Staffing the Sphere of Artificial Intelligence with Higher-Educated Personnel
https://doi.org/10.15826/umpa.2022.04.028
Abstract
The development in the sphere of artificial intelligence and the introduction of its technologies in the sectors of the Russian economy is a priority task. Within knowledge-based economy, the resource to provide this development is highly qualified personnel. Our article examines the sources of supplying the staff demand in the sphere of artificial intelligence. The main sources are as follows: higher-educated graduates of the corresponding educational programs, self-educated and/ or professionally re-trained workers with higher education. The methodological basis of the study is the balance method, applied to poll and statistical data. It is found out that the demand for personnel with higher education in the sphere of artificial intelligence is going to be supplied by university graduates only at the level of 35 % in the nearest future, which is below the average for the Russian economy. The total contribution of all the sources considered will provide only 70 % of the demanded higher-educated staff. A qualitative analysis of meeting the demand made it possible to identify deficient groups of educational specialties, as well as to form a list of leading universities in training personnel with necessary competencies. This study is the first attempt to quantitatively and qualitatively analyze the sources of covering the staff demands in the Russian sphere of artificial intelligence. The work practically specifies the volume of necessary training, determines the provision with staff according to different groups of specialties / areas of training, and identifies training centers for such personnel. When making system management decisions on adjusting admission quotas, when developing educational programs and professional standards in the sphere of artificial intelligence, this article might be of use for directors and employees of relevant departments, as well as for representatives of the corresponding scientific and educational communities.
About the Authors
A. O. AveryanovRussian Federation
Alexander O. Averyanov – Postgraduate Student, Leading Specialist, Department of Forecasting, Center of Budget Monitoring
185910, Petrozavodsk, Lenin ave., 33
I. S. Stepus
Russian Federation
Irina S. Stepus – PhD (Economics), Chief of the Department of Forecasting, Center of Budget Monitoring
185910, Petrozavodsk, Lenin ave., 33
V. A. Gurtov
Russian Federation
Valery A. Gurtov – Dr. hab. (Physics and Mathematics), Director of Center of Budget Monitoring
185910, Petrozavodsk, Lenin ave., 33
References
1. Indeks gotovnosti prioritetnykh otraslei ekonomiki Rossiiskoi Federatsii k vnedreniyu iskusstvennogo intellekta [Index of Readiness of Priority Sectors of the Economy of the Russian Federation for the Introduction of Artificial Intelligence], Moscow, Analytical Center for the Government of the Russian Federation, Lomonosov Moscow State University, 2021, 159 p. (In Russ.).
2. Fedotov A. V., Belyakov S. A., Klyachko T. L., Polushkina E. A. Kadrovoe obespechenie prioritetnykh napravlenii sotsial’no-ekonomicheskogo razvitiya: sostoyanie i problemy [Staffing the Priority Directions of the Socio-Economic Development: Situation and Problems]. Universitetskoe upravlenie: praktika i analiz [University Management: Practice and Analysis], 2017, nr 21(3), pp. 27–37. doi:10.15826/umpa.2017.03.035. (In Russ.).
3. Gurtov V. A., Pitukhin E. A. Prognozirovanie potrebnostei ekonomiki v kvalifitsirovannykh kadrakh: obzor podkhodov i praktik primeneniya [Prognostication of the Demands of Economics in Qualified Personnel: Review of Approaches and Application Experience]. Universitetskoe upravlenie: praktika i analiz [University Management: Practice and Analysis], 2017, vol. 21, nr 4 (110), pp. 130–161. doi:10.15826/umpa.2017.04.056. (In Russ.).
4. Blinova T. N., Fedotov A. V., Kovalenko A. A. Sootvetstvie struktury podgotovki kadrov s vysshim obrazovaniem potrebnostyam ekonomiki: problemy i resheniya [The Structure of Personnel Training within Getting Higher Education Meets the Needs of Economy: Problems and Solutions]. Universitetskoe upravlenie: praktika i analiz [University Management: Practice and Analysis], 2021, vol. 25, nr 2, pp. 13–33. doi:10.15826/umpa.2021.02.012. (In Russ.).
5. Vinichenko V. A. Disproportsii sprosa i predlozheniya v sisteme vosproizvodstva kadrov dlya transportnoi otrasli [Disproportions of Supply And Demand in the Personnel Reproduction System for the Transport Industry]. Universitetskoe upravlenie: praktika i analiz [University Management: Practice and Analysis], 2022, vol. 26, nr 3, pp. 83–99. doi:10.15826/umpa.2022.03.023. (In Russ.).
6. Sigova S. V., Stepus I. S. Kadrovoe obespechenie prioritetov razvitiya Arkticheskoi zony Rossii – vklad sistemy vysshego obrazovaniya [Recruitment Needs for the Russian Arctic Zone Priorities Development – Higher Education System Value]. Universitetskoe upravlenie: praktika i analiz [University Management: Practice and Analysis], 2015, nr 5 (99), pp. 19–29. (In Russ.).
7. Budzinskaya O. V., Martynov V. G., Sheinbaum V. S. Kadrovoe obespechenie toplivno-energeticheskogo kompleksa kak ob»ekt proektirovaniya [Human Resources Supply in Oil and Gas Companies as an Object of Designing]. Upravlenie ustoichivym razvitiem [Sustainability Management], 2020, nr 5 (30), pp. 76–84. (In Russ.).
8. Popolitova S. V., Ushmodina L. I., Karplyuk Yu. A. Klasternyi podkhod pri obespechenii potrebnosti v kadrakh rossiiskikh predpriyatii oboronno-promyshlennogo kompleksa s uchetom situatsii na regional’nykh rynkakh truda [Using the Cluster Approach and the Labour Market for Personnel Source Selection to Cover Russian Defense Industry Requirements]. Vestnik MGTU «Stankin» [Vestnik MSTU “Stankin”], 2017, nr 1 (40), pp. 122–126. (In Russ.).
9. IT-kadry dlya tsifrovoi ekonomiki v Rossii [IT Personnel for the Digital Economy in Russia], Moscow, Assotsiatsiya predpriyatii komp’yuternykh i informatsionnykh tekhnologii, 2020, 19 p. (In Russ.).
10. Amirov R. A., Egorov E. V. Tsifrovaya ekonomika i aktual’nye zadachi ee kadrovogo obespecheniya v Rossii [Digital Economy and Actual Tasks of Its Staffing in Russia]. Upravlencheskoe konsul’tirovanie [Management Consulting], 2018, nr 9 (117), pp. 42–50. doi:10.22394/1726–1139–2018–9–42–50. (In Russ.).
11. Okun’kova E. A. Strategicheskii forsaiting kadrovykh potrebnostei innovatsionnogo razvitiya sotsial’noekonomicheskikh system [Strategic Foresighting of Personnel Requirements of Innovative Development of Social and Economic Systems]. Upravlenie [Management], 2019, nr 1, pp. 114–120. doi:10.26425/2309–3633–2019–1–114–120. (In Russ.).
12. Gaynanov D. A., Klimenteva A. Yu. Prioritety kadrovogo obespecheniya tsifrovoi ekonomiki [The Priorities of Staffing the Digital Economy]. Kreativnaya ekonomika [Creative Economy], 2018, vol. 12, nr 12, pp. 1963–1976. (In Russ.).
13. Trofimova I. N. Podgotovka kadrov dlya tsifrovoi ekonomiki: tekushchie problemy i tselevye orientiry [Human Resource Training for Digital Economy: Current Problems and Targets]. Sotsiodinamika [Sociodynamics], 2020, nr 10, pp. 1–10. (In Russ.).
14. Aver’yanov A. O., Stepus’ I. S., Gurtov V. A. Prognoz kadrovoi potrebnosti dlya sfery iskusstvennogo intellekta v Rossii [Human Resource Needs Forecast for Artificial Intelligence in Russia]. Problemy prognozirovaniya [Problems of Forecasting], 2023, nr 1 (196), pp. 113–133. (In Russ.).
15. Istochniki novykh industrii. Iskusstvennyi intellekt v promyshlennosti: ekspertno-analiticheskii doklad [Sources of New Industries. Artificial Intelligence in Industry], Saint Petersburg, 2022, 44 p. (In Russ.).
16. Academic Offer of Advanced Digital Skills in 2019–20. International Comparison: Focus on Artificial Intelligence, High Performance Computing, Cybersecurity and Data Science, Luxembourg, 2020, 76 p. doi:10.2760/225355. (In Eng.).
17. Artificial Intelligence Index Report 2022. Stanford University Human-Centered Artificial Intelligence, 2022, 229 p. (In Eng.).
18. Zweben S. and Bizot B. Growth Continues but New Student Enrollment Shows Declines. Survey Bachelor’s and Doctoral Degree Production, Taulbee, 2020, 67 p. (In Eng.).
19. Klyukin B. N. Kushlin V. I., Yakovets Yu. V. Balansovye metody i makromodelirovanie v dolgosrochnom prognozirovanii [Balance Methods and Macromodeling in Long-Range Forecasting]. In: Prognozirovanie, strategicheskoe planirovanie i natsional’noe programmirovanie [Forecasting, Strategic Planning and National Programming], Moscow, 2011, pp. 151–188. (In Russ.).
20. Ryabko T. V., Gurtov V. A., Stepus’ I. S. Analiz pokazatelei podgotovki kadrov dlya sfery iskusstvennogo intellekta po rezul’tatam monitoringa vuzov [Analysis of Artificial Intelligence Training Indicators according to the Results of Russian Universities Monitoring]. Vysshee obrazovanie v Rossii [Higher Education in Russia], 2022, vol. 31, nr 7, pp. 9–24. doi:10.31992/0869–3617–2022–31–7–9–24. (In Russ.).
Review
For citations:
Averyanov A.O., Stepus I.S., Gurtov V.A. Staffing the Sphere of Artificial Intelligence with Higher-Educated Personnel. University Management: Practice and Analysis. 2022;26(4):22-36. (In Russ.) https://doi.org/10.15826/umpa.2022.04.028