Kopilot founder Kenny Hill presented at Mumbrella360 on a new kind of cognitive overload hitting marketing teams. He calls it AI brain fry, and it is not about the tools failing.
The pitch was fewer repetitive tasks and more creative thinking. The reality, for many marketers, is more repetitive reviewing and less time to think.
Kenny Hill has a name for what is happening to marketing teams in 2026. He calls it AI brain fry.
Hill, founder of AI business consultancy Kopilot, presented the concept at Mumbrella360 last week. The term describes a specific kind of cognitive overload: the mental fatigue that comes from spending a working day prompting, reviewing, steering and correcting AI outputs across multiple tools and contexts.
It is different from regular decision fatigue. Traditional decision fatigue comes from making consequential choices under pressure. AI brain fry comes from something lower-stakes but relentlessly continuous: hundreds of small evaluations every day, each requiring enough judgment to assess whether the AI output is good enough, accurate enough or directionally right enough to use.
The nature of the problem
Marketing teams have absorbed the productivity argument for AI faster than most. They were early adopters. But the adoption curve created an assumption that the tools would reduce cognitive load. In practice, for many teams, they have redistributed it.
Before AI, a copywriter produced three pieces of work a day. Now they review and revise twenty. The volume went up. The judgment required per piece did not go down. What went away was the creative engagement with the work itself, replaced by an editorial function that is less satisfying and more draining.
Hill's framing is significant because it shifts the conversation away from AI productivity wins and toward the human cost of adoption at scale. The tools are not failing. The teams using them are absorbing a new kind of cognitive tax that was not in the change management plan.
What it looks like
The symptoms that teams describe are specific. Loss of creative confidence, because constant AI suggestions erode trust in one's own judgment. Difficulty distinguishing good work from passable work, because the evaluative bar has blurred. Decision avoidance around AI outputs, defaulting to "good enough" because the evaluation cost is too high. A general flatness to the work coming out of the team.
None of these show up on a productivity dashboard. Output volume is up. Quality is harder to measure. The people doing the work are tired in a way they cannot easily explain to a manager who is tracking task completion rates.
The management gap
Organisations that deployed AI tools without deploying governance frameworks to match are now encountering the cost of that decision. The productivity gains are real but unevenly distributed. The people nearest the tools absorb the burden. The people looking at the dashboards see the upside.
Hill's recommendation from Mumbrella360 is practical rather than philosophical: teams need explicit review time built into workflows, not just production time. The assumption that reviewing AI output takes less time than creating it is wrong for complex work and wrong for brand-sensitive categories where every output carries reputational risk.
The AU context
Australian marketing teams are, by most benchmarks, under-resourced relative to the volume of content they are now expected to produce. The AI adoption story in Australia has been driven by efficiency arguments aimed at boards and CFOs. The conversation about what that efficiency costs the people operating inside those systems has been slower to surface.
Hill naming it at Mumbrella360 gives teams language for something they have been experiencing without a clean way to describe it. AI brain fry is not a reason to avoid AI. It is a reason to design work around it more carefully than most organisations have managed so far.