AI Reliance Linked to Lower Managerial Decision Quality, New Study Finds

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FORMOSA NEWS - Sorong - A 2026 study by Kamaluddin of the University of Muhammadiyah Sorong, together with Waras from the University of Wijaya Putra and Dharma Widada from the University of Mulawarman, reveals that heavy reliance on artificial intelligence (AI) in decision-making can reduce the quality of managerial judgment. Published in the Formosa Journal of Science and Technology, the research highlights a growing concern in modern organizations: while AI improves efficiency, it may also weaken critical thinking if overused.

The findings arrive at a time when businesses and public institutions increasingly integrate AI into daily operations. From financial forecasting to strategic planning, AI systems are now central to how decisions are made. This study provides timely evidence that the human role in decision-making remains essentialand potentially at risk.

Growing Dependence on AI in Decision-Making

Organizations worldwide are adopting AI to handle complex data and accelerate decision processes. In Indonesia and beyond, this shift is part of a broader digital transformation aimed at improving productivity and competitiveness.

However, the study draws attention to a lesser-known phenomenon called cognitive offloading. This occurs when individuals delegate mental tasks such as analysis or judgment—to external tools like AI systems. While this reduces mental workload, it can also lead to reduced engagement in critical thinking.

Kamaluddin of the University of Muhammadiyah Sorong explains that AI is no longer just a tool. It increasingly acts as a “cognitive partner,” influencing how managers think and decide. This shift raises important questions about how much control humans retain over decision outcomes.

How the Study Was Conducted

The research involved 248 middle- and senior-level managers from organizations that actively use AI in decision-making. Participants came from various industries, ensuring a broad perspective on how AI is applied in real-world settings.

Data was collected through structured questionnaires and analyzed using a statistical modeling approach that identifies relationships between variables. The study focused on three key factors:

  • Cognitive offloading (dependence on AI)
  • Metacognition (awareness of one’s own thinking)
  • Cognitive bias (systematic errors in judgment)

This approach allowed the researchers to evaluate how these factors interact and influence managerial decision quality.

Key Findings: Efficiency Comes with Trade-Offs

The study presents clear and measurable results:

1. Cognitive offloading reduces decision quality
Managers who rely heavily on AI tend to produce lower-quality judgments (β = -0.32).

2. Cognitive bias remains a major risk
Bias significantly decreases decision quality, even in AI-supported environments (β = -0.37).

3. Metacognition improves decision quality
Managers with strong self-awareness of their thinking processes make better decisions (β = 0.29).

4. Metacognition partially offsets AI risks
It acts as a buffer, reducing the negative impact of over-reliance on AI.

5. Strong explanatory power
The model explains 64% of the variation in decision quality (R² = 0.64), indicating robust findings.

These results confirm a critical insight: AI enhances efficiency but does not guarantee better decisions.

The Hidden Risk: Automation Bias

One of the most significant concerns identified in the study is automation bias. This occurs when individuals accept AI-generated recommendations without sufficient scrutiny.

Waras from the University of Wijaya Putra notes that managers may assume AI outputs are inherently accurate, leading to reduced critical evaluation. Over time, this habit can erode analytical skills and increase the likelihood of errors.

Dharma Widada of the University of Mulawarman adds that decision-making quality depends not only on the technology itself but also on how users interpret and challenge its outputs.

Metacognition as a Critical Safeguard

Despite the risks, the study highlights a key solution: strengthening metacognitive skills.

Metacognition refers to the ability to monitor and evaluate one’s own thinking. Managers with high metacognitive awareness are more likely to question AI recommendations, reflect on alternatives, and make balanced decisions.

According to the authors, individuals who actively engage with AI rather than passively accepting its outputs can maintain or even improve decision quality. This suggests that human cognition remains a decisive factor in AI-supported environments.

As Kamaluddin from the University of Muhammadiyah Sorong emphasizes, the interaction between humans and AI is “dualistic” it can both enhance and undermine decision-making depending on how it is managed.

Implications for Business and Policy

The study offers several practical implications for organizations adopting AI:

1. Rethink the role of managers
Managers must evolve from decision-makers to supervisors of human-AI collaboration.

2. Invest in cognitive training
Organizations should prioritize critical thinking and metacognitive skills alongside technical training.

3. Design responsible AI systems
AI tools should support human judgment, not replace it entirely.

4. Encourage reflective decision-making
Managers should be trained to question and validate AI outputs before acting on them.

These insights are particularly relevant for industries where decisions carry high stakes, such as finance, healthcare, and public policy.

Author Profiles

Kamaluddin, S.E., M.M.
Researcher and lecturer at the University of Muhammadiyah Sorong, specializing in management and technology-driven decision-making.

Waras, S.E., M.M.
Academic at the University of Wijaya Putra with expertise in organizational behavior and modern management systems.

Dharma Widada, S.E., M.Si.
Researcher at the University of Mulawarman focusing on strategic management and digital transformation.

Source

Kamaluddin, Waras, & Widada (2026). Cognitive Offloading in AI-Supported Decision Making: Implications for Managerial Judgment Quality. Formosa Journal of Science and Technology, Vol. 5, No. 4, pp. 1043–1054.


This study reinforces a crucial message for the AI era: technology can assist decision-making, but it cannot replace human judgment. Organizations that balance AI capabilities with strong cognitive skills will be better positioned to make high-quality decisions in an increasingly complex world.

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