How AI Can Augment Human Decision-Making

• Chinmay
• March 19, 2025
• Artificial Intelligence, Industrial IoT, Internet of Things
• AI Decision-Making, AI in manufacturing, automation, Data Analysis, Digital Transformation, Embedded Systems, Generative AI, industrial IoT, Industry 4.0, IoT Sensors, Job Opportunities, Machine Learning, Manufacturing Technology, predictive maintenance, smart factories, upskilling, Visual Inspection
• 0 Comments

Is AI Taking Humans Out of Decision-Making in Manufacturing?

AI promises to replace human labor in a variety of use cases, but it cannot supplant human intuition and creativity—especially in sensitive industries like manufacturing, where even minor mistakes can lead to catastrophic failures. Traditionally, human engineers have provided a safeguard by monitoring data for anomalies and making critical decisions, whether that means decommissioning machinery or making small adjustments to avoid potential breakdowns.

Today, however, smart IoT sensors, AI-powered models, and other digitization technologies can sift through enormous volumes of data from multiple sources and draw conclusions similar to those of a highly trained engineer—only much faster. This prompts the question: Will these advancements remove humans from the decision-making process entirely?

The reality is more nuanced than a simple yes or no. Consider a statistical process control scenario where temperature levels must stay within predefined upper and lower bands. If the temperature hovers just above the lower threshold repeatedly—without crossing it—an experienced engineer might sense potential trouble ahead and intervene. Many manufacturers would rather leave the choice of when or how to intervene in human hands.

Such examples illustrate that AI should not replace human decision-making but instead enhance an engineer’s ability to make accurate, proactive decisions. As AI becomes more adept at integrating and analyzing massive datasets, it will provide higher-quality insights faster—empowering human experts to act with confidence. Generative AI can also suggest recommendations like “According to this data, you should do X and Y,” but the final call remains with the engineer.

Case in Point: AI-Powered Visual Inspection

A prime example of AI–human collaboration is a visual inspection solution built by an end-to-end IoT solutions provider. Using advanced algorithms and deep-learning techniques, the system scans a production line’s wheel to detect missing or incorrect lug nuts, scratches, or other defects. When an issue is identified, the solution automatically generates a trouble ticket with visual feedback for the operator, enabling immediate action to minimize disruptions. By streamlining how defects are identified, this system enhances human decision-making rather than replacing it.

Co-Collaborators, Not Replacements

AI, like many other transformative manufacturing technologies, is meant to complement human expertise by delivering real-time, data-driven insights. While AI may reduce the need for certain repetitive tasks, it does not diminish the importance of human intuition and creativity in making difficult, high-stakes decisions.

Opportunities in IoT, Embedded, and AI
As AI-driven solutions continue to evolve, industries will require professionals skilled in IoT, embedded systems, and AI algorithms. This shift opens up numerous career paths for engineers, data scientists, and system architects who can develop and manage these smart platforms. Students looking to break into this field should focus on building strong foundations in sensor integration, machine learning, and data analysis. By mastering the latest AI frameworks, networking protocols, and device-level programming, they can stay ahead of the curve and contribute meaningfully to the ever-expanding realm of AI-enhanced manufacturing.