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Mullistavat HR-teknologiat

Josh Bersinin raportissa on hyvää asiaa tulevista mullistavista HR-teknologioista. Näkemykseni on yhtenevä ja perustelen muutamia keskeisiä blogissani.

Yritysten henkilöstöjohtamisessa korostuu jatkossa tiimien suorituskyvyn kehittäminen. Tiimien suorituskyvyn ongelmat on tiedostettu jo usean vuoden ajan. Suurin syy on esimiestoiminnan huono laatu ja laadun suuri hajonta. Karkeasti ottaen 20 % esimiehistä taitaa esimiestyön, 60 % omaa merkittävää kehittämispotentiaalia ja lopulla 20 % on esimiestyössä vakavia ongelmia. Sama hajonta on tiimien suorituskyvyssä. Nyt haetaan siis uusia ratkaisuja tiimien suorituskyvyn parantamiseen.

“Companies want management tools that help enable and empower teams, drive team-centric engagement and performance, and support agile, networkfocused HR practices.” toteaa Bersin.

Olen tutkinut esimiestoiminnan laadun hajontaa ja selvittänyt sen juurisyytä. Yksi oleellinen syy on esimiesten huono osaaminen ihmisten johtamisessa. Vain harva esimies osaa hyviä ihmisten johtamisen käytäntöjä. Ne esimiehet, jotka eivät osaa esimieskäytäntöjä, eivät niitä myöskään toteuta käytännössä. Esimiesten johtamistaidoissa on samankaltainen hajonta kuin tiimien suorituskyvyssä. Tiimien suorituskykyä olen mitannut työyhteisön kokemalla Työelämän Laadun indeksillä (QWL, Quality of Working Life), jossa suorituskyky määräytyy motivaatioteorian mukaan.

Voidaanko esimiesten osaamisongelma ratkaista kouluttamalla esimiehille hyviä ihmisten johtamisen käytäntöjä? Ei voida, koska ongelma on pirullinen (wicked). Pirullisen siitä tekee se, että ne esimiehet, jotka tarvitsisivat ihmisten johtamistaitoja, eivät koe niitä tarvitsevansa. Heiltä puuttuu oppimiseen vaadittava motivaatio. Vaikka he käyvät koulutuksissa, johtamiskäyttäytyminen ei muutu. Heidän mielestä ihmisten johtamiskäytännöt syövät arvokasta työaikaa, joka kannattaa mieluummin käyttää tuloksen tekemiseen. Heillä on vahva käsitys (bias), että heidän toteuttama tulosjohtamisen malli on hyvä, ja syyt tiimin työhyvinvoinnin ja suorituskyvyn ongelmiin ovat heidän mielestä muualla – tekijöissä, joihin he eivät voi vaikuttaa.

Ongelma voidaan ratkaista uusilla HR-teknologioilla, jotka mahdollistavat seuraavat asiat:

  • tiimin työelämän laatu tehdään näkyväksi jatkuvatoimisella mittauksella (continuous QWL measurement)
  • tekoälyavusteinen simulaatio opettaa kokemuksellisesti paremman johtamismallin (redefining leadership mind-set)

Suorituskyvyn jatkuva parantaminen on monessa organisaatiossa tavoitteena. Jatkuva parantaminen on tehotonta, mikäli ongelmiin reagoidaan liian myöhään. Tehokas jatkuva parantaminen vaatii jatkuvaa henkilöstön näkemysten huomioimista, jolloin ongelmia voidaan ratkaista nopeasti ja ennakoivasti. Tuloshyödyt ovat niin merkittäviä, että jatkuva QWL-mittaus tulee yleistymään nopeasti. Aluksi se voisi tarkoittaa kuukausittain toteutettavaa henkilöstökyselyä. Kysymyksiä on vain muutamia ja niillä mitataan henkilöstön kokemuksia työelämän laadun tekijöistä.

When companies start implementing continuous performance management, they often realize that feedback and engagement survey systems should be connected to the process. … To do this effectively, organizations need a set of tools that facilitate continuous listening, which goes well beyond annual surveys.” toteaa Bersin

Työelämän laatu on aineeton tuotantotekijä. Se on tuotantotekijänä vähintään yhtä tärkeä kuin henkilöstömäärä. Työelämän laatu linkittyy asiakastyytyväisyyteen, innovatiivisuuteen sekä yrityksen talouteen henkilöstövoimavarojen tuotantofunktion avulla. Jatkuvatoiminen tiimin työelämän laadun mittaus nostaa esimiestoiminnan laadun ja tuottavuuden ongelmat esille. Samalla se automaattisesti pelillistää esimiestoimintaa, sillä rationaalinen esimies haluaa kokeilla, miten oma vuorovaikutusjohtaminen vaikuttaa tiimin kokemaan työelämän laatuun. Esimiehen huomio siirtyy tuloksesta ihmisten johtamiseen ja tuloksen parantuminen tulee pienellä viiveellä, kuten simulaatio opettaa.

“Let me add another hot trend that most people don’t understand yet. I am now convinced that virtual and augmented reality (VR and AR) are going to be big in the learning and performance support market.” toteaa Bersin

Olemme kehittäneet tekoälyavusteisen simulaatiopelin, joka opettaa esimiehille ihmisten johtamista ja sen vaikutuksia talouteen ja työelämän laatuun. Simulaatiomaailma (augmented reality) on luotu johtamisen peliteorian ja henkilöstötuottavuuden analytiikan avulla. Simulaatioon on kytketty tekoäly (artificial intelligence, AI). Se analysoi erilaisia vaihtoehtoja esimiehen puolesta ja ehdottaa sitten parhaita, joilla saadaan kestävää kilpailuetua. Ihminen tarvitsee apua nimenomaan pitkän aikavälin vaikutusten ymmärtämiseen. Ilman tekoälyn apua ihminen on taipuvainen tekemään hätiköityjä päätöksiä omien oletusten mukaan. Organisaation suorituskyvyn johtamisessa nämä oletuksiin perustuvat käyttäytymismallit (biases) ovat usein huonoja pitemmällä aikajänteellä. Suorituskyvyn parantaminen onnistuu parhaiten, kun ihmisellä on apuna tekoälyn kyky nähdä pitemmälle tulevaisuuteen.

Lähteet

Bersin J. (2018). HR Technology Disruptions for 2018: Productivity, Design, and Intelligence Reign. Deloitte. http://marketing.bersin.com/rs/976-LMP-699/images/HRTechDisruptions2018-Report-100517.pdf

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)

Kesti M. (2018). Henkilöstötuottavuuden tutkimusohjelma. Tiedolla johtamisen hanke.

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.

Management_AI_architecture_dark

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.

 

REFERENCES

Bersin, J. (2018). HR technology disruptions for 2018: Productivity, design, and intelligence reign. New York, NY: Deloitte. Retrieved from http://marketing.bersin.com /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)

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.