"Cookie cutter" has long been shorthand for the difference between something crafted and something manufactured. The phrase suggests uniformity—efficient, repeatable, and predictable—but lacking the quirks and intentional decisions that make something feel genuinely human.
It's a cruel fate for anything involving cookies.
After all, there's nothing inherently wrong with a perfectly consistent, store-bought chocolate chip cookie. But chances are it still doesn't compare to your grandmother's recipe. Her cookies weren't memorable because they were perfectly uniform. They were memorable because they carried a human touch—small imperfections, personal choices, and a little bit of love.
Ironically, even if Grandma used a cookie cutter, her cookies were the opposite of "cookie cutter."
The comparison feels increasingly relevant in the age of generative AI and the materials created to support critical field training programs. As more organizations rely on the same models, prompts, templates, and workflows, we're beginning to see the same patterns emerge across training content, videos, and imagery. This is feeding into a lack of distinctiveness, which endangers memorability and impacts the quality of training engagement.
The first generation of AI-generated content was impressive simply because it existed. Today, that's no longer enough.
Spend some time scrolling through LinkedIn, marketing emails, presentation decks, or even training videos, and certain patterns begin to emerge. The polished digital avatars. The perfectly symmetrical faces. The overly animated hand gestures. Mouth movements that are just slightly out of sync. Voices that sound natural at first but carry an unmistakable cadence—carefully punctuated, evenly paced, and just a little too perfect.
The same thing happens with illustrations, icons, and infographics. After a while, you develop an eye for it. Not because the work is bad, but because it all begins to share the same visual vocabulary.
That isn't a coincidence.
Many organizations are using the same handful of AI platforms, drawing from similar libraries of avatars, templates, and generative models. When thousands of people ask similar questions of the same systems, it's no surprise that the answers begin to converge.
The result is content that's reasonably competent—but increasingly difficult to distinguish from everyone else's.
All of this is a consequence of how the technology works.
Generative models don't create ideas the way people do; they predict them. Every image, sentence, or animation is assembled from statistical patterns learned across enormous datasets. The output is designed to be plausible, resembling what has come before.
That makes AI remarkably effective at producing a strong first draft or establishing a visual baseline. In fact, we've experienced this firsthand. Clients occasionally send examples of work they'd like to emulate, only for us to discover that a nearly identical version can be recreated in minutes using an AI tool. The resemblance isn't because anyone copied the original; it's because both pieces are drawing from the same underlying patterns.
AI excels at finding the average. Human creativity often comes from intentionally departing from it.
Perhaps the biggest risk is AI’s consistent tendency toward that average work.
When everyone relies on similar prompts, similar models, and similar reference material, creative diversity naturally begins to shrink. Messages sound alike. Training videos follow familiar patterns. Infographics begin to share the same layouts and illustration styles.
Ironically, the more organizations adopt identical tools in pursuit of differentiation, the more similar their content seems, and the more forgettable interactions with it become.
Standing out becomes harder precisely because everyone is using the same shortcut.
Another challenge receives less attention.
Every generative model depends on a universe of human-created work. Designers, illustrators, photographers, writers, animators, and subject matter experts have spent years developing their craft and refining distinctive styles.
As AI-generated content becomes more prevalent, questions naturally arise about where those source materials came from, how creators are credited, and whether they're compensated. Those conversations continue to evolve.
There's also a practical consideration for trainers. In highly specialized fields like healthcare and life sciences, accuracy matters just as much as aesthetics. AI-generated medical illustrations or anatomical imagery may appear convincing while introducing subtle inaccuracies that only an expert would recognize. A polished visual isn't necessarily a trustworthy one, and delivering accurate material is key to both the credibility of your training program and the throughput of information in the field.
To ensure success, human review, editing, and enhancement remain essential.
The answer is to use AI intentionally, as a tool to augment capabilities.
AI is exceptional at accelerating repetitive tasks, exploring concepts, organizing information, and producing first drafts. It lowers barriers for people who may not have formal design or production experience and helps creative professionals spend less time on routine execution.
But the final layer still belongs to people.
AI may make asset creation look easy, but great output that stands above the mundane and achieves learning success can’t always be "easy." A recent project at CLD involved a trainer who tried to make a video using AI tools, but the output missed the mark. We stepped in to help. With the right combination of tools (including non-AI tools and talented people to use them) we delivered the kind of quality we demand from ourselves, and our clients have come to depend on.
Our role in coming alongside our clients has evolved to include the emerging discipline of AI creative producers. Experienced creators, skilled in prompt engineering and life sciences, are able to craft more specific, individualized results that serve the needs of field team trainers.
The strongest creative work reflects judgment, personality, lived experience, and an understanding of the audience. These qualities don't emerge from prediction alone. They emerge through iteration, experimentation, and the willingness to make unexpected choices.
The creative muscle still needs exercise.
The organizations that will benefit most from AI won't be the ones that fully automate. They'll be the ones that use AI to move faster while preserving the distinctly human perspective that makes their work recognizable, memorable, and authentic.
Key Takeaways
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