Barriers to adopting automated organisational decision-making through the use of artificial intelligence

Authors: (Left to right) Dawid Booyse, The Hongkong and Shanghai Banking Corporation, South Africa, and, Caren Scheepers, Gordon Institute of Business Science, South Africa

An overview of your research

While artificial intelligence (AI) has shown its promise in assisting human decision, there exist barriers to adopting AI for decision-making (Moser et al., 2021), The study aimed to identify barriers in the adoption of AI for automated organisational decision-making. AI plays a key role, not only by automating routine tasks but also by moving into the realm of automating decisions traditionally made by knowledge or skilled workers (Autor and Dorn, 2013; Frey and Osborne, 2013; Loebbecke and Picot, 2015)

The study uniquely applied the adaptive structuration theory (AST) model, DeSanctis and Poole (1994) to AI decision-making adoption, illustrated the dimensions relevant to AI implementations and made recommendations to overcome barriers to AI adoption. The AST offered a deeper understanding of the dynamic interaction between technological and social dimensions.

Why is the research needed?

Several previous studies have shown that decision-making that requires highly cognitive skills, traditionally performed by knowledge workers, can be automated in an organisational context (Manyika et al., 2017; McAfee and Brynjolfsson, 2014). This study determines the factors that impede organisations from adopting AI for automated decision making.

The research if of key importance due to the increased rise of AI adoption by many organisations and as companies strive to optimise and automate to increase productivity and lower cost not every situational problem or human process can be replaced with AI. It is thus key for organisations to understand the limitations or barriers when considering the adoption of AI.

Organisations can use our research through the lens of AST to firstly identify key barriers or pitfalls when adopting AI and avoid or overcome them thus making the adoption of AI a smoother process and less costly.

What were the research themes explored?

Theme 1: The need for social interactions and norms in the workplace can be a barrier to AI adoption.

Theme 2: Regulatory and liability concerns can be a barrier to AI adoption.

Theme 3: Environments requiring creativity, spontaneity and intuition can be barriers to AI adoption.

Theme 4: The need for transparency and trust in decision-making can be a barrier to AI adoption.

Theme 5: Dynamic and constantly changing business environments can be barriers to AI adoption.

Theme 6: The loss of control and power for current decision makers can be barriers to AI adoption.

Theme 7: The need to be ethical and non-discriminatory can be a barrier to AI adoption.

A summary of the research methodology

The study applied an interpretive paradigm and conducted exploratory research through qualitative interviews with 13 senior managers in South Africa from organisations involved in AI adoption to identify potential barriers to using AI in automated decision-making processes.

What were the results from your research?

The first theme on social interactions and norms that are needed in the workplace, which is a barrier to AI adoption, relates to the atmosphere dimension, namely, that impersonal and formal structured interactions with AI characterised the technology adoption. Team dynamics and the employee-leader bond could be severely impacted by having AI make team decisions thus in a human social environment, AI would be better suited to augment decision-making rather than replace a human leader.

The second theme on regulatory and liability concerns as a barrier to adoption of AI related to the AST dimension of conflict management dimension. The resolution of conflict of AI with regulations like reducing jobs is difficult to achieve. Governments could be forced to introduce taxes to prevent increased unemployment, and in struggling economies, there is the ethical dilemma of organisations having to choose between increased revenue from the use of AI and increased unemployment with its negative social impact on communities.

The third theme on creativity, spontaneity and intuition that are required in environments which create barriers for AI adoption, relates to the efficiency dimension of AST because time is compressed due to the amount of data that can be processed fast, but creativity is lacking. Organisations might need to consider reskilling employees more towards creative and people-based skills rather than data
processing.

The fourth theme as a barrier to AI adoption is transparency and trust needed in decision-making. The AST dimension that was most related to this theme was the decision-making process dimension. The type of decision-making process, which is being promoted through the use of AI, is that the decision-making process is being dominated by AI due to automation that is one-sided in its decision-making. Strong quality control measures and AI algorithm audits will be needed to prevent discrimination.

The fifth theme as barriers to AI adoption was dynamic and constantly changing business environments. This theme related to the AST dimension on efficiency, since time is compressed with AI, which makes it efficient. AI is mostly designed to solve very specific problems and is trained on well-prepared data sets. In a fast-paced, dynamic environment, AI could potentially be unaware of all the relevant data and variables needed for accurate decisions. The occurrence of random events could also render AI decisions inaccurate.

The sixth theme revealed that one of the barriers to AI adoption is the loss of control and power for current decision makers. This theme relates to the AST leadership dimension as AI operates in an exclusive manner. Organisations need strong change management and organisational transformation practices, combined with programmes to reskill affected employees, to avoid resistance and hostility towards implementing AI.

Conclusion

Managers should be aware that AI is not a silver bullet. AI algorithms today are designed to solve very specific problems or to automate a specific task. AI algorithms require data very specific to the problem domain to achieve a high rate of accuracy. AI algorithms today can be trained to perform any task based on data, as well as or better than humans can perform.

This study uniquely applied the AST model to AI adoption. We thus contributed by extending the AST model and illustrating the dimensions relevant to AI implementations and made recommendations to overcome barriers of AI adoption.

References

• Autor, D.H. and Dorn, D. (2013), “The growth of low-skill service jobs and the polarization of the US labor market”, American Economic Review, Vol. 103 No. 5, pp. 1553-1597.
• DeSanctis, G. and Poole, M.S. (1994), “Capturing the complexity in advanced technology use: Adaptive structuration theory”, Organization Science, Vol. 5 No. 2, pp. 121-147.
• Frey, C.B. and Osborne, M.A. (2013), “The future of employment: how susceptible are jobs to computerisation?”, Technological Forecasting and Social Change, Vol. 114, pp. 254-280.
• Loebbecke, C. and Picot, A. (2015), “Reflections on societal and business model transformation arising from digitization and big data analytics: a research agenda”, The Journal of Strategic Information Systems, Vol. 24 No. 3, pp. 149-157.
• Manyika, J., Chui, M., Miremadi, M., Bughin, J., George, K., Willmott, P. and Dewhurst, M. (2017), “A future that works: automation, employment and productivity”, McKinsey Global Institute,
  available here.
• McAfee, A. and Brynjolfsson, E. (2014), The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies, WW Norton and Company, New York, NY.
• Moser, C., den Hond, F. and Lindebaum, D. (2021), “Morality in the age of artificially intelligent algorithms”, Academy of Management Learning and Education, Vol. 21 No. 1, published online 7 April 2021, doi: 10.5465/amle.2020.0287.
   

Author Bios:

Dawid Booyse, Experienced Software Development Manager with a demonstrated history of working in the financial services and banking industry. Skilled in Banking, Artificial Intelligence, Agile Methodologies, Java and Databases. Strong computer science professional with a Master of Business Administration (M.B.A.) focused on Business Administration and Management, from Gordon Institute of Business Science. Dawid also holds a BSc Honours Computer Science degree and is currently working as Head of Software delivery for HSBC in South Africa.

Caren Scheepers is a Professor in Contextual Leadership; Organisational Design and Gender Studies and management consultant who has worked in various environments to gain experience in Assessments, Leadership, Team and Organisational Development. Caren's passion for people development has encouraged her to pursue studies and a career in the field of psychology, management consulting and executive coaching. 
Caren holds a Ph. D. in Psychology and certificates in Organisational Development and Clinical Hypnosis. Prof Caren holds a full Professor role at the Gordon Institute of Business Science.