Is AI creating a barrier between leadership and employees

Fresh the award-winning intranet platform and part of Advania UK, has published findings from research based on a survey of 1,000 professionals across the UK and the United States. The Employee Attention Recession report studied the gap between the creators and recipients of internal communications.

That gap is two-fold, both in terms of the information produced by the leadership and the amount read by employees. Then the gulf in understanding between the intent and understanding.

According to employees, 83% believe that they receive too much information, with 35% saying that they can no longer keep up with the volume. The results are what Fresh calls the Attention Recession, where employees have rationed “their mental bandwidth.”

Other research backs this up. Politemail internal email benchmark report noted that the average open rate for internal emails is around 64%. Furthermore, employees spend, on average, an hour reading internal emails every month.

The second gaps what employees understand from those emails. Axios HQ research found that 80% of leaders felts internal comms are clear and engaging, only around 50% of employees agree. In fact, according to Fresh, only about 12% of internal emails are now read. The reason is that AI is now an additional barrier between leaders and employees.

The AI Barrier

As businesses increasingly use AI, communication could be worsening. According to the research, 95% of employees trust AI to accurately capture key points. However, 92% of Internal Communications (IC) professionals believe automated summaries distort the emotional register and intent.

In additional, as Amy Gallo points out in an HBR article, “But the use of AI, especially as an intermediary in—or even substitute for—work relationships, has the potential to damage the very connections we need at work.”

AI is causing an issue with 88% of employees using AI to rewrite any messaging sent by leadership in order to summarise it. Are they getting the message correctly? With only 12% actually reading the complete messages, this is a concern.

David Bowman, Product Director at Fresh, commented, “I’ve spent years at internal comms events, and as a vendor, people tell you the truth. The truth is: most communicators already know which content is noise. Awareness is a problem but it’s also that ‘no’ is a high-stakes word when the person making the request is also the link between you, the information you need, and the employees who need it. This report should give practitioners the data to say it.”

The research shows that AI-generated summaries have overtaken traditional channels as the leading way employees discover company news. Now, 28% of employees first receive company updates through AI, ahead of the intranet, leadership emails, and word of mouth.

Although 98.6% of IC professionals know their workforce is using AI to filter messages, only 33% of organisations have formal policies or monitoring in place to manage how that content is being reinterpreted.

Bowman added, “Subtraction is now a strategic necessity. To be heard in 2026, organisations must design content to survive the summary. If you don’t cut the noise, the ‘Ghost in the Machine’ will do it for you and you won’t like the result.”

Enterprise Times: What does this mean?

There are some serious concerns emerging about whether internal communication is as effective with AI as a barrier. The release (the full report was not available) infers that there is, but offers few suggestions for the fix. Can IC find new channels to avoid the AI summary whitewashing of messaging? Where AI translation is used, will the impact be even worse?

The future workplace is still evolving, and AI will play an important part. How will people build and maintain relationships internally. And how can they build trust if AI is acting as an intermediary?

There is another side to this, Leaders often issue poor communications; can AI actually improve understanding or clarify the original intent? It would be interesting to see more comprehensive research into this. Another factor is how often the messaging is created using AI; is that having an impact? If AI is summarising an already summarised AI-created message, will it miss out on key parts?

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