AI tools can close a ticket, draft code or generate a cloud configuration in seconds. But when IT professionals use them to cross a knowledge gap without learning what sits beneath the answer, today’s productivity gain can become tomorrow’s liability.
TalentLMS calls that accumulating backlog of missing skills “learning debt” and recently published a survey finding 41% of employees say they believe their roles are evolving faster than their employers’ ability to train them.
Meanwhile, nearly six in 10 respondents said they use AI at least sometimes for tasks they were never trained to perform, and half use it to complete work they do not fully understand.
The risk is easy to overlook because the assignment still gets completed. In the survey, 37% of employees said AI had made them appear more competent than they were, while 29% had delivered work they could not fully explain.
Learn the Why
Avoiding learning debt does not require abandoning AI. It requires treating the technology as a learning partner rather than a substitute for understanding.
“Professionals who are using AI to replace their thinking are the ones who are falling short,” says Jack Hogan, vice president of Advanced Growth Technologies at SHI International.
He says treating AI as an enablement tool and not as a replacement for knowledge is key to staying sharp and not becoming dependent on it.
“Those who are curious and choose to question AI are building real expertise that helps in understanding the systems, models, and technologies and will be best equipped down the line,” he says.
Instead of accepting an answer, IT professionals can ask the tool to explain its reasoning, compare possible approaches or identify weaknesses in its recommendation. They should then validate the response through testing, documentation or an independent source.
Andrea Lorenzon, CPTO at Epignosis, the parent company of TalentLMS, says professionals should develop enough judgment to determine whether an AI-generated answer is correct.
“Used well as an AI learning assistant rather than an autopilot, it can accelerate both performance and development,” she says.
Suresh Sigera, senior instructor at General Assembly, recommends a simple test: Consider whether the task could still be completed if the AI tool suddenly disappeared.
“If the answer is no, it’s worth learning how to handle it manually before turning to AI,” he says.
From his perspective, understanding how a task works end-to-end makes you a much better critic of what AI hands back to you.
Spot Hidden Gaps
Learning debt often becomes visible when conditions change. An employee may produce acceptable work under normal circumstances but struggle when a system fails, requirements shift or an unusual edge case appears.
“The biggest warning signs of learning debt are when an IT worker can use AI effectively but struggles to troubleshoot when it fails,” Hogan says.
He notes other warning signs are when they accept AI outputs without validating accuracy or understanding the reasoning.
An inability to explain a technical decision is an additional warning. Nicolás Zapata, delivery director at Globant, says polished execution can conceal a fragile understanding of the work.
“That disconnect tends to surface in a few predictable ways, like resistance to new tools or a struggle to walk a client through the thinking behind a decision during code review or a planning session,” Zapata says.
He cautions learning debt tends to hide behind competence for a long time, which is exactly what makes it dangerous.
The problem can remain hidden from managers. The TalentLMS research found close to half (47%) of employees had stayed quiet about not knowing how to perform a work-related task, while 28% said their managers did not know how frequently they struggled with required skills.
Protect the Fundamentals
As AI assumes more routine work, foundational knowledge becomes more important, not less. IT professionals still need to recognize when an output is inaccurate, insecure or inappropriate for the business context.
“IT professionals should continue developing systems thinking, troubleshooting, security, architecture, and an understanding of how technologies interact,” Lorenzon says.
These capabilities help them determine whether an AI-generated solution is accurate, appropriate, and secure.
That includes retaining practical skills such as reading code, tracing errors to their source and testing whether a proposed fix works beyond the most obvious scenario.
“Producing working code doesn’t mean you’ll be able to fix it when it breaks,” Sigera says.
Hogan points to data architecture, data quality, cloud infrastructure, governance, security and compliance as durable areas of expertise.
Human oversight remains critical because AI cannot independently understand an organization’s full risk tolerance, regulatory obligations or business priorities.
“AI doesn’t have the capability to do that on its own,” he says. “That’s why AI needs to work alongside humans to ensure their systems are staying secure and accurate.”
Communication and product thinking also matter. IT professionals increasingly need to explain trade-offs, translate technical decisions for executives and connect an implementation to a measurable outcome.
Prove the Understanding
Managers can help identify learning debt by evaluating the process behind the deliverable instead of looking only at its speed or polish.
“Managers can ask employees to explain their reasoning and strategy, not just present the data and their outputs,” Hogan says. “They can also keep a lookout for people who are improving workflows and not just completing tasks faster.”
Code reviews, scenario exercises and post-project discussions can reveal whether employees understand the systems they are working with.
Managers can ask what would happen if a solution failed, how an edge case should be handled or why one technical approach was selected over another.
“The question isn’t whether someone used AI,” Sigera says. “It’s whether they understand the work.”
Build Career Resilience
Knowing how to operate AI tools will increasingly become a baseline expectation. Career growth will favor professionals who can govern, integrate and improve AI systems while applying independent technical judgment.
“As AI becomes standard in IT, simply knowing how to use it will no longer be a competitive advantage,” Lorenzon says. “What will matter is the expertise and judgment to use it well.”
Employers may consequently place greater emphasis on case-based interviews and demonstrations that reveal how candidates challenge AI output, troubleshoot failures and keep projects moving without automated assistance.
“Those are the people managers are going to trust with harder assignments and more responsibility,” Sigera says. “Learning debt is ultimately going to shape who gets hired and promoted, and who doesn’t.”
