An Executive Voices Blog by Simran Bhatia, Brand Licensing Technologist

For years, brand-licensed foods meant familiar tastes wrapped in beloved logos—fun, but fleeting. Now, generative AI is turning flavor into a storytelling and brand-building tool. Instead of being an afterthought, flavor itself can become part of the narrative arc.

By “brand-licensed foods,” we mean IP partnerships—not restaurant permits or food-safety licenses. A recent example is the Pokémon × Oreo collaboration. Fans weren’t just buying cookies; they were buying into the story of “catching them all,” with collectible embossments that carried nostalgia and encouraged play. What generative AI adds is the ability to scale this kind of approach. Instead of starting with what snack fits the brand, teams can now begin with the emotions at the heart of an IP—such as hope, awe, or nostalgia—and translate those concepts into taste, texture, and aroma.

AI analyzes ingredient networks and sensory language to propose on-brand flavor families, predict which options will resonate by region and demographic, and flag what’s feasible at scale. The result is a faster path from idea to shelf and a line that refreshes naturally as the story world evolves.

This matters because memory is emotional. When a flavor reliably cues a feeling—like joy or comfort—people store that pairing and reach for it again. In licensing, an emotion-first brief moves a product from “logo nearby” to “part of the narrative.” The taste no longer sits beside the story; it belongs to a character moment, a season arc, or even a live event. For instance, a limited-edition Stranger Things Upside-Down Sundae could shift from light vanilla to dark fudge layers, echoing the narrative’s turning point. Or a Super Bowl Victory Blend could layer spice and crunch to match the thrill of the game. That’s how recall turns into loyalty.

The idea isn’t theoretical. Early experiments like “Romance Bread” in Japan showed how themes can shape food experiences. In that case, the bread’s heart-shaped packaging, soft texture, and lightly sweet flavor were designed to evoke intimacy and tenderness. This proved that both flavor and presentation can carry emotional cues. Today, food-tech tools do this at product speed: they take in scripts and mood boards, character notes, historic recipes, and sales or tasting data, then suggest promising directions—along with who is likely to enjoy them, expected cost, and any allergen or supply limits.

However, it’s important that people still make the calls. Think of AI as the palette; chefs and scientists ultimately compose the dish.

In practice, teams start with three simple “emotion briefs.” A hero’s-journey spice set might open bright and citrusy for the call to adventure, deepen into smoky spice for the struggle, and finish with an umami-rich blend for the return. A nostalgia dessert line might lean into cereal-milk softness, browned-butter warmth, and a gentle crunch that feels like home. Because the logic is narrative—not gimmick—the line refreshes with each episode, season, or event and travels easily across channels: venue LTOs, QSR dustings and dips, grocery freezer pints and seasoning sticks, and DTC tasting flights.

The workflow can stay simple and clear. Rights holders share the story beats and target audiences but this model works beyond narrative-heavy entertainment. Sports IPs could launch “Victory Citrus Coolers” tied to tournament wins while fashion IPs could extend into food with something like the “Dior Elegance Éclair.” Even corporate mascots or seasonal promotions can adapt to this framework. The food-tech partner translates briefs into flavors and formats factories can run. Development sprints then produce samples that pass safety and cost checks.

Small pilots follow—a couple of weekends in a flagship store, a theater week, one retail partner, or a short DTC drop. Feedback loops are lightweight, like a two-question taste poll, a QR that unlocks a bonus flavor, or a quick vote on what returns next month. Keep what wins on repeat purchase and retire the rest before folding the learning into the next drop.

Good guardrails keep everyone safe and on brand. Use clear, descriptive, story-linked copy (like “inspired by the reunion scene” or “crafted for the training montage”) rather than suggesting moods or health effects. Treat AI suggestions like any R&D input—everything still goes through QA, labeling, allergen, and recall protocols.

Localize with care. Emotions are universal, but preferred levels of heat, sweetness, and texture are not. Tune variants by market without breaking the brief. In licenses, require basic AI disclosure and development logs, clarify who owns the data and outputs (including any emotion-to-flavor taxonomies), set simple sensory acceptance criteria (minimum liking scores in named demos and an “on-brand” check), and add change control if a model update shifts a formula.

Measurement should go beyond sell-through. Track blinded liking by target audience, repeat across “chapters,” and attach rates to companion products or experiences as well as buyer extension requests. Use retail media and loyalty data to decide which “chapters” land in which doors. Over time, those metrics become an asset for both licensor and licensee—a practical map of which emotions convert best, by format and region.

A pilot should fit inside one quarter. Spend two weeks agreeing on emotion briefs and audiences. Use the next month for AI-guided formulation and safety screens. Run a short pilot window, capture a handful of comparable metrics, and then close with a story-plus-data readout and a refresh calendar. Then, repeat the process. Each cycle lowers risk, sharpens fit, and builds a repeatable playbook.

What comes next is discipline, not hype. Brands will ask for transparent pipelines (who used what, when), simple localization playbooks (one emotional brief, tuned per region), and KPIs that buyers trust.

Personalization will grow carefully—from segment-based assortments guided by taste quizzes to small-batch or on-demand runs where operations allow. Sustainability will move upstream so models weigh predicted liking alongside sourcing, packaging, and waste.

With those pieces in place, emotional AI becomes a practical tool for food licensing—co-creating flavors that carry narrative feeling, extend a world fans love, and give retailers a clear, refreshable reason to reset.

Simran Bhatia is a Brand Licensing Technologist at a leading apparel and accessories company. She is a forward-thinking brand strategist who blends AI, licensing, and market insights to drive innovation. Working closely with cross-functional teams, she builds smart tools that simplify workflows and support better decision-making. Her focus includes optimizing brand partnerships, leading data-driven negotiations, strengthening licensor relationships, and tracking consumer and cultural trends to uncover new opportunities and keep brands ahead of the curve.