According to the researcher, this behavior is particularly common among people who work with multiple AI agents. Due to their high scalability, errors could quickly spiral out of control if appropriate controls and permissions were missing.
“You often only see the negative effects after three, four or five steps,” she emphasizes. “It then takes extensive cleanup and detective work to figure out where the agent took a wrong turn.”
Use AI…but don’t to much
Interestingly, more than half of those surveyed report that they receive more support from AI than from their superiors in their everyday work. They also often find collaborating with AI easier than working with humans.
At the same time, many face a dilemma when it comes to disclosing their use of AI. 54 percent of – from their own perspective – particularly successful AI users use unauthorized tools or use approved tools illegally. 36 percent hide how much AI contributes to their performance.
According to Hinds, this depends heavily on the company culture and sense of psychological safety. “In many organizations there is massive pressure to demonstrate AI competence and show that you are a power user,” she explains. On the other hand, some employees wanted to avoid their performance appearing to be too dependent on AI and their own value being questioned.
What successful organizations do differently
According to the report, leading companies are not characterized by spending more time on AI. Rather, they invest more time in work around AI. This includes that they
- set the context,
- define quality standards;
- Develop judgment, and
- decide which tasks should not be delegated to a model in the first place.
The companies with the greatest transformation potential are proactively addressing AI challenges: They offer training and support, view AI as an opportunity to redesign work, and formally reward AI skills.
One of the most difficult skills is knowing when not to use AI, explains Hinds: “It’s not just about clicks or tokens spent, but about real skills and real learning.”
In addition, successful companies clearly communicated their AI strategy and explained the meaning behind it. Governance should not be a static set of rules, but must be continuously developed.
This needs to happen at every level, including senior management, emphasizes Hinds: “It’s about seeing how the leaders are using the technology and sharing both the success stories and the failures.”
Successful companies also actively use metrics that are based on existing key performance indicators (KPIs). They measure quality, efficiency and employee engagement in different ways and provide employees with data so they can assess their own acceptance and success.
“It’s less about monitoring and more about feedback on how we work together,” explains the research director.
Hings finds it “fascinating, but perhaps not surprising” that employees are increasingly using AI themselves as a learning tool and preferring it to other learning channels. This underscores the importance of low-code and no-code tools with a flat learning curve and organizational context that are directly embedded in workflows.
“This is very different from what we have seen with previous technologies.” (mb)
This article originally appeared at our sister publication CIO.com.
