tekoälyssä käytetään ei-ohjattua (unsupervised) ja ohjattua oppimista (supervised learning).

Tekoalyn_periaate

Ei-ohjattu oppiminen (unsupervised learning) hyödyntää luokittelematonta dataa. Algoritmien avulla voidaan tunnistaa datassa olevia asioita ja indikaatioita, jotka liittyvät johonkin ilmiöön. Datasta voidaan selvittää, mitkä tapahtumat liittyvät todennäköisesti esimerkiksi sairauspoissaolojen kasvuun, työkyvyttömyyteen tai ei-toivottuun irtisanoutumiseen. Sen avulla saadaan suosituksia; esimerkiksi urakehityksessä tiettyyn koulutukseen hakeutuneille suositellaan jatkokursseja, jotka on koettu hyödyksi. On selvää, että organisaatiokontekstin ymmärrys on tärkeää, sillä ei-ohjattu oppiminen ei itsessään ymmärrä asiayhteyksiä ja syy-seuraussuhteiden kausaalisuutta.

Ohjattu oppiminen (supervised learning) on datan hyödyntämistä tapauksissa, joissa tieto on luokiteltua ja lopputulos tiedetään. Analysoinnissa käytetään regressiotarkasteluja, jotka ovat monelle tuttuja mm. Excelin kautta. Ohjattu oppiminen hakee datapisteitä noudattavan funktion, jonka avulla voidaan ennustaa tulevaa kehitystä. Esimerkiksi työelämän laadun (QWL) ja sairauspoissaolojen välillä on havaittu yhteys: työelämän laadun huonontuessa sairauspoissaolot lisääntyvät. Voidaan siis hakea funktio, joka mallintaa työelämän laadun yhteyttä sairauspoissaoloihin ja siten ennustaa sairauspoissaolojen kehitystä mittaamalla työelämän laatua.

Vahvistettu oppiminen (Reinforcement learning) opastaa käyttäytymään siten, että lopputulos on parempi. Se siis vahvistaa käyttäytymistä, jonka avulla saadaan optimaalinen lopputulos pitkällä aikavälillä. Vahvistettu oppiminen vaatii luotettavan mallin, joka simuloi organisaation toimintaa ja johtamisen vaikutusta siihen. Mallia voidaan ”pyörittää” eteenpäin, jolloin nähdään, miten johtamiskäyttäytyminen vaatii tiettyjä uhrauksia (aikaa ja kuluja), mutta järkevästi toteutettu johtaminen tuo tulosta myöhemmin ja tämä tulos maksaa moninkertaisesti takaisin (vrt. ROI eli return on investment). Vahvistetun oppisen tekoäly siis mallintaa kontekstin ROI vaihtoehtoja, vahvistaen sellaista käyttäytymistä (strategiaa), joka johtaa maksimaaliseen takaisinmaksuun eli tuottoon pitemmällä aikavälillä.

Kaikki tekoälyn osa-alueet voidaan valjastaa johtamisen avuksi esimerkiksi seuraavasti: ei-ohjattu tekoäly seuraa datavirtaa ja indikoi, että jossain ryhmässä on henkilöstön suorituskyky heikentynyt. Pulssityyppinen henkilöstökysely käynnistyy tällöin automaattisesti ja mittaa työntekijöiden kokeman työelämän laadun. Tiedot menevät tekoälyavusteiseen simulaatioon, jossa esimies voi tekoälyltä kysyä, mitä johtamisaktiviteetteja kannattaa toteuttaa, jotta saadaan paras takaisinmaksu ROI tuottona. Ennakoiva johtaminen parantaa työyhteisön työelämän laatua, jolloin tuottavuus paranee ja uhkaavat sairauspoissaolot vältetään.

Mitä tämä tarkoittaa yrityksen kilpailukyvyssä? Yksi tuottavuuden suurimpia haasteita on esimiestoiminnan huono laatu ja suuri hajonta. Vain erittäin harva organisaatio on kyennyt ratkaisemaan tämän ongelman. Yritys, joka käyttää tekoälyä johtamisen apuna, kykenee nostamaan esimiestoiminnan laatua merkittävästi. Työelämän laadun parantuminen tuotantotekijänä nostaa tehollista työaikaa kuormittamatta työntekijöitä. Yrityksen tehollisen työtunnin kustannus voi näin olla yli 20% alhaisempi kuin kilpailijalla, vaikka yritys maksaa henkilöstölleen parempaa palkkaa (vrt. Time-Driven-Activity-Based-Cost, BSC, Kaplan). Lisäksi tyytyväisemmät asiakkaat tuovat kilpailuetua, sillä henkilöstön kokema työelämän laatu parantaa asiakaskokemusta.

Rohkaisen johtajia käynnistämään tekoälyn “evoluution” omassa organisaatiossa. Dataa pitää kerätä ja ymmärtää aiempaa paremmin. Ihan ensimmäiseksi tekoälyn hyödyntäminen vaatii tiedolla-johtamisen tason nostoa ja seuraavaksi rohkeutta lähteä kokeilemaan ja oppimaan.  

Lisätietoa marko.kesti(at)ulapland.fi

Game theory approach to human capital management

Game theory provides mental models to integrate behavioral capital with finance and HR data. In this blog post, I will illustrate what management game theory is and why it is so powerful in connection with artificial intelligence (AI).

Behavioral capital is becoming increasingly important in creating business value. These “soft skills” are difficult to acquire because human social context is complex due to different personalities and biases. However, there is an emerging new science that will solve this problem and foster organizational performance. Game theory sheds light on management behavior and helps illuminate the relationship between the actions of management and the performance of subordinates. It helps illustrate why some organizations fail at change management or face high staff turnover. Game theory is science that applies mathematics to better understand human decision-making and social behavior. Game theory is key in creating new generation model-driven artificial intelligence to reinforce managers’ behavior and create sustainable competitive advantages.

Every leader, manager or supervisor is playing a game that includes the following game theory principles:

  1. Strategic game
  2. Bayesian game
  3. Stochastic game
  4. Non-symmetric game
  5. Signaling game
  6. Non-co-operative or co-operative game
  7. Zero-sum or general sum game

Strategic game: A leader’s behavior today affects an organization’s profits after twelve months. This phenomenon of long-term effects makes leadership strategic. Every leader has a certain management mindset or policy that he or she follows, either consciously or subconsciously. There are also human biases that dictate leadership behavior. In addition, there are personal assumptions about the rewards of leadership behavior. Some leaders are able to predict future rewards while others think only about fast rewards or avoiding possible punishment, which may be strategically unwise.

Bayesian game: The management game is Bayesian, meaning we have to make decisions with imperfect information. Managers’ have prior assumptions about their leadership behavior’s effects. With experience the prior assumptions may change as learning from doing gives better understanding about the context and behavior’s causalities. This is called reinforcement learning that rational persons naturally have, and it is also included at Bayesian game theory. Leaders operate at organization environment that is complex and may sometimes be hard to comprehend. However, leaders know certain probability distributions upon which they can base their decisions. Rational leaders utilize the brain’s natural phenomenon of reinforcement learning despite the imperfect information from complex environment.

Stochastic game: The management world is stochastic, which usually leads to negative surprises. One cannot expect that each day’s activities will be fulfilled as planned. Often there are stochastic interventions which require our attention. Also, humans are heavily affected by current moment bias in which short-term reward (or avoiding immediate punishment) is valued more than long-term reward (which would require different actions and more strategic thinking). The stochastic world is evolving and manager behavior will have a great effect on the outcome.

Non-symmetric game: Leaders, managers and supervisors are all in non-symmetric positions compared to their subordinates. The leadership power of managers is controlled by management systems. Thus, managers usually have different strategies than their subordinates. While worker focus on doing their tasks, managers have to think about whole team collaboration and performance. In addition, there are myriad personal and social features that form the way leaders use their non-symmetric power to influence subordinates.

Signaling game: The behavior of the leader can modify a team’s culture and culture dictates the signaling game. When there is common trust that problems are solved in a positive way, there will be more signals about possible problems and development needs. In addition to staff comments and feelings, there are also signals from management systems. For example, a digital leaderboard can signal increased sickness risk and recommend activating early intervention for preventing absences. In this case, the digital system analyzes data (i.e. staff inquiries and other data) and sends out alarms and offers advice for action.

Non-cooperative or cooperative game: In the famous prisoner’s dilemma there are two prisoners who can’t communicate but are forced to choose to either cooperate with each other or act in their own best interest. In the context of an organization, communication is not restricted, but the same type of social decision dilemma is present in every organization and team: Do the employees choose to cooperate, or do they act on their personal best interest? A non-cooperative mindset reduces productivity and may cause severe performance problems. Using game theory, it is possible to foster a more cooperative mindset in which employees innovate and solve problems in a positive atmosphere.

Zero-sum or general sum game: In zero-sum game, a gain for one player results in a loss for the other player. A zero-sum game mindset is harmful for organizations because it prevents cooperation. In the general sum game, the players help each other to achieve rewards and higher performance levels. General sum players are also willing to take risks together. They are focused on winning and are ready to make sacrifices to secure long-term rewards. Both, the zero-sum and general sum game leave marks at organization data, thus it is possible to analyze which type of game the organization plays. Data-driven AI helps identify exiting culture and model-driven AI helps with teaching which behaviors lead to the general sum game.

Why is game theory combined with machine learning an incredible breakthrough? Because we can model complex human behavior in an organizational context using mathematical modeling. First, we have to make digital presentations for organizations that model the effect of management decisions on fiscal and human performance, then we must implement Markov sequences at this digital twin and start running reinforcement Q-learning with the Bellman function. This may sound complicated (and it actually is), but when the digital twin world is built, it explains why human capital management within organizations is so difficult. AI can provide advice for managers in their decision-making, forming sort of a crystal ball that reveals future alternatives and reinforce behavior for winning.

There are significant benefits in utilizing game theory, data-analytics and machine learning in an organizational setting. I believe there is going to be an emerging new management science in this field. Game theory provides mental models to integrate behavioral capital with finance and HR data. This is not an easy task, but this revolutionary research has already begun. At this stage, it is essential to have close research collaboration with companies where data is created every day and performance problems are difficult to solve. It seems that Kurt Lewin’s saying, “Nothing is more practical than good theory,” applies here as well.

Marko Kesti
M.Sc., Dr., Adjunct Professor (human capital productivity)
Research Director, University of Lapland
markokesti.wordpress.com
https://www.linkedin.com/in/markokesti/

HRM connection to Business Performance – Opening the black-box of HRM-P

HRM-Performance theory at learning video

In the following example I will illustrate the principle of human capital production function. Example analyze is from the case company of 400 employees (in Full-Time-Equivalent).  Each employee has the theoretical working time, which in this case is 2 000 hours per year. From this theoretical working time the auxiliary working time takes roughly 15%. Auxiliary working time (AX) includes vacation, absence, maternity leave, work orientation, staff training, HR-practices and HR-development.

When auxiliary working time is reduced from theoretical working time we get the time for work. During the time spend at work the intangible human assets have to be considered. Quality of Working Life (QWL) describes the utilizing degree of intangible human capital. If Quality of Working Life is at the level of 60%, then the effective working time share from the time for work is 60 % as well. Therefore the calculated effective working time is 85% times 0.6. Thus, 51% from theoretical working time is calculated to be effective.

Other working time can now be calculated and it is 85% – 51% = 34%. This other working time includes work planning, quality assurance, reporting, wasted working time due to errors, and fuss because poor motivation and engagement, and handling customer complaints.

With the effective working time the company makes the revenue. There is company specific coefficient (K), which determines how much revenue the company can make with one effective working hour. This example company makes 100 million dollars revenue. According the annual profit and loss calculation there are variable costs of 50 million dollars which is 50% from the revenue. Variable costs are materials and purchase services which are needed in making the revenue. Staff costs are 25 million dollars, and other fixed costs, like rents and licenses, are 15 million dollars. Finally, when all costs are reduced there will be operating profit, in this case 10 million dollars.

HRM_P_theory_Initial_Figure

Figure. Human capital production function with example company initial data.

Management wants to improve company’s human capital productivity, and therefore invest some time to staff training and HR-practices. For simplicity, in this case we assume that this training and HR-practices time is already included at earlier year auxiliary working time, so the time for work remains the same.

When training and HR-practices are done properly, there will be positive effect at the Quality of Working Life. In practice we have recorded at case studies that 5 % improvement at QWL is possible and realistic objective at one year. In this case it means that the Quality of Working Life improves from 60 to 63%. Because QWL-index determines the Effective Working Time share, there will be equal 5% improvement at the Effective Working Time.

In growing business environment the company can utilize the K-factor in making more revenue. When revenue increases the variable costs are increased as well. Therefore $5M more revenue means in this case $2.5M dollars more variable costs. HR-development creates so called virtual talent capacity increase, which means that Human Resource capacity will increase without fixed costs raise. Improving intangible human assets effectively will enable more production capacity without fixed cost increase. Therefore only variable costs increase are reduced from Revenue and the rest $2.5M will improve the operating profit. As a conclusion, this company makes 6 250 dollars more profit for each employee.

HRM_P_theory_productivity_improvement

Figure. Improving company productivity by HR-development.

Human capital production function mathematical equation goes like this:

Revenue = K x HR x TWh x (1-AX) x QWL
where
K = K-coefficient (Human Resource Business Ratio, $/h)
HR = human resource in full time equivalent (FTE)
TWh = theoretical yearly working time (h)
AX = auxiliary working time, this is the same as the time for work (%)
QWL = quality of working life index (%)

The operating profit, in financial terms Earnings before Interest, Taxes, Depreciation and Amortization, is revenue reduced by all costs.

The Human Resource Business Coefficient, shortly the K-factor, can be calculated from earlier year realization with following equation:

HRBR_equation

When case company data are inserted at the equation, there will be coefficient of 245.1 dollars per hour. This means that one effective working hour produce 245.1 dollars revenue. Company management can influence on K-factor by certain strategic innovations and my investing at product development and IC-technology. There is saying that management determines the profitability and leadership determines the productivity. At the Human Capital Production Function this means that management determines the K-factor and leadership the Quality of Working Life. Usually when K-factor is improved for example by strategic innovation there will be some reduce at the QWL, causing decline at organization productivity.

Determining the QWL-index is somewhat more complex than just measuring average results from the staff inquiry. This is due to the fact that those measured inquiry items have different scale and effect on performance. I will explain the validated scientific method for determining the QWL index later in coming blog post.

Marko Kesti
Dr. (admin.), M.Sc. (tech.)
Adjunct Professor, University of Lapland
+358 40 717 8006
marko.kesti@ulapland.fi

CEO Playgain (playgain.eu)
Advisor Vibecatch (vibecatch.com)
EVP Mcompetence (mcompetence.fi)
Non-fiction writer (https://markokesti.wordpress.com/)

References

Kesti, M., Leinonen, J. and Syväjärvi, A. (2016). A Multidisciplinary Critical Approach to Measure and Analyze Human Capital Productivity. In Russ, M. (ed.). Quantitative Multidisciplinary Approaches in Human Capital and Asset Management (pp 1-317). Hershey, PA: IGI Global. (1-22). doi: 10.4018/978-1-4666-9652-5.

Kesti, M. and Syväjärvi, A. (2015) Human Capital Production Function in Strategic Management. Technology and Investment, 6, 12-21. doi: 10.4236/ti.2015.61002.

Kesti M. (2013). Human Capital Production Function, GSTF Journal on Business Review, Volume 3, Number 1, pages 22-32.