AI May Soon Replace Even the Most Elite Consulting Companies

AI

By Mostafa Sayyadi and Michael J. Provitera 

Now, with the onslaught of AI, the tried-and-true business model has seriously endangered the survival and continuity of management consulting organizations. Perhaps the survival and continuity of both large and small companies will be to engage in more agility and more adaptable models that offer a full suite of services from human resource management to strategic management consulting.

The Increasing Rise of Artificial Intelligence for Business

By leveraging AI, businesses are able to make real-time decisions, streamline processes, reduce costs, and increase efficiency. [1] [2] [3] With a more distributed decision-making process, organizations can empower people to make decisions faster and more accurately utilizing the data available. [4] [5] [6] Businesses around the world are automating their processes to improve customer relationships, allowing them to respond faster and more efficiently, increasing customer satisfaction and creating a competitive edge. [7] [8] [9]

There is a minimal rate of error in Google’s algorithms, and this is why Google is regarded as the apex of high-tech artificial intelligence. With AI technology, organizations can move beyond existing limitations and gain access to innovative business models and by utilizing AI and continuous learning, organizations can reach a high level of excellence.

Alibaba’s success serves as an inspiration to other businesses looking to leverage the power of AI for their own success. Algorithms, by introducing consistency and accuracy, allow businesses to reduce mistakes, optimize processes, and ultimately reach the ambitious goals of Six Sigma: having few errors. AI programs are not considered in most corporate strategies by top executives. A company without an AI strategy, while its competitors are quickly advancing in the market, is like a Formula One (F1) race where all the competitors are driving high-tech F1 cars, but one team is driving a fast but regular streetcar. Just as the streetcar cannot compete with the advanced technology of the F1 cars, the company without an AI strategy will fall behind its competitors who are leveraging AI. [10] [11] [12] Are CEOs at risk to be replaced if they do not consider AI-powered strategy? We don’t have the answer obviously, but what is more than reasonable realizing that AI is needed to enable businesses to gather and analyze data, to make better and quicker decisions. 

What are the actions to be undertaken? 

  • Identify dynamic capabilities to support continuous growth through exploiting competitive advantages and being agile, considering limited or not full availability of strategic resources. [13]
  • Design, develop and align people and IT /Digital systems through a Digital mindset embedded into organisational culture. [14] 
  • Creating a flexible and resilient workforce
  • Implement an effective knowledge management system to ensure that companies have the experience retained to adapt and perform in a changing environment and use the double-loop learning to augment the experience. [15]

Ineffective knowledge management leads to the loss of expertise and knowledge, delays, and inefficiencies in communications, which limits innovation and collaboration, resulting in lower productivity and success. RPA and chatbots and AI will allow the creation of data supported by a data culture that can be coupled and enhanced by AI for strategy development.

Digital core knowledge includes technical and digital literacy skills that are essential to using and interacting with digital tools and technologies, effectively and efficiently. This is how digital core knowledge enables data-driven decisions that are more accurate and reliable than ever before. CEOs must consider cost benefits and determine if and what AI-enhancing software is the most effective and efficient solution for their business needs. Otherwise, any further development of AI-powered strategies can be hampered if the software is not efficiently evaluated and implemented. Additionally, in order to streamline and automate many processes, organizations must redesign their current processes.

To ensure buy-in and successful implementation, CEOs should emphasize the importance of leveraging AI and other automation tools to augment and enhance human capabilities. This can help to foster a culture of collaboration between human resources and technology, allowing for greater efficiency and innovation.

Management Consulting in the AI and Automation Era

Management consulting firms like KPMG, Deloitte, PwC and BCG, use a variety of automation and AI. The technologies are based on data analytics, intelligent automation, machine learning, natural language processing, computer vision, and chatbots to business process optimisation, trend identification, forecasting and strategies related, enabling micro-decision making. [16]

On a different level than the Big 4, the application of artificial intelligence for management and strategy purposes seems to be nearer than from a conceptual perspective only. “Robo-advisors” (RAs). In fact, the utilization of RAs is a trend which brings a LOW level of disruption based on the trust in algorithm as authority aids in legitimating RAs as innovation. Guaranteeing and protecting the consulting knowledge domain.

It is worth bringing other two examples of disruption of the classic management consulting business model supplied by AI services providers for consulting.

Praioritize can be allocated in a middle level between pure RAs and standardised knowledge. It is a SaaS company owned and operated by Dutch company, Transparency Lab, that started with generative A.I. in 2020. They present digital consulting solutions AI empowered very close to RAs. Their technical proposal disrupts the classic consultancy business model as AI draws knowledge from a huge database of white papers (11.000) and real case studies (23.000) to solve problems. The first disruption is related to the standardized Knowledge while AI does assessments and generates proposals utilizing RAs. The second disruption is related to that the company’s targets which are both Consultants and Companies. Creating a possible way to cut-off consulting starting from their knowledge management for clients. [17]

On another side, AI solutions based, allow the knowledge domain protection and there are partial utilisations of automation. The disruption is evident when automating certain tasks, of the consulting lifecycle, while maintaining and improving the engagement based on client-consultant relationships. Mindset.ai uses AI to deliver property knowledge and services such as consulting, change management, coaching, with a dedicate Machine learning repository for each professional, utilizing the AI model Chat-GPT 4. Here knowledge is delivered by AI according to customised and tailored services for specific client needs.

Recently, Chat-GPT has started dominating the scene for multiple businesses. At a lower level based on the research of O’Mahoney (2023), consulting packages should stand on the assumption that AI chat-GPT should be [18]:

  • Used to interrogate clients’ documents.
  • Connected to the internet and clients’ documentation.
  • Used to improve clients’ marketing operations.
  • Used with bots to answer common questions from clients.
  • Used to process large amounts of data to recognize patterns and trends; and 
  • Finally, used to analyze consulting data better and faster.  

The integrated model will invite managers and staff to learn about AI and will highlight the importance of intellectual property and human capital. 

AI also raises new needs for clients. [19] These new needs manifest themselves in using AI to provide more accurate data analysis and make more accurate forecasts for clients. [20] [21]

Management consulting in the AI era needs to offer more adaptive management consulting packages for clients to meet these new needs. Following the predictions of Christensen, Dina, and Derek (2013) the impact of DT on the MC services have received an increased level of analysis even if it is not possible to speak yet about maturity models. [22] [23]

The number of startups will increase as the great resignation persists along with quiet quitting and quiet firing. [24] [25] [26] [27] The dependence on outside consultants will increase and cause management consulting companies to focus on the intersection of knowledge management and innovation. This huge disruption will cause a close relationship between management consulting companies and startups related to artificial intelligence as showed above for Praioritize and Mindset. 

Thus, management consulting companies will turn to using artificial intelligence to serve clients and advise them more effectively to better understand environmental threats and respond to them better in real-time. Recurring to AI will be a huge pill to swallow for consulting industry anyway.

In addition to this, with the increase of automation, both partial or total, we will assist to the evolution of the trust models and client-consultants’ relationships, for virtual and digital consultancy along the consultancy life-cycle [28], and the additional trust element for AI and automation adoption thus for innovative technology. [29] 

In Conclusion

Prepare your organization for a new workplace that is technologically challenged and prone to use AI to do the work. If you keep doing what you always have done, then you will continually get what you always got and if that is not working then inertia has set in. Since management consulting impacts every industry, every business will eventually be realizing some form of transformation. Do not ignore the beginning signs of this transformation because they are visible to many.

About the Authors

Mostafa SayydiMostafa Sayyadi works with senior business leaders to effectively develop innovation in companies and helps companies—from start-ups to the Fortune 100—succeed by improving the effectiveness of their leaders.

Michael J ProviteraMichael J. Provitera is a senior faculty professor of Management and Leadership, in the Andreas School of Business at Barry University, Miami, Florida, USA . He is an author of Level Up Leadership: Engaging Leaders for Success, published by Business Expert Press.

References

  1. https://praioritize.com
  2. https://www.mindset.ai/product

References

  1. Demir, F. (2022). Artificial Intelligence. In: Innovation in the Public Sector. Public Administration and Information Technology, Vol 39. Springer, Cham. https://doi.org/10.1007/978-3-031-11331-4_4
  2. Enholm, I.M., Papagiannidis, E., Mikalef, P. et al. (2022) Artificial Intelligence and Business Value: a Literature Review. Information Systems Frontiers, Vol. 24, pp. 1709–1734. https://doi.org/10.1007/s10796-021-10186-w
  3. Davenport, T., Guha, A., Grewal, D. et al. (2020) How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, Vol. 48, pp. 24–42. https://doi.org/10.1007/s11747-019-00696-0
  4. De Mauro, A., Sestino, A. & Bacconi, A. (2022) Machine learning and artificial intelligence use in marketing: a general taxonomy. Italian Journal of Marketing, Vol. 2022(4), pp. 439–457. https://doi.org/10.1007/s43039-022-00057-w
  5. Sarker, I.H. (2022) AI-Based Modeling: Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems. SN Computer Science, Vol. 3, pp. 1-20. https://doi.org/10.1007/s42979-022-01043-x
  6. Sousa, M.J., de Barros, G.O., Tavares, N. (2022). Artificial Intelligence Trends: Insights for Digital Economy Policymakers. In: Guarda, T., Anwar, S., Leon, M., Mota Pinto, F.J. (eds) Information and Knowledge in Internet of Things. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-75123-4_8
  7. Choudhary, R., Karmel, A. (2022). Robotic Process Automation. In: Raje, R.R., Hussain, F., Kannan, R.J. (eds) Artificial Intelligence and Technologies. Lecture Notes in Electrical Engineering, Vol. 806. Springer, Singapore. https://doi.org/10.1007/978-981-16-6448-9_3
  8. Itagi, S., Gowda, S., Udupa, T., Shylaja, S.S. (2022). Future Frame Prediction Using Deep Learning. In: Raje, R.R., Hussain, F., Kannan, R.J. (eds) Artificial Intelligence and Technologies. Lecture Notes in Electrical Engineering, Vol 806. Springer, Singapore. https://doi.org/10.1007/978-981-16-6448-9_21
  9. Sharma, M., Kumar, C.R.S. (2022). Machine Learning-Based Smart Surveillance and Intrusion Detection System for National Geographic Borders. In: Raje, R.R., Hussain, F., Kannan, R.J. (eds) Artificial Intelligence and Technologies. Lecture Notes in Electrical Engineering, vol 806. Springer, Singapore. https://doi.org/10.1007/978-981-16-6448-9_19
  10. Barriga, A., Rutle, A. & Heldal, R. (2022). AI-powered model repair: an experience report—lessons learned, challenges, and opportunities. Software and Systems Modeling, Vol. 21, pp. 1135–1157. https://doi.org/10.1007/s10270-022-00983-5
  11. Belhadi, A., Mani, V., Kamble, S.S. et al. (2021). Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: an empirical investigation. Annals of Operations Research. https://doi.org/10.1007/s10479-021-03956-x
  12. Kersting, K. (2018). Making AI Smarter. KI – Künstliche Intelligenz, Vol. 32, pp. 227–229. https://doi.org/10.1007/s13218-018-0562-8
  13. Fabrizio, C., Kaczam, F., & De Moura, G. (2021). Competitive advantage and dynamic capability in small and medium-sized enterprises: a systematic literature review and future research directions. Review of Managerial Science volume 16, 617–648.
  14. Neeley, T., & Leonardi, P. (2022,) Developing a Digital Mindset. Harvard Business Review, volume 100(5-6), pp. 50-55.
  15. Gribbin, J., A. M., Young, R., & Park, S. (2016). Double-loop reflective practice as an approach to understanding knowledge and experience. International Conference: Future–Focused Thinking (pp. Vol. 8, pp. 3181-3198).
  16. Ross,M & Taylor, J. (2018, November 10). Managing AI Decision-Making Tools. Retrieved from Harvard business Review: https://hbr.org/2021/11/managing-ai-decision-making-tools
  17. Libert, B. and Beck, M., 2017. AI may soon replace even the most elite consultants. Harvard Business Review, 24(7).
  18. O’Mahoney, J. (2023). How can AI help my consultancy?, https://www.linkedin.com/pulse/how-can-ai-help-my-consultancy-prof-joe-o-mahoney/
  19. Bankins, S., Formosa, P. (2023). The Ethical Implications of Artificial Intelligence (AI) For Meaningful Work. Journal of Business Ethics. https://doi.org/10.1007/s10551-023-05339-7
  20. Weber, F. (2023a). Business Analytics and Intelligence. In: Artificial Intelligence for Business Analytics. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-37599-7_1
  21. Weber, F. (2023b). Artificial Intelligence. In: Artificial Intelligence for Business Analytics. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-37599-7_2
  22. Christensen C, Dina W, Derek B (2013) “Consulting on the Cusp of Disruption”, Harvard Business Review, available at: https://enterprisegrowth.org/wp-content/uploads/2017/08/Consulting_On_the_Cusp.pdf
  23. Deelmann, T. (2019). Consulting: Characterization of Concepts and Connections. In: Nissen, V. (eds) Advances in Consulting Research. Contributions to Management Science. Springer, Cham. https://doi.org/10.1007/978-3-319-95999-3_2
  24. Bala Subrahmanya, M.H. (2022). Competitiveness of High-Tech Start-Ups and Entrepreneurial Ecosystems: An Overview. International Journal of Global Business and Competitiveness, Vol. 17, pp. 1–10. https://doi.org/10.1007/s42943-022-00056-w
  25. Cavallo, A., Ghezzi, A. & Rossi-Lamastra, C. (2021). Small-medium enterprises and innovative startups in entrepreneurial ecosystems: exploring an under-remarked relation. International Entrepreneurship and Management Journal, Vol. 17, pp. 1843–1866. https://doi.org/10.1007/s11365-020-00698-3
  26. Hura, J., Al-Rabeei, S., Korba, P., Hovanec, M., Pjurová, S. & Sekelová, I. (2022). Development of Startups During the Covid-19 Pandemic. In: Perakovic, D., Knapcikova, L. (eds) Future Access Enablers for Ubiquitous and Intelligent Infrastructures. FABULOUS 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Vol. 445. Springer, Cham. https://doi.org/10.1007/978-3-031-15101-9_18
  27. Savin, I., Chukavina, K. & Pushkarev, A. (2022). Topic-based classification and identification of global trends for startup companies. Small Business Economics. https://doi.org/10.1007/s11187-022-00609-6
  28. Collina, L. (2021). What are the implications of virtualisation for building trust during the management consultancy lifecycle. Management consulting Journal, 7, 18-28. : https://doi.org/10.2478/mcj-2021-0015
  29. Bedué, P. and Fritzsche, A., 2022. Can we trust AI? An empirical investigation of trust requirements and guide to successful AI adoption. Journal of Enterprise Information Management, 35(2), pp.530-549. DOI 10.1108/JEIM-06-2020-0233

The views expressed in this article are those of the authors and do not necessarily reflect the views or policies of The World Financial Review.