AI boosts brainstorming but may slow the creative process

Generative AI has emerged as a powerful catalyst for creative brainstorming, yet it can slow experienced designers once the work turns toward finishing a piece.

The finding reframes AI not simply as a productivity tool but as a collaborator whose benefits and drawbacks depend on where a creator stands in the creative process.

Generative AI slows experts

Inside poster design tasks that moved from sketching ideas to producing final artwork, the tension between inspiration and execution became visible.

Jinghui Hou, an assistant professor at the University of Houston (UH), linked the delay to expert habits.

Less experienced designers could take AI output and move on, while veterans often stopped to revise, edit, and rebuild it.

That extra cleanup matters because creative work rarely ends with a first spark, and the paper focused on what happens afterward.

Using AI for brainstorming

The researchers divided creative work into ideation, the stage of generating many possibilities before committing to one path.

After that comes the harder task of choosing one option, building it out, and making it fit the brief.

The team found that AI raised early-stage scores by 76% in novelty, 24% in relevance, and 97% in complexity.

Those gains make sense because abundance helps when people are still searching, but abundance can become noise during finishing.

Experts spend time editing

Years of training can harden into expertise fixation, which keeps experts reaching for familiar routines.

When AI produced images with its own logic, professionals often had to translate that output back into their practiced methods.

Screen recordings showed heavier revision work, with expert designers adding elements and editing existing ones more often before settling.

In the student experiment, experts using AI in implementation spent 57% more time and still reached similar creativity scores.

Beginners gain more help

People without deep design training kept gaining help because AI handled parts of production they had not already mastered.

Instead of defending a personal routine, they could accept suggestions, borrow structure, and keep moving toward a workable result.

Among lower-expertise students, implementation improved novelty, relevance, and complexity when AI arrived only during that later stage.

That pattern suggests AI can lower barriers for beginners even while it frustrates people who already work from strong habits.

Designers tested with experiments

The evidence came from two experiments: 192 students completed a lab poster task, and 120 professionals tackled a real advertising brief.

One test kept conditions tight enough to separate idea generation from execution, while the other moved into real professional work.

The field study also used Midjourney V6.1, a text-to-image generative AI system that creates detailed images from written prompts, allowing the authors to test whether newer models changed the basic pattern.

Professional designers still slowed during implementation, spending about 14.6 extra minutes when AI entered only at that point.

AI boosts mental stimulation

AI clearly expanded the number of ideas people tried, yet it did not trap them in endless indecision.

Most participants still carried roughly one option into the final stage, even after exploring several machine-made possibilities first.

Professionals also reported more mental stimulation during brainstorming, while feelings of overload barely moved at all.

That balance helps explain why early experimentation opened the process instead of freezing it with too many options.

Hou argued that the next improvement should happen in the interface, not only in the raw image generator.

“We would suggest that all people embrace AI in the brainstorming stage. In the implementation stage, we find that AI is still very helpful for those ordinary people, but it creates more work for expert designers,” Hou said.

That advice points toward systems that adapt to users, instead of forcing every user to adapt to the system.

Beyond graphic design

The paper’s logic extends beyond posters, because many creative jobs also move from open exploration to disciplined execution.

Writing, advertising, and product work all ask people to generate options first, then narrow them into something usable.

Whenever AI expands the search without disturbing a practiced routine, people are more likely to feel helped than interrupted.

Once the tool begins shaping the final form, the question becomes less about talent and more about control.

Limitations and future research directions

These results came from graphic design tasks, so they do not settle how musicians, filmmakers, or architects will respond.

Real projects can loop through many drafts, and the paper simplified that mess into two clear stages for comparison.

Even so, the repeated finding across students and working professionals makes the central split hard to brush aside.

Future studies will need to test whether better controls, different media, or team settings can ease the expert slowdown.

AI looked strongest when it expanded possibility and weakest when it collided with trained routines, which recasts creativity as a sequence.

Tools may work best when beginners can lean on automation and experts can decide exactly when assistance enters.

The study is published in Information Systems Research.

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