AI at work: Navigating employee backlash and implementation challenges
Recently, Shopify CEO Tobi Lutke asked employees requesting more resources to prove they “cannot get what they want done using AI,” according to a memo posted to X. The directive reflects a wave of anxiety among workers that AI will eliminate potential jobs—or their current ones.
- 41% of C-suite executives said adopting generative AI (genAI) is tearing their company apart and creating power struggles, per Writer’s 2025 Generative AI Adoption in the Enterprise survey.
- 31% of employees admit to sabotaging their company's genAI strategy, with higher percentages among younger workers, the survey found.
Business leaders face a challenge: How can they encourage AI experimentation without harming employee morale?
The AI implementation gap
Among C-suite execs, 75% think AI implementation has been successful in the past 12 months, but among employees, that number is just 45%, per Writer.
If AI implementation is careless, with leadership stressing the need to “adopt” and “innovate” without clear directives, “I can see that being a point of friction,” said our analyst Gadjo Sevilla on our “Behind the Numbers” podcast.
Nearly half (49%) of C-suite members say employees have been left to figure out genAI on their own, per the Writer survey.
This implementation gap extends to basic communication about AI strategy. While 89% of executives claim their company has an AI strategy, only 57% of employees are aware of such plans, highlighting a failure in organizational communication.
Why employees resist AI adoption
Many workers view genAI as a threat to their value, creativity, and their jobs, potentially adding to their workload, per the Writer survey.
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"It's the responsibility of leadership to change the perception," said our senior vice president of media content, Henry Powderly. "If the C-suite executives in this survey feel like it's tearing their company apart, they're obviously making it a priority while at the same time not giving any resources out to their teams to figure this stuff out."
The narrative around AI implementation needs reframing, according to Sevilla: "I think the narrative should be that it's a tool that can help augment but not replace your employees. And using AI for things like support or just doing the more menial, time-sucking tasks, that could make a big difference in an eight-hour workday."
The AI accuracy paradox
Another challenge facing organizations is the paradoxical messaging around AI use. Employees are simultaneously encouraged to use AI for efficiency, while being warned about its limitations and inaccuracies.
AI was incorrect in over 60% of news queries, with accuracy varying across platforms, according to a March study from the Columbia Journalism Review.
"The news landscape is full of small players, scrapers... The language models already have a challenge when it comes to disseminating the most authoritative news sources," Powderly said. "It's more problematic because the language model doesn't say 'I don't know' when it's confused. It makes up an answer."
Practical tips for effective AI implementation
1. Start small and measure success. "When piloting AI tools, you should start small. So just use it on one team, one department," Sevilla said. Then leadership can measure its effectiveness and replicate the use case across teams.
2. Train AI on your style. Using features like "Claude styles" can train AI on specific writing examples, said Powderly. "It does a really good job of helping you come up with a style standard."
3. Be specific and follow up. The more precise your requests to AI tools, the better the outcome. If the first output misses the mark, follow up with specific instructions for improvement.
4. Always verify AI outputs. Despite advances in AI technology, these tools still make mistakes and occasionally fabricate information. Fact-checking remains essential for any AI-generated content.