We are entering an era of AI Determinism where we obsess over the technical specs of our tools while ignoring our own behavioral responses to them. We believe that as algorithms become more precise, productivity must also increase. We chase that coveted 99th percentile with the belief that the more reliable the machine is, the more effective we as humans become.
Somewhere in between the race to master AI fluency, hone our digital intelligence, and maximize technological outputs, a gap has developed where the risks of AI can start to creep in. Many organizations measure success through metrics like time saved, output volume, completion rates, and other visible immediate productivity indicators. However, if we aren’t careful, AI blind spots begin to develop and can introduce systemic risks to our independent skills and expertise, a lot of which took years to learn and hone.
A growing body of research suggests that AI-assisted performance doesn’t always hold up when the tool is removed. In some cases, AI assistance improves immediate performance while reducing independent judgment people need to perform well without it. The risk is that people may look more productive while becoming less practiced, less confident, or less able to evaluate the work without AI. Human contribution becomes less frequent and more consequential at the same time.
The risks of AI automation
Automation inherently carries a hidden trade-off. In 1983, Lisanne Bainbridge described this as one of the ironies of automation. As systems become more capable, people perform less of the routine work, leaving them less prepared to intervene in high-stakes situations when their judgment matters most. Effective oversight requires accurate mental models, domain knowledge, and situation awareness. Yet routine automation strips away the practice and feedback that maintain those capabilities.
AI brings those risks into everyday knowledge work. People do less of the producing, synthesizing, and creating. Instead, they simply evaluate what the system produced. In many cases, that demands more expertise, situated judgment, and a deep understanding of the task context. Without those capabilities, the dangers of AI become clearer with the disturbing reality that the human role narrows just as the stakes of human oversight rise.
What is deskilling and never-skilling?
The most unsettling finding in recent research: AI assistance can make skilled professionals worse at their jobs. Researchers call it “deskilling,” the gradual erosion of independent capability under sustained assistance, and the evidence is becoming harder to ignore. Research on cognitive automation has documented how skill erosion can become self-reinforcing. As a system performs more of a task, employees receive less practice. Reduced practice weakens expertise and confidence. Lower confidence increases reliance on the system. The automated system continues to compensate for the declining human capability, so the problem may remain hidden until the system fails or an unusual case requires independent judgment.
Never-skilling occurs when people begin relying on AI before they have built the underlying capability to perform the task, relying on automation from the very beginning. An early-careers professional or a new employee of an organization can produce competent-looking work without ever developing the mental models, factual knowledge, or pattern recognition that an experienced employee uses to judge that work.
Entry-level and routine tasks, while sometimes menial in nature, often serve as informal training. They expose employees to repeated examples, exceptions, mistakes, and corrective feedback. Some of that work is tedious, but it helps people build the knowledge base that supports human judgment. When organizations automate those tasks, they remove both the production burden and the experience through which expertise develops. The intended progression from routine work to higher-level analysis breaks when employees reach the higher-level role without the knowledge built through the earlier work.
What really shapes human-AI performance?
The Human-AI Collaboration framework treats AI-supported work as a system that develops over time. Each component of this AI interaction – the human, AI itself, and the task at hand – brings its own factors that need to be considered:
People enter that system with different foundations, including domain knowledge, reasoning capability, curiosity, confidence, evidence standards, and awareness of their own limitations.
AI brings its own features, such as the ability to provide direct answers, explanations, evidence, alternatives, or uncertainty information.
The task contributes its stakes, complexity, feedback, and learning value.
All of these conditions shape collaboration behaviors. People may direct the AI clearly, produce an initial view, request explanations, verify evidence, challenge recommendations, revise the output, integrate contextual knowledge, and retain ownership. They may also delegate the full task, copy the response, perform a surface review, and accept the result without adjusting their confidence.
As these interactions are repeated, the resulting behaviors create different system states. Calibration describes how well a person’s level of trust in AI matches its actual reliability for the task. Well-calibrated reliance means knowing when AI can be trusted. Miscalibrated reliance occurs when people trust AI too much or too little. Offloading involves shifting part of the mental effort to AI. It can be strategic when people stay properly engaged. It becomes abdication if they completely withdraw from the reasoning process. Over time, these patterns can strengthen human capability or gradually weaken important skills.
The outcomes therefore extend beyond task quality and speed. Many other factors need to be considered including:
- Learning
- Independent performance
- Error detection
- Confidence calibration
- Ownership
- Accountability
- Well-being
- Capability over time
The future of AI in the workplace
The human-AI collaboration model’s systems perspective helps explain why use of AI in the workplace cannot be evaluated through adoption rates or productivity alone. The same output can reflect very different underlying processes and very different future capability trajectories.
Moving forward, the discussions need to take it a step further to evaluate the true impact of these systems both to the organization and the individuals interacting with them. Striking a balance that maximizes productivity without sacrificing the skills that employees have worked diligently to learn will be one of the biggest challenges businesses will face. Those who do find that middle ground will be the best positioned to capitalize on the benefits of AI while preserving the highest standard of human involvement.
AI isn’t delivering ROI on its own. People are.
Download this guide to discover why organizations that invest in human capability alongside AI are better positioned to improve productivity, decision quality, innovation, and long-term performance.
WHAT YOU’LL LEARN
- Why AI ROI depends on more than adoption metrics and how organizations often mistake usage for transformation.
- The human factors behind successful AI collaboration, including critical thinking, judgment, learning agility, and decision quality.
- The risks of overlooking workforce readiness, from over-trust and AI misuse to inconsistent performance across teams.
- Talogy’s Human AI Collaboration Model, which helps organizations understand how people interact with AI, how collaboration evolves over time, and how it influences business outcomes.
- How to align talent strategy with AI strategy to maximize productivity, innovation, and organizational performance.