AI Skills Gap Is Real – and Faking AI Fluency Won’t Cut It AnymoreAI Skills Gap Is Real – and Faking AI Fluency Won’t Cut It Anymore

AI illiteracy is quietly becoming a liability in the tech workforce. Despite 95% of tech executives calling AI skills critical for job security, a whopping 91% admit to overstating their own expertise.

That's according to a Pluralsight survey of 1,200 executives and IT professionals in the U.S. and the UK, which found nearly eight in 10 (79%) tech workers overall are faking their AI fluency.

The report paints a picture of a workforce caught between pressure to adapt and fear of falling behind: 61% of employees say using generative AI at work is viewed as "lazy," while 90% fear AI could replace their roles, and 91% believe their current skills are becoming obsolete.

The findings suggest that as organizations push to integrate AI technologies, the challenge isn't just adoption — it's bridging the gap between perception and real capability across all levels, all the way up to the C-suite.

"This tendency isn't unique to AI," OB Rashid, chief technology officer at Absorb Software, said. "In any domain, there will always be individuals who strive to be the smartest person in the room and others who rely on a 'fake it till you make it' mindset."

In the case of AI, this may be best explained by the Dunning-Kruger effect, where limited understanding leads to inflated confidence, he said.

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"The illusion of expertise is especially risky in a fast-moving field like AI," Rashid cautioned. "With capabilities doubling every few months and the landscape constantly shifting, staying current is non-negotiable."

He argued that organizations need to place smart, timely bets to stay ahead.

"When leaders misjudge what's possible or what's truly valuable, they risk chasing hype, wasting resources, and falling behind," Rashid said. "In this race, missteps don't just cost money – they cost momentum and market relevance."

Reliable Benchmarks Are Critical

Pluralsight CTO Chris McClellen said reliable benchmarking is a critical first step in effectively addressing the gap between IT teams' perceived and actual AI proficiency.

"Without objective baseline data about their team's skills, leaders cannot strategically plan for upskilling," he said.

McClellen added that IT leaders set the tone for technology culture within their organizations.

"Providing access to generative AI tools that can enhance productivity and then promoting use cases, best practices, and notable outcomes from using those technologies is a great way to change the culture around AI within an organization," he said.

McClellen noted that investing in AI education is also important because it shows a commitment to embracing these tools.

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"In today's economy, everyone is a technologist to some degree," he said.

Prompt engineering courses provide universal skills that every professional can use to get better information from large language models (LLMs), regardless of their functional area within the organization. 

Normalization, Enablement of AI

Wrike CEO Thomas Scott said his organization has found that normalization and enablement are the keys to closing the AI proficiency gap.

"AI tools shouldn't feel like optional add-ons — they should be integrated into everyday workflows in a way that encourages curiosity, experimentation, and ongoing learning," he explained.

When employees see AI actively embedded in the systems they already use, the mystery and pressure behind it dissipates, Scott said.

"People feel more empowered to ask questions, try things out, and build real fluency over time, not just feign understanding to keep pace," he said.

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McClellen said it's important to establish a learning culture, where employees don't fear identifying their technology deficiencies, but instead see it as a growth opportunity that can make them more effective.

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"Of course, once skills gaps are identified, employees need to have access to quality educational content that can help them close those gaps," he said.

Rewarding or celebrating upskilling milestones among teams and individuals can help motivate learning, which in turn leads to innovative thinking. 

Scott said another critical element is offering IT teams visibility into AI's actual impact.

In Wrike's research, while 46% of knowledge workers said they feel their tasks are low-impact, only 14% of their workload is assisted by AI.

"This gap presents an opportunity," Scott said. "If organizations make AI assistance more visible by highlighting how automation enhances work rather than how it replaces workers, employees will feel more comfortable using these tools rather than exaggerating their AI proficiency out of fear."

From his perspective, cultural perception around AI adoption is a leadership issue, not a technology one.

"Our internal initiatives encourage employees using AI to increase output, reduce burnout, and free up time for strategic thinking," he said.

When leadership uses and endorses these tools transparently and celebrates time savings or workflow enhancements as wins, the narrative about using AI shifts from "lazy" to "smart."

The Upskilling Gameplan

To maximize the impact of AI adoption, organizations must recognize that a one-size-fits-all approach to training will likely fall short.

Therefore, it is crucial to strategically establish distinct learning pathways tailored to the specific needs and responsibilities of both technical experts and non-technical professionals.

This ensures that each group receives the knowledge and skills necessary to effectively contribute to the organization's AI initiatives.

Rashid said IT leaders can help by investing in targeted upskilling paths, offering hands-on exposure to AI tools, and creating space for people to experiment.

"When employees recognize AI as a pathway to higher-value roles rather than a risk to their current ones, fear gives way to confidence — and the entire organization becomes more resilient and future-ready," he explained.

Scott said the most effective upskilling programs also emphasize personalization.

"With AI playing a larger role in high-impact work, training should focus on how employees can leverage AI for strategic decision-making, not just operational efficiency," he said.

This can look like a shift from simply "teaching AI" to "teaching AI application" — helping employees understand when and how to apply AI tools for the strongest business impact.

McClellen added that learning must be built into the job description.

"Team members need to see upskilling as a responsibility that is baked into their roles, and leaders need to offer time, incentives, and access to upskilling tools so that their teams can meet that responsibility," he said.

From Scott's perspective, upskilling should start with empathy.

"Rather than positioning AI as a replacement, IT departments should position it as a tool to reduce repetitive tasks and enable more meaningful contributions," he said.