What CIOs can do when AI boosts performance but kills motivation

In the past few years, many in the tech field have outsourced part of their thinking to AI. Tools like ChatGPT, Copilot, and others have become commonplace, helping humans debug code, draft reports, or brainstorm ideas. Productivity is soaring. But something else is happening at the same time. Many techies invest less in the learning process, think less critically, and feel disconnected from their work. AI may be boosting performance, but it’s often draining motivation.

“We’ve seen a tendency for people to go on autopilot,” says Mike Anderson, CIO at Netskope. “When someone pastes AI-generated content into an email or presentation without reading or editing it, that’s not productivity — that’s disengagement.”

A recent experimental study published in Scientific Reports supports Anderson’s concerns. Researchers found that while AI improved performance in the moment, it didn’t lead to better results later on, when people tackled similar tasks without its help. Moreover, when subjects switched back to working solo, many said they felt less motivated and more bored. In other words AI helps us be sharper right now, but at the cost of making the next task feel more tedious, less engaging, and less meaningful.

The issue of machines leading to a lack of motivation and boredom on the job is not new. Long before AI, earlier waves of automation raised similar concerns about worker disengagement.

“It was a big deal a century ago when industrialization and automation created jobs for people that were very routine, as the machine did everything,” says Chester Spell, professor of management at Rutgers University’s Camden School of Business. “Alcoholism, demotivation, and even sabotage by employees posed a big problem, and this led to employee assistance programs that remain a huge part of the workplace today.”

As AI becomes more embedded in everyday workflows, removing friction and speeding up tasks, it’s changing not just how we work, but how we feel about our work. For leaders looking to harness the power of AI without compromising the human side of work, recognizing and addressing employee engagement, learning, and long-term motivation is essential.

The cost of making work too easy

Most people assume we’re wired to seek easy tasks and quick rewards. But what keeps us stimulated isn’t the simplicity, but the challenge. We try to take on high-stakes tasks not because they’re easy, but because they’re difficult enough to make progress feel earned. We’re naturally drawn to tasks that challenge and offer something new. In short, we’re wired not just for performance, but also for growth. The sweet spot between effort and mastery is what makes the experience satisfying.

“Work that doesn’t involve challenges we can overcome can reduce intrinsic motivation,” says Eva Lermer, a professor of business psychology and VP for excellence in research and academia at the Technical University of Applied Sciences Augsburg in Germany. “For example, we only have the chance to get into the so-called ‘flow state’ when the tasks are challenging but can be mastered with the skills we have.”

Further research shows that motivation depends largely on three factors: autonomy, competence, and relatedness. When these psychological needs are met, people feel more engaged, fulfilled, and connected to their work even in the absence of external rewards.

“If AI removes all challenges or uncertainties, it can also take away the opportunity to experience growth, mastery, and personal responsibility,” Lermer adds. “The result would be a task that feels efficient but meaningless to oneself.”

For many knowledge workers, identity is closely linked to their role as problem solvers. If AI takes over the entire problem-solving process, there’s little sense of achievement and therefore little meaning for the person performing the task. Paradoxically, boredom and loss of motivation can occur even when objective results improve.

“When AI takes over most of the exciting work, there’s no sense of achievement because all you’re doing is copying and pasting,” says Lermer. “It’s likely the person will feel more like a passive supervisor than a successful problem solver. This undermines a person’s self-esteem and self-confidence.”

It doesn’t mean we should avoid using AI in work considering its immense potential. But to realize its real value, there’s needs to be a rethink in the way it’s integrated.

How CIOs can spot the issues

When working with gen AI tools, it’s easy to assume everything’s going well. But CIOs must look beyond productivity metrics and make an effort to detect subtle signs that show when employees get bored.

“One of the clearest signs is copy-paste culture,” Anderson says. “When employees use AI output as-is, without questioning it or tailoring it to their audience, that’s a sign of disengagement. They’ve stopped thinking critically.”

To prevent this, CIOs can take a closer look at how teams actually use AI. Honest feedback from employees can help, but there’s often a gap between what people say they use AI for and how they actually use it in practice, so trying to detect patterns of copy-paste usage can help improve workflows.

CIOs should also pay attention to how AI affects roles, identities, and team dynamics. When experienced employees feel replaced, or when previously valued skills are bypassed, morale can quietly drop, even if productivity remains high on paper.

“In one case, a senior knowledge expert, someone who used to be the go-to for tough questions, felt displaced when leadership started using AI to get direct answers,” Anderson says. “His motivation dropped because he felt his value was being replaced by a tool.”

Over time, this expert started to use AI strategically, and saw it could reduce the ad-hoc noise and give him space for more strategic work. “That shift from threatened to empowered is something every leader needs to watch for and support,” he adds.

Anderson also noticed a subtle form of resistance to AI emerging from the company’s most creative teams. “It’s not vocal pushback, it’s silence and inaction,” he says. “That kind of quiet avoidance can be easy to miss, but it’s just as important to address.”

As with every major shift in technology, the real challenge is cultural. Success depends on preparing people to work and think differently, and trust new processes.

Keeping employees engaged and involved

One challenge CIOs have is to show employees that AI can be a collaborator, not a replacement, and how to work best with this technology. “We train teams to validate responses, fact-check data, and refine outputs, not blindly trust them,” Anderson says. “When AI makes mistakes, we use those as coaching moments to help improve prompt quality and critical thinking.”

The organization goes further than simply training its employees, making AI adoption a hands-on and competitive process, encouraging people to experiment.

“This fall we’re hosting a company-wide prompt-a-thon,” Anderson says. “It’s our version of a hackathon, focused on prompt writing, Gem creation, and NotebookLM use cases. Teams are creating role-specific Gems, like a Salesforce Architect or Product Owner, to streamline their day-to-day tasks while staying connected to the work.” The goal, he adds, is to turn passive users into critical thinkers and creators, because education around prompt writing and responsible AI use is now a critical skill.

After all, playing with AI-powered tools ultimately shows people what the technology can do and what the future might look like. It also builds transparency, demystifying what the limits of these tools are, and how human input still shapes the outcome.

“Our strategy is focused on clarity of outcomes, business case prioritization, and clear communication to the organization and how it impacts team members,” says Richard Amos, SVP and CIO of Blue Mantis. “We’re placing change management at the heart of our implementation to not only maintain cohesion, but enable team members to embrace the change and understand how it impacts them personally.”

Sustaining performance over time

To maintain both productivity and engagement in the long run, organizations must rethink workflows, create space for human judgment, and ensure technology serves to elevate not eclipse human contributions.

“Leaders should actively manage the psychological contract between employees and their work, and ask if people are still learning, growing, and proud of what they do,” Lermer says. “It’s also advisable to promote a culture in which using AI is seen as a skill rather than a shortcut.”

She also advises CIOs to design workflows that incorporate time for reflection. This way, employees can pause to assess and question the outputs generated by AI, which reinforces critical thinking and maintains a sense of ownership over the work. Equally important is to continuously refine how AI-powered tools are used. “That means building guardrails, creating educational programs, and reinforcing human judgment in every workflow,” Anderson says.

Spell also urges leaders to approach AI adoption with thoughtfulness. “Carefully consider the implications of introducing AI into individual work environments as opposed to just adopting the flavor of the month,” he says.