AI is changing the mathematics of marketing. AI agents are taking on a larger role in how consumers discover, evaluate, and purchase products, and by 2030, discovery will become more algorithm-driven than not. This major and fast-moving shift means the familiar rules of brand building, loyalty, and shelf strategy are being rewritten.
In the second webinar of Circana's AI, Measured series, Circana’s Ananda Roy, lead advisor for EMEA, and Sally Lyons Wyatt, global chief advisor, examined how AI is reshaping many facets of retail and CPG. They shared 10 formulas that connect AI investment to measurable return, which is crucial when hard numbers matter.
The Path to Purchase Is Being Redistributed
Today’s shopping journeys have different arcs. Consumers recognize a need and browse websites, apps, social media, and stores. Then they compare options, make a choice, and complete the transaction themselves, on their screen or at the physical store.
In this emerging journey, work increasingly shifts toward AI agents. The consumer sets the intent through need, budget, and preferences. Next, an agent scans the market and evaluates options in real time, identifies the best fit against the consumer's instructions, completes purchases autonomously, learns from results over time, and shapes future repurchase decisions.
One critical change is the redistribution of responsibility across the entire journey. The agent becomes a central audience for brands and retailers, alongside the consumer it represents. In this environment, business leaders should work to ensure agents have the information they need to find, understand, and recommend products.
Circana's 10 Formulas for Turning AI Investment into ROI
Each formula addresses a specific pinch point on this new path to purchase, where targeted intervention is required.
1. Desirable > Shoppable
Being shoppable is table stakes. It means being visible and included in recommendations, which requires consistent listings, standardized product data across retailers, and correct tagging. Being desirable and trusted is what moves a brand up the algorithmic scale. It is the difference between being found on the AI shelf and winning on it.
Recommended action: Drive reviews and endorsements that reinforce key claims, strengthen proof points AI can cite, and make social commerce signals searchable and tangible.
2. Emotional Connection > Transaction
Much of the AI conversation focuses on the transaction. Brand builders know that the moment of truth, derived from deep human insight, can sharpen AI ranking and prevent commoditization on attributes like brand name, pack size, and price.
Recommended action: Translate brand emotion, identity, and sentiment into machine-readable data. Build hero SKUs with high availability, trusted ratings, and endorsed claims to serve as the category entry point into your portfolio.
3. In-Store Brand Experience > Frictionless Commerce
The prevailing narrative favors frictionless commerce. The counterintuitive reality is that in-store brand experience will become more important, not less. Consumers want shopping to be fun, experiential, and engaging. The risk of programmatic buying is clinical convenience.
Recommended action: Invest in experiential merchandising, retail media, digital signage, in-store guidance, and immersive technologies. For low-involvement categories, this justifies presence. For high-involvement categories, it justifies a premium from AI discovery through to the store.
4. Agent Recommendations > Shopping Habit
Big brands have long relied on three habits to sustain loyalty:
Routine replenishment: Repeat purchases made without comparison, which makes trading consumers up difficult.
The legacy effect: The brand becomes the default category choice.
The mental availability shortcut: "It worked before, so I'll buy it again."
AI agent recommendations break these habits, so brands must continuously earn their recommendation status.
Recommended action: Control the attributes agents scan, evaluate, rank, and select on. Link reviews, certifications, and expert endorsements to key claims.
5. Value > Price
AI exhibits a comparison bias toward target price points. Brands risk being underrated for the equity they deliver.
Recommended action: Hard-code sources of value beyond price and demonstrate that price-pack architecture delivers real value. Create obvious winner SKUs. Note that retailers approach value differently. They embed margin into ranking logic, optimize for basket value, and guide trade-up paths.
6. Challenger = Established
When AI agents return only 2 to 4 recommendations, control over discovery shifts from brands to algorithms. For the first time, an AI-tuned, digital-shelf-optimized challenger brand can compete effectively with established players.
Visibility narrows further when retail media shapes AI recommendations. In addition, fewer recommendations limit visibility; if paid search returns 10 to 20 options, agents return two to four.
In this market, established brands can no longer rely on salience and top-of-mind awareness alone.
Recommended action: Compete for algorithmic visibility by optimizing digital shelf presence, strengthening structured product content, and using retail media to influence AI-driven recommendations.
7. Trust = Penetration
AI adoption remains relatively low and is higher in the U.S. than in Europe and Asia Pacific. Circana's survey found that only 27% of consumers are completely confident allowing an AI agent to automatically find, compare, and purchase products. Another 38% are uncomfortable, and 36% are slightly uncomfortable. Their concerns include:
Not wanting AI to be the decision-maker
Privacy and security risks
AI evolving too fast
Mistrust of the information provided
Larger brands and retailers hold a native advantage in this environment. As adoption grows, banked trust becomes a lever for penetration.
Recommended action: Strengthen trust signals by ensuring accurate information, reducing risk, and using brand credibility to increase consumers’ confidence in AI-assisted purchasing.
8. Conversion > Choice
Consumers value choice, but agents prioritize conversion. Two insights stand out:
AI deprioritizes products with inconsistent availability. Recommending out-of-stock items makes the agent look ineffective.
AI favors best-choice options. Too many similar SKUs confuse the agent.
Recommended action: Win digital shelf distribution, stay in stock, simplify assortment, and enforce supply chain discipline. Retailer private labels are often already optimized for conversion.
9. Algorithmic Loyalty > Repurchase
Brands have invested decades in emotional loyalty. In programmatic shopping, repurchase increasingly depends on the algorithm. Agents optimize for holistic value rather than brand attachment. Simple, structured, machine-readable products are prioritized, and immediately available rewards drive repurchase. Losing brand visibility means losing loyalty.
Agents are also built differently by retailer. Amazon's agents value price per unit, convenience, shipping, and returns policy. Many premium retailers prioritize availability, equity, and performance.
Recommended action: Tune how your products appear against each retailer's agent priorities. Premium brands must pursue both algorithmic and analog loyalty and maintain emotional equity while learning what agents value.
10. Personalization > Fragmentation
The funnel keeps narrowing:
Channel | Typical SKUs Presented |
Supermarket | About 20,000 |
Online storefront | About 200 |
Personalized offers | About 20 |
AI agent ranking | 2–4 |
Recommended action: Optimize the digital portfolio, delist long-tail items with retailers on an ongoing basis, and lead with highly rated, broadly available hero SKUs. Use loyalty data to personalize the right SKU. A shopper who pays more for performance should not be served value-pack promotions.
Conversion is the ultimate metric. Solving these 10 formulas will separate category leaders from the rest in an AI-guided ecosystem.
Watch the full webinar to learn more.
