Artificial intelligence adoption in public organizations: a case study


  • Luis Guedes FIA Business School, São Paulo, (Brasil)
  • Moacir Oliveira Júnior Faculdade de Economia, Administração, Contabilidade e Atuária da Universidade de São Paulo - FEA/USP, São Paulo, (Brasil)



Artificial Intelligence, Public sector, Innovation, Technology adoption


Purpose: The study explores the key factors influencing AI adoption by public organizations, and sought to understand the dynamics of AI adoption, aiming to identify the potential challenges of integrating AI with ESG considerations.

Originality/value: This research addresses the gap in understanding AI adoption in the public sector at the firm level, emphasizing the challenges and risks of technology integration. The study discuss how AI can be used effectively, contributing to societal appropriation of technological progress.

Methods: Methodology employs a multi-stage analysis of literature, followed by ten interviews and a case study on Brazil's Federal Revenue Service. Empirical data was probed through rigorous coding and thematic analysis, selecting the most impactful factors influencing AI adoption.

Results: The conclusions highlight the role of AI in elevating public services performance and reach. However, the deployment of AI calls for vigilant oversight to mitigate adverse effects and inequalities and demands a multidisciplinary strategy addressing an interplay of challenges.

Conclusion: The study provides a framework for effective AI adoption, offering insights for decision-makers on strategizing AI adoption, emphasizing the importance of factoring ESG concerns into de decision to adopt this technology.


Download data is not yet available.

Author Biographies

Luis Guedes, FIA Business School, São Paulo, (Brasil)

Pós-doutorando em Inteligência Artificial e Doutorado em Administração pelo Programa de Pós-Graduação em Administração da Faculdade de Economia, Administração, Contabilidade e Atuária da Universidade de São Paulo - FEA/USP, São Paulo, (Brasil). Professor da FIA Business School, São Paulo.

Moacir Oliveira Júnior, Faculdade de Economia, Administração, Contabilidade e Atuária da Universidade de São Paulo - FEA/USP, São Paulo, (Brasil)

Doutor em Administração pela Universidade de São Paulo - USP, São Paulo, (Brasil). Professor Titular do Departamento de Administração da Faculdade de Economia, Administração, Contabilidade e Atuária da Universidade de São Paulo - FEA/USP, São Paulo. Coordenador do Escritório de Desenvolvimento de Parcerias da Universidade de São Paulo - USP. Coordenador Científico do Programa de Gestão da Inovação e Tecnologia da Universidade de São Paulo - USP/PGT. Pesquisador Principal e Coordenador de Inovação do ARIES CEPID (Centro de Pesquisa, Inovação e Difusão em Pesquisas Antimicrobianas), financiado pela FAPESP.


Acemoglu, D., and Restrepo, P. (2018). Artificial intelligence, automation, and work. In The economics of artificial intelligence: An agenda (pp. 197-236). University of Chicago Press.

Ahmad, T., Zhang, D., Huang, C., Zhang, H., Dai, N., Song, Y., & Chen, H. (2021). Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities. Journal of Cleaner Production, 289, 125834.

Agarwal, P.K. (2018). Public administration challenges in the world of AI & bots. Public Administration Review, 78(6), 917-921.

Agrawal, A., J. Gans & A. Goldfarb (2018) Prediction Machines: The Simple Economics of Artificial Intelligence. Boston (MA): Harvard Review Pres.

Ai Hleg (High-Level Expert Group on Artificial Intelligence). 2019. Ethics Guidelines for Trustworthy Artificial Intelligence. European Commission. Available at Retrieved on 12-May-2023

Alamoodi, A. H., Zaidan, B. B., Zaidan, A. A., Albahri, O. S., Mohammed, K. I., Malik, R. Q., ... & Alaa, M. (2021). Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: A systematic review. Expert systems with applications, 167, 114155.

Alsheibani, S.; Messom, C.; Cheung, Y.; and Alhosni, M. (2020). Reimagining the Strategic Management of Artificial Intelligence: Five Recommendations for Business leaders. AMCIS 2020 Proceedings.

Arksey, H., O'Malley, L. (2005). Scoping Studies: Towards a Methodological Framework. International Journal of Social Research Methodology: Theory & Practice, 8(1), 19–32.

Arundel, A., Bloch, C., and Ferguson, B. (2019). Advancing innovation in the public sector: Aligning innovation measurement with policy goals. Research policy, 48(3), 789-798.

Barnett-Page, E., and Thomas, J. (2009). Methods for the synthesis of qualitative research: a critical review. BMC Medical Research Methodology, 9(1), 1-11.

BCB. Banco Central do Brasil (2022). Fiscal statistics. Available at Retrieved on 03-Feb-2023

Bengio, Y. et al (2023). Pause Giant AI Experiments: An Open Letter. Future of Life Institute. Available at Retrieved on 23-Feb-2023

Blackwell, A. F. (2020). Objective functions:(In) humanity and inequity in artificial intelligence. Science in the ForeSt, Science in the PaSt, 191. ISBN: 978-1-912808-42-7

Boon, W., Edler, J. (2018). Demand, challenges and innovation. Making sense of new trends in innovation policy. Sci. Public Policy 45(4), 1–13.

Botelho Jr, J., Costa, S. C., Ribeiro, J. G., & Souza Jr, C. M. (2022). Mapping roads in the Brazilian Amazon with artificial intelligence and Sentinel-2. Remote Sensing, 14(15), 3625.

Brown, N., Brown, I. (2019). From digital business strategy to digital transformation – how: A systematic literature review. SAICSIT ‘19: Proceedings of the South African Institute of Computer Scientists & Information Technologists

Erik, B., and Andrew, M. (2017). The Business of Artificial Intelligence: What It Can—and Cannot—Do for Your Organization. Harvard Business Review Digital Articles, 7, 3-11.

Braun, V., and Clarke, V. (2012). Thematic analysis. American Psychological Association.

Bughin, J., Hazan E., Ramaswamy, S., Chui, M., T., Henke, N., Trench, M. (2017). Artificial intelligence: The next digital frontier? McKinsey Global Inst.

Bughin, J., M. Chui, and J. Manyika (2015). An Executive's Guide to the Internet of Things. McKinsey Quarterly 9(2), p. 89-105.

Cai, J., Chu, X., Xu, K., Li, H., & Wei, J. (2020). Machine learning-driven new material discovery. Nanoscale Advances, 2(8), 3115-3130.

Campion, A., Hernandez, M. G., Jankin, S. M., and Esteve, M. (2020). Managing artificial intelligence deployment in the public sector. Computer, 53(10), 28-37.

Canada. Ethics Guidelines for Trustworthy AI (2023). Available at: Retrieved in 13-Apr-2023.

Cavoukian, A. (2012). Privacy by design. In George O.M. Yee (Ed.). Privacy protection measures and technologies in business organizations: Aspects and standards (pp. 170–208). IGI Global.

Chalmers, D., MacKenzie, N. G., and Carter, S. (2021). Artificial intelligence and entrepreneurship: Implications for venture creation in the fourth industrial revolution. Entrepreneurship Theory and Practice, 45(5), 1028-1053.

Chui, M., Manyika, J., Miremadi, M., Henke, N., Chung, R., Nel, P. and Malhotra, S. (2018). Notes from the AI frontier insights from hundreds of use cases, McKinsey Global Institute, 2.

Chui, M. (2017). Artificial intelligence the next digital frontier? Company Global Institute, 47, 3–6.

Criado, J. I., and Gil-Garcia, J. R. (2019). Creating public value through smart technologies and strategies: From digital services to artificial intelligence and beyond. International Journal of Public Sector Management, 32(5), 438-450.

Cunha, C. R., & Santos, J. C. F. (2021). Tributação, IA e Aprendizado de Máquina: o caso do Sistema de Seleção Aduaneira por Aprendizado de Máquina–SISAM sob a perspectiva da transparência administrativa. Editora Fundação Fênix, 19.

Davenport, T. H., Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116

De Vries H., Bekkers, V.J.J.M. and Tummers, L. (2016) Innovation in the public sector: A systematic review and future research agenda. Public Administration 94(1): 146–166.

Desouza, K.C. (2018), Delivering Artificial Intelligence in Government: Challenges and Opportunities. IBM Center for the Business of Government, 48

Dobrescu, E. M., and Dobrescu, E. M. (2018). Artificial Intelligence (AI)—The Technology That Shapes the World. Global Economic Observer, 6(2), 71–81.

Eggers, W.D., Schatsky, D. and Viechnicki, P. (2017), AI-Augmented Government: Using Cognitive Technologies to Redesign Public Sector Work. New York (NY): Deloitte University Press.

Eisenhardt, K. M., and Graebner, M. E. (2007). Theory building from cases: Opportunities and challenges. Academy of Management Journal, 50(1), 25–32.

Emre C., Trott, P. and Simms, C. (2019). A systematic review of barriers to public sector innovation process. Public Management Review, 21(2), 264–290.

Enholm, I.M., Papagiannidis, E., Mikalef, P. et al. (2022). Artificial Intelligence and Business Value: a Literature Review. Inf Syst Front 24, 1709–1734.

EPA (2023). United states environmental protection agency greenhouse gas equivalencies calculator. Available at Retrieved in in 20-Jan-2023.

European Union (2022). Ethics Guidelines for Trustworthy AI. Available at Retrieved in 19-Mar-2022

Exmeyer, P. C., Hall, J. L. (2023). High time for a higher‐level look at high‐technology: Plotting a course for managing government information in an age of governance. Public Administration Review, 83(2), 429–434.

Ezugwu, A. E., Ikotun, A. M., Oyelade, O. O., Abualigah, L., Agushaka, J. O., Eke, C. I., and Akinyelu, A. A. (2022). A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects. Engineering Applications of Artificial Intelligence, 110, 104743.

Fogel, D. B. (2006). Evolutionary computation: toward a new philosophy of machine intelligence. John Wiley & Sons.

Gobble, M. (2018). Digital strategy and digital transformation. Res. Technol. Manag. 61(5), 66–71.

Gong, Y., Yang, J., & Shi, X. (2020). Towards a comprehensive understanding of digital transformation in government: Analysis of flexibility and enterprise architecture. Government Information Quarterly, 37(3), 101487.

Grandhi, B., Patwa, N., and Saleem, K. (2021). Data-driven marketing for growth and profitability. EuroMed Journal of Business, 16(4), 381-398.

HAI. Stanford Institute for Human-Centered Artificial Intelligence (2023). Artificial Intelligence Index: Measuring trends in Artificial Intelligence. Stanford University.

Hansson, J., Ovretveit, J., and Brommels, M. (2012). Case Study of How Successful Coordination Was Achieved between a Mental Health and Social Care Service in Sweden. International Journal of Health Planning and Management 27(2), e132–e145.

Henman, P. (2020). Improving public services using artificial intelligence: possibilities, pitfalls, governance. Asia Pacific Journal of Public Administration, 42(4), 209 – 221.

Hwang, T. (2018). Computational power and the social impact of artificial intelligence. arXiv preprint arXiv:1803.08971.

Jambeiro Filho, J. (2015). Artificial Intelligence in the Customs Selection System through Machine Learning (SISAM). Prêmio de Criatividade e Inovação da RFB. Brasília: Receita Federal do Brasil.

Joffe, H. (2011). Thematic analysis. Qualitative research methods in mental health and psychotherapy: A guide for students and practitioners, John Wiley & Sons.

Kouziokas, G. N. (2017). The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation research procedia, 24, 467-473.

Liu, S. M., and Kim, Y. (2018). Special issue on internet plus government: New opportunities to solve public problems? Government Information Quarterly, 35(February), 88–97.

Lopes, K. M. G., Macadar, M. A., and Luciano, E. M. (2019). Key drivers for public value creation enhancing the adoption of electronic public services by citizens. International Journal of Public Sector Management, 32(5), 546-561.

Lu, H., Li, Y., Chen, M., Kim, H., and Serikawa, S. (2018). Brain Intelligence: Go beyond Artificial Intelligence. Mobile Networks and Applications, 23(2), 368–375.

Manyika, J., Chui, M., Miremadi, M., Bughin, J., George, K., Willmott, P., & Dewhurst, M. (2017). A future that works: AI, automation, employment, and productivity. McKinsey Global Institute Research, Tech. Rep, 60, 1-135.

Marsden, G., Frick, K. T., May, A. D., and Deakin, E. (2011). How Do Cities Approach Policy Innovation and Policy Learning? A Study of 30 Policies in Northern Europe and North America. Transport Policy 18(3, SI): 501–512.

McCarthy, J. (2007). What is artificial intelligence.

McCarthy, J., Minsky, M. L., Rochester, N., and Shannon, C.E. (1955). A Proposal for The Dartmouth Summer Research Project on Artificial Intelligence. AI Magazine, 27(4), 12–14.

McCulloch, W. S., Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5(4), 115–133.

Mikhaylov, S. J., Esteve, M., and Campion, A. (2018). Artificial intelligence for the public sector: opportunities and challenges of cross-sector collaboration. Philosophical transactions of the royal society a: mathematical, physical and engineering sciences, 376(2128), 20170357.

Munn, Z., Pollock, D., Khalil, H., Alexander, L., McLnerney, P., Godfrey, C. M., Peters, M. and Tricco, A. C. (2022). What are scoping reviews? providing a formal definition of scoping reviews as a type of evidence synthesis, JBI Evidence Synthesis, 20(4), pp. 950-952.

Newell, S., and Marabelli, M. (2020). Strategic opportunities (and challenges) of algorithmic decision-making: A call for action on the long-term societal effects of ‘datification’. Strategic Information Management (pp. 430-449).

Obschonka, M., and Audretsch, D. B. (2020). Artificial intelligence and big data in entrepreneurship: a new era has begun. Small Business Economics, 55, 529-539.

Oecd (2019), The Future of Work: OECD Employment Handbook 2019. Highlights. OECD Publishing, Paris. Available at Retrieved on 04-Apr-2023.

Oecd (2019b). Recommendation of the Council on Artificial Intelligence. Available at Retrieved on 14-Abr-2023.

Oliveira, T., and Martins, M. F. (2011). Literature review of information technology adoption models at firm level. Electronic Journal of Information Systems Evaluation, 14(1), 110. eISSN: 1566-6379

Oliveira, T., Thomas, M., and Espadanal, M. (2014). Assessing the determinants of cloud computing adoption: An analysis of the manufacturing and services sectors. Information and Management, 51(5), 497-510.

Omitaomu, O. A., Niu, H. (2021). Artificial intelligence techniques in smart grid: A survey. Smart Cities, 4(2), 548-568.

Pan, M. J., and Jang, W. Y. (2008). Determinants of the adoption of enterprise resource planning within the technology-organization-environment framework: Taiwan's communications industry. Journal of Computer information systems, 48(3), 94-102.

Paré, G., Trudel, M. C., Jaana, M., and Kitsiou, S. (2015). Synthesizing information systems knowledge: A typology of literature reviews. Information and Management, 52(2), 183-199.

Peters, M. D., Godfrey, C., McInerney, P., Munn, Z., Tricco, A. C., & Khalil, H. (2020). Chapter 11: scoping reviews. JBI manual for evidence synthesis, 169(7), 467-473.

Pournader, M., Ghaderi, H., Hassanzadegan, A., & Fahimnia, B. (2021). Artificial intelligence applications in supply chain management. International Journal of Production Economics, 241, 108250.

Ransbotham, S., Kiron, D., Gerbert, P., and Reeves, M. (2017). Reshaping business with artificial intelligence: Closing the gap between ambition and action. MIT Sloan Management Review, 59(1).

Rogers, E.M. (1995). Diffusion of Innovations (3rd ed.). New York: MacMillan

Rosenblat, A., & Hwang, T. (2016). Regional diversity in autonomy and work: A case study from Uber and Lyft drivers. Intelligence and Autonomy, 1, 15.

Sakhnyuk, P. A., and Sakhnyuk, T. I. (2020, June). Intellectual technologies in digital transformation. In IOP Conference Series: Materials Science and Engineering (Vol. 873, No. 1, p. 012016). IOP Publishing.

Saldaña, J. (2021). The coding manual for qualitative researchers. The coding manual for qualitative researchers, 1-440.

Saura, J. R., Ribeiro-Soriano, D., and Palacios-Marqués, D. (2022). Assessing behavioral data science privacy issues in government artificial intelligence deployment. Government Information Quarterly, 39(4), 101679.

Schmidt, R., Zimmermann, A., Möhring, M., and Keller, B. (2020). Value creation in connectionist artificial intelligence–a research agenda. AMCIS 2020 proceedings-Advancings in information systems research. August 10-14, 2020, Online, 1-10.

Sikdar, S. (2018). Artificial intelligence, its impact on innovation, and the Google effect. Clean Technologies and Environmental Policy, 20(1), 1–2.

Slowik, A., and Kwasnicka, H. (2020). Evolutionary algorithms and their applications to engineering problems. Neural Computing and Applications, 32(16), 12363-12379.

Susha, I., and Gronlund, A. (2014). Context Clues for the Stall of the Citizens’ Initiative: Lessons for Opening up E-Participation Development Practice. Government Information Quarterly, 31(3): 454–465.

System of National Accounts (SNA), 2008. New York: United Nations Statistical Commission (UNSC).

Thornberg, R., and Charmaz, K. (2014). Grounded theory and theoretical coding. The SAGE handbook of qualitative data analysis, 5, 153–69.

Tomaev, N., Cornebise, J., Hutter, F., Mohamed, S., Picciariello, A., Connelly, B., Belgrave, D., Ezer, D., van der Haert, F. C., Mugisha, F., Abila, G., Arai, H., Almiraat, Tomaev, N., et al (2020). AI for social good: unlocking the opportunity for positive impact. Nature Communications, 11.

Tornatzky, L. and Fleischer, M. (1990). The process of technology innovation. Lexington (MA): Lexington Books.

Van Noordt, C., and Misuraca, G. (2022). Artificial intelligence for the public sector: results of landscaping the use of AI in government across the European Union. Government Information Quarterly, 39(3), 101714.

Vial, G. (2019). Understanding digital transformation: A review and a research agenda. The journal of strategic information systems, 28(2), 118 – 144. eBook ISBN9781003008637

Viechnicki, P. and W.D. Eggers (2017). How much time and money can AI save government? Cognitive technologies could free up hundreds of millions of public sector worker hours. Deloitte University Press.

Weber, K. M., Heller-Schuh, B., H. Godoe, and Roeste, R. (2014). ICT-Enabled System Innovations in Public Services: Experiences from Intelligent Transport Systems. Telecommunications Policy 38(5/6), 539–557.

Williams, E., Galvin, J., and LaBerge, L. (2021). The new digital edge: Rethinking strategy for the postpandemic era. McKinsey Global Publishing.

Wirtz, B. W., Weyerer, J. C., and Geyer, C. (2019). Artificial intelligence and the public sector—applications and challenges. International Journal of Public Administration, 42(7), 596-615.

World Bank (2023). Manufacturing, value added as % of GDP – Brazil. World Bank national accounts data, and OECD National Accounts data files. Available at Retrieved on 11-Jun-2023

Yang, Z., Kankanhalli, A., Ng, B. Y., and Lim, J. T. Y. (2013). Analyzing the enabling factors for the organizational decision to adopt healthcare information systems. Decision Support Systems, 55(3), 764-776.

Yang, Z., Sun, J., Zhang, Y., and Wang, Y. (2015). Understanding SaaS adoption from the perspective of organizational users: A tripod readiness model. Computers in Human Behavior, 45, 254-264.

Yin, Robert K. 2018. Case Study Research and Applications: Design and Methods. Sixth ed. Los Angeles: SAGE

Zhao, M., Agarwal, N., Basant, A., Gedik, B., Pan, S., Ozdal, M., Komuravelli, R., Pan, J., Bao, T., Lu, H., Narayanan, S., Langman, J., Wilfong, K., Rastogi, H., Wu, C., Kozyrakis, C., and Pol, P. (2021). Understanding and codesigning the data ingestion pipeline for industry-scale recsys training. CoRR, abs/2108.09373.

Zhu, K., Kraemer, K., and Xu, S. (2003). Electronic business adoption by European firms: a cross-country assessment of the facilitators and inhibitors. European Journal of Information Systems, 12(4), 251-268.

Zou, J., Han, Y., and So, S. S. (2009). Overview of artificial neural networks. Artificial neural networks: methods and applications, 14-22.




How to Cite

Guedes, L., & Oliveira Júnior, M. (2024). Artificial intelligence adoption in public organizations: a case study. Future Studies Research Journal: Trends and Strategies, 16(1), e860.



Artigos / Articles