How to Use Algorithms to Generate Creative Output
In the rapidly evolving world of artificial intelligence, algorithms are revolutionizing creative processes across various industries. This article explores how AI generates surprising keynote titles, crafts art supply descriptions, and enhances email marketing personalization. Drawing on insights from experts in the field, we'll delve into the fascinating intersection of algorithms and creativity.
- AI Generates Surprising Keynote Titles
- Transformer Model Crafts Art Supply Descriptions
- Algorithm Boosts Email Marketing Personalization
AI Generates Surprising Keynote Titles
At Gotham Artists, we once used an algorithm to generate keynote titles for our speaker roster, and the results honestly surprised us.
We fed an AI model about 200 past event titles, industry keywords, and even popular TED Talk phrasing patterns. But here's the twist: we trained it not just to mimic—but to deliberately break patterns and surface titles that felt familiar yet read like plot twists.
Example: instead of generating something like "Leading Through Change," it produced: "Change Doesn't Ask Permission: Leading When the Rules Break Themselves."
That one was picked up by three different event planners that month.
What worked: It wasn't just that the algorithm saved us time—it gave us permission to get creative. It helped us leap past the usual "safe" language and find phrases that made people lean in.
Transformer Model Crafts Art Supply Descriptions
At our online art-supply shop, we have built a slim transformer model trained on two carefully curated sources: thousands of artist-friendly product blurbs from leading brands, and the underlying technical sheets that list pigment codes, binders, barrel woods, light-fastness ratings, and safety notes. When a new item arrives—say a watercolour set or a graphite pencil—we send the raw spec through the model together with a style tag such as "calm & inspiring" or "playful & experimental". The model blends the figures with evocative language that speaks to painters and illustrators, stressing qualities like flow, tint strength, and archival longevity while never missing mandatory facts.
Post-generation, a regex pass checks that essential details (e.g., ASTM light-fastness grade, AP/CL safety mark, series number) are present, and a quick sentiment filter weeds out stray negatives. The workflow now turns out first-draft copy for a full catalogue refresh in minutes—roughly 80% faster than our old manual process—and A/B tests show a 10-12% lift in "Add to Basket" clicks. Crucially, every description stays on-brand and genuinely creative, letting our human editors focus on fine brushes and clever upsells rather than starting from a blank page.
Algorithm Boosts Email Marketing Personalization
I once used an algorithm to generate text for a marketing campaign. I wanted to create personalized email content for different customer segments based on their past purchasing behavior. Using a natural language processing model, I fed the algorithm with data on each segment's preferences, demographics, and previous interactions. The algorithm then generated tailored email drafts with different tones and calls-to-action. The result was a set of emails that resonated more with each audience.
The campaign saw a 20% increase in open rates and a 15% boost in conversions compared to our previous generic emails. What I learned from this experience is how effective algorithms can be at scaling creativity in marketing while still maintaining personalization. It gave me a more efficient way to generate meaningful content that felt custom-made for each recipient.
