Cognitive Tech Debt: Is AI Making Your Workforce Faster but Less Capable?

What happens when AI makes employees more productive today but gradually weakens the expertise companies will depend on tomorrow?

In this episode of Tech Talks Daily, I speak with Dr. Margaret Cunningham, VP of Security and AI Strategy and Field CISO at Darktrace, about cognitive tech debt, the growing risk that companies are gaining short-term efficiency from AI while unintentionally weakening critical thinking, technical expertise, problem-solving ability, and human judgment.

Margaret brings a rare combination of experience to this conversation. With a PhD in Applied Experimental Psychology and a career spanning behavioral science, cybersecurity, privacy, human-centered security, and AI strategy, she examines technology adoption through the lens of how people actually think, learn, develop expertise, and make decisions.

She explains cognitive tech debt by comparing it with the technical debt familiar to software teams. Companies can introduce technology quickly and enjoy immediate improvements in speed and output, only to discover weaknesses underneath those gains later. With AI, the debt may accumulate in people. Employees can appear highly productive while outsourcing the difficult cognitive work required to build judgment, recognize patterns, understand failures, and develop genuine expertise.

We discuss emerging evidence that over-reliance on AI is already affecting professional skills. Software engineers may become less capable of diagnosing problems in code they did not create themselves. Medical professionals can lose decision-making capabilities when they become dependent on automated systems. Across knowledge work, deep reading and sustained concentration are increasingly being replaced by summarization, generation, and superficial review.

Margaret describes the current period as the “bridge years,” when AI systems are becoming increasingly capable but people still need to maintain the expertise required to recognize mistakes, question recommendations, recover from failures, and understand when automation should not be trusted. Companies cannot safely abandon human skills before technology can reliably perform those responsibilities without supervision.

The conversation also challenges one of the most repeated promises surrounding enterprise AI adoption: that automation will remove routine work and allow employees to concentrate on higher-value activities. Margaret argues that companies have done a poor job of defining which tasks people genuinely want to give up and which skills they need to preserve. Some of the repetitive, slow, and difficult work being automated may be exactly where people develop pattern recognition, creativity, and professional judgment.

This creates a serious challenge for cybersecurity teams and other high-stakes professions. If employees become reviewers of AI-generated outputs rather than practitioners developing expertise through experience, where will the next generation of senior engineers, security analysts, doctors, researchers, and technical specialists come from?

Margaret explains why leaders need to understand which AI techniques are being used for different business problems rather than treating every form of artificial intelligence as interchangeable. Large language models, machine learning systems, behavioral analytics, and other technologies have different strengths and limitations. Knowing what questions to ask requires domain expertise, creating a difficult paradox for companies that may be automating away the very experience needed to govern these systems responsibly.

We also examine the human consequences of AI adoption. Technical specialists who enjoy solving difficult problems can lose motivation when meaningful work is replaced by reviewing machine-generated outputs. Companies may struggle to understand who owns decisions made through collaboration between humans and AI, while younger employees could lose access to the experiences that previously helped people progress from beginners to experts.

Margaret offers practical advice for business and technology leaders deciding how quickly to introduce AI across their workforce. Companies can identify the skills they need to preserve, create opportunities for employees to practice difficult cognitive work, use simulations and training to maintain expertise, ask teams which aspects of their jobs give them purpose, and resist pressure to automate every task simply because the technology exists.

The message is not anti-AI. Margaret sees enormous potential for artificial intelligence in scientific research, cybersecurity, productivity, and solving difficult problems. But realizing those benefits requires a more intentional relationship between people and machines.

For business leaders, CISOs, technology teams, AI practitioners, and anyone concerned about the future of human expertise, this conversation provides a practical framework for recognizing cognitive tech debt, deciding what should and should not be automated, preserving critical thinking skills, and building healthier forms of human-AI collaboration.

AI can make people faster. The bigger question is whether companies can capture those productivity gains without losing the human capabilities they will need when the technology gets something wrong.