Assessing the Impact of Automation on Decision-Making Within Large Organizations

Automation is transforming decision-making processes within large organizations, but the role of human judgment remains critical. In his 2023 doctoral thesis, "Assessing the Impact of Automation on Decision Making Within Large Organisations," David Feavearyear explores this balance, providing a nuanced model that categorizes decision-making into three distinct zones. The research combines a comprehensive literature review and qualitative analysis of interviews with senior executives, offering valuable insights into how data sufficiency and complexity determine the suitability of automation in decision-making.

Key Themes

Data Sufficiency and Complexity as Determinants of Automation

Feavearyear argues that the appropriateness of automation for decision-making hinges on two key factors:

• Data Sufficiency: How much data alone can inform a decision.
• Data Volume/Complexity: The intricacy and amount of data required to make the decision.

These factors form the foundation for a model that defines three decision-making zones:

The research proposes a model outlining three distinct decision-making zones based on the interaction between data sufficiency and complexity:

• Coping Pioneers: In scenarios with high data sufficiency and complexity, machines excel at making rapid, data-driven decisions. Here, automation acts as a "coping pioneer," revealing insights and streamlining processes.

"If you take situations where you have very high volumes of data and very high data sufficiency, actually, you can build very good models to make a decision…machines become what I've called coping pioneers."

• Engineering Bottlenecks: When data sufficiency and complexity are low, human judgment becomes essential due to the subjective and nuanced nature of these decisions.
• Grey-zone Decisioning: In cases with moderate data sufficiency and complexity, humans and machines collaborate. Technology augments human judgment, but human intuition and experience still play a significant role.

Value of Human Judgment

While automation can enhance decision-making in data-rich environments, Feavearyear emphasizes the irreplaceable value of human judgment, particularly in high-stakes decisions that require navigating ambiguity and subjectivity.

• Intuition and Experience: Experienced individuals bring valuable insights drawn from past scenarios, allowing them to make informed decisions even with imperfect or incomplete data.

"Part of having a good imagination helps you think about possibilities. Think about what could be rather than what is."

• Subjectivity and Ambiguity: Human judgment is crucial for decisions involving emotional factors, subjective elements, or ambiguity.

The Role of Regulation and Ethics

Feavearyear underscores the importance of regulatory boundaries and ethical considerations in automated decision-making. While regulations may limit certain activities, they also provide necessary clarity and trust, ensuring responsible automation practices.

• Transparency and Trust: Automated decision-making must be transparent, especially concerning ethical considerations and consumer trust.

Change Management and Resistance

Resistance to automation comes from both internal and external sources. Historically, internal resistance stems from fears of job displacement, while external resistance often involves governments and regulatory bodies navigating the growing role of automation.

Important Concepts and Insights

The "Mangle of Practice"

Feavearyear discusses the messy, iterative nature of real-world decision-making, contrasting it with theoretical models. He emphasizes that decision-making is rarely linear and often deviates from planned frameworks.

Bounded Rationality

The thesis highlights the concept of bounded rationality, acknowledging the limitations of human decision-makers, who often settle for satisfactory solutions rather than striving for computationally complex, optimal outcomes.

"Being-in-the-World"

Drawing on Dreyfus's phenomenology, Feavearyear contrasts human decision-making with machine-based rationality, highlighting the importance of embodied experience and context in human judgment.

The Importance of Feedback Loops

Feedback loops are essential for refining the quality of automated decisions over time. Continuous evaluation of decision outcomes helps improve the decision-making model, enhancing both machine and human capabilities.

"As that work evolved, it got better because people learned about the quality of the decisions they were making…There is something about the quality of decisions that shifts and advances the arcs in your model."

Conclusion

David Feavearyear’s research provides a valuable framework for understanding how automation can coexist with human judgment in decision-making within large organizations. His model offers practical insights into when automation is appropriate and when human judgment should prevail. As organizations continue to adopt automation technologies, they must carefully balance data, technology, and human intuition while navigating regulatory and ethical considerations.

As automation evolves, organizations need to develop adaptive strategies that integrate machines as collaborators rather than replacements, ensuring that decision-making remains agile, responsible, and effective in the face of growing complexity.