How AI Dependency Is Killing Innovation Skills
As more companies implement AI solutions across their operations, a recurring concern has emerged: how will AI impact or displace the workforce?
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In a previous post about the future of work with AI, we explored both sides of this debate. While it’s true that job displacement is occurring across industries due to AI’s ability to expedite, automate and handle tasks that previously required entire teams, there’s an even greater opportunity for companies to create roadmaps that can help employees reimagine their roles through human-AI collaboration.
As I contemplated this displacement versus upskilling debate, I identified an even more critical concern that many companies are overlooking: how AI deskilling could actually prevent organizations from innovating by causing employees to lose their foundational skills. This isn’t just an HR quandary; rather, it should be a company-wide strategic mission that drives competitive advantage, strengthens risk management and creates long-term value.
“How AI deskilling could actually prevent organizations from innovating by causing employees to lose their foundational skills.”
What Is AI deskilling?
Deskilling happens when employees become so over-reliant on AI tools that they lose the core competencies required for their roles. While this might seem like simple laziness, the reality is more concerning: workers forget the fundamental skills needed to innovate, problem-solve and adapt when AI systems fail or encounter novel situations.
Consider this example impacting healthcare: The New York Times recently asked, “Are AI Tools Making Doctors Worse at Their Jobs?” The story revealed that while AI technology initially significantly improved doctors’ detection rates of precancerous growths during colonoscopies, an observational study revealed troubling results. In the three months before AI implementation, doctors identified growths in about 28% of colonoscopies. After the AI tool was implemented, their detection rate actually fell to 22%, which was well below their baseline performance. The evidence suggests that the doctors became over-reliant on AI assistance rather than taking the time to independently verify their scans.
This deskilling pattern has emerged across multiple industries. This study shows how Aviation provides perhaps the starkest example of these hidden costs: pilots who become overly dependent on automated flight systems eventually lose the manual flying skills needed when those systems fail. This creates a dangerous “vicious cycle” whereby awareness of skill loss leads to even greater reliance on automation. “The 1995 Cali, Colombia, crash tragically demonstrated these risks. Pilots accustomed to computerized navigation systems struggled to recognize terrain proximity and spatial orientation using traditional methods when automation couldn’t provide expected assistance.” This illustrates how deskilling doesn’t just reduce capability; it creates critical vulnerabilities precisely when human expertise is needed the most.
The Strategic Opportunity: Companies that recognize this pattern early on can build “anti-fragile” organizations that become stronger through AI disruption rather than more fragmented. By maintaining foundational employee skills while leveraging AI capabilities, these organizations create sustainable competitive advantages that their fully-automated competitors cannot match.
Why Deskilling Threatens Your Bottom Line
When it comes to investing in your employees and building an anti-fragile organization, this McKinsey study shows why AI’s biggest potential threat isn’t the technology itself; rather, it’s how companies implement it. “48% of employees rank training as the most important factor for gen AI adoption; yet nearly half feel they are receiving moderate or less support.” Even more concerning is that more than a fifth of them reported that they had received minimal to no support at all.
This training gap creates the perfect conditions for deskilling. When employees don’t receive proper support to maintain their foundational skills alongside AI adoption, companies risk losing the uniquely human expertise and critical thinking that drives innovation and handles edge cases when AI fails.
The business case for prevention is compelling: Forbes has estimated that those companies that emphasize employee development will yield 218% higher income per employee. With this article estimating that over 120 million workers are poised to undergo retraining in the next three years as AI reshapes industry demands, the companies that get ahead of this curve will capture a significant competitive advantage. The ROI on training? There is an average cost saving of 70%-92% when employers upskill existing employees rather than hire new ones.
Real-World Consequences of Deskilling
As AI becomes more entrenched across business, especially in roles where there is redundancy or the need to streamline processes, job displacement has also become very real. Yet, the greater unrealized threat is the risk of becoming overdependent on technology, which impacts not just business, but education and how we all communicate.
Just as texting and emails transformed how we communicate, AI is reshaping how we think and create. Take the simple example of penmanship. My handwriting wasn’t all that great to begin with, but thanks to my over-reliance on texts and emails, it now requires subtitles to translate what I’ve handwritten—which gives new meaning to the old adage “You lose what you don’t use.” However, this isn’t just about illegible handwriting, it’s about what we’re losing cognitively.
As the CNN article “Has technology ruined handwriting?” points out: “People aren’t using their minds and they are relying on technology to make the decisions for them.” The research shows that cursive writing combines mental and physical processes involving both sides of the brain, while “if you are typing or texting, it’s a matter of punching and finger-moving. You are doing very little thinking because you are not allowing your brain to form neural processes.”
The mechanization of textile production in the 19th and early 20th centuries provides a historical parallel. The introduction of mechanical looms replaced skilled artisans who could design, create and produce fabrics with unskilled factory workers who could only operate machines. While factories could produce textiles faster and cheaper, workers lost the comprehensive knowledge of fabric creation; they could follow instructions on how to operate the machinery, but couldn’t innovate or problem-solve without the guidance of the factory system. This separated the thinking (design) from the doing (production), creating efficiency, but eliminating the deep, uniquely human expertise that drove innovation.
The lesson for business leaders is clear: they must identify how to balance relying on AI without diminishing their employees’ cognitive abilities and capacity for innovation, both of which are vital to gaining—and maintaining—a competitive advantage.
The Competitive Advantage Hidden in AI Dependency
As AI becomes seamlessly integrated into our email platforms, browsers and applications, it is becoming increasingly difficult for us to not lean heavily on this technology for both business and personal needs. This dependency requires us to think less and less, eventually resulting in skill atrophy that diminishes our ability to problem-solve and innovate—just as texting and emailing have hurt the mental and physical aspects of penmanship.
But here’s what I find exciting: while most companies are automating everything without thinking about the consequences, smart leaders have an opportunity to build something that their competitors can’t replicate. The businesses that figure out how to balance human expertise with AI capabilities aren’t just avoiding deskilling, they’re creating sustainable innovation advantages.
There are still things that we as humans can do that strike the right balance between man and machine. My friend and strategist Neil Perkin shared how we can achieve this:
Human Vision + Machine Iteration: Humans set the strategic direction while AI rapidly tests scenarios and explores possibilities within that vision.
Outcomes + Optimization: Humans define goals and success metrics; AI optimizes execution within those human-defined boundaries.
Curiosity + Support: Humans explore and discover; AI organizes, analyzes and reveals patterns in what they find.
Tastemakers + Generators: Humans curate and decide; AI creates multiple options and variations for human selection.
Social Nuance + Simulation: Humans navigate complex interpersonal situations; AI provides data modeling and identifies potential oversights.
The long-term effects of AI dependency could be much greater than we realize, but the companies that recognize this pattern early won’t just avoid the pitfalls, they’ll turn it into a competitive edge.
Questions about your AI strategy?
Email me here. As always, thank you for reading and/or listening.
