About this video

• Video Title: On-Demand Webinar - Agentic Commerce Now
• Channel: Publicis Commerce
• Speakers: Peter Breen, Jason Goldberg, Candy Arington, Julia Miller, Mike Black
• Duration: 52:42

Overview

This webinar discusses the rapid advancements and implications of "Agentic Commerce," a new paradigm in online shopping driven by AI. The speakers explore how AI agents are changing product discovery, purchasing decisions, and the overall retail landscape. They aim to cut through the hype, provide a clear understanding of the current state of agentic commerce, and offer guidance on how businesses can adapt and thrive in this evolving market.

Key takeaways

• Evolution of Shopping: The video traces the evolution of product discovery from in-store experiences to search engines, social media, and now to AI agents.
• AI as Shopper Assistants: AI agents are becoming sophisticated assistants that can understand complex needs, compare products, and even make purchases on behalf of consumers, significantly reducing friction.
• Transformative Impact: Agentic commerce has the potential to be highly transformative, impacting not just where and how we shop, but potentially who the "shopper" is by outsourcing decision-making to AI.
• New Competitive Dynamics: This shift creates new competitive landscapes, where AI agents can displace category captains and empower challenger brands by prioritizing clear benefits and values.
• Data and Content are Crucial: Brands need to optimize their product catalogs and content to be "agent-readable," "agent-selectable," and "agent-recommendable" to ensure visibility and relevance to AI.
• Trust is Paramount: For AI agents, trust signals like reviews, claims, and stock availability are more critical than traditional branding for product recommendations.
• Actionable Steps: Businesses are advised to identify friction points in their current customer journeys, ensure their content is optimized for AI interpretation, and consider their distribution and merchandising strategies in the context of agentic commerce.
• Not Just Future, but Present: Agentic commerce is not a distant concept but is already a reality, with significant user activity and evolving capabilities from major tech players.

The emergence of AI agents has shifted product discovery from traditional methods like in-store browsing and simple search engine queries to a more conversational and personalized experience. Consumers can now interact with AI agents using detailed prompts (e.g., 30 words into ChatGPT) to receive highly personalized recommendations. Furthermore, AI agents can act as subject matter experts, guiding consumers to the right products, even within a physical store by analyzing shelves via camera input. This is exemplified by the demonstration where an AI agent recommended reef-safe sunscreen for a specific vacation destination, considering factors like SPF and brand suitability. The transition is from a consumer actively searching or browsing to an AI proactively assisting or even making decisions based on a deeper understanding of the consumer's needs and context.

Agentic Commerce Management involves several key components for brands and retailers to navigate the new world of AI-driven commerce:

• Optimizing Product Catalogs: This is a fundamental building block, with the primary goal being to "win the product card" in AI recommendations. This means ensuring catalogs are rich with accurate, complete, and contextual attributes that agents can understand and use.
• Cross-Channel Signal Optimization: Agents analyze signals from various channels (direct-to-consumer sites, TikTok Shop, Amazon, Walmart, Instacart, etc.) to make recommendations. Brands must ensure consistency and quality across all these touchpoints, as agents consider pricing, fulfillment speed, reviews, and promotional patterns.
• Content Organization and Optimization: Content needs to be organized for machine interpretation while remaining optimized for human understanding. This involves creating content that can inspire not only shoppers but also the AI agents themselves.
• Understanding Agent Prioritization: Brands need to identify which channels and SKUs agentic systems are prioritizing and recommending. This influences where brands should launch and focus their efforts.
• Merchant of Record Strategy: Decisions need to be made regarding when a brand acts as the merchant of record versus when a retailer does, impacting how products are presented and sold through AI.
• Line Extension and Variant Logic: How product variations and extensions fit into the recommendation logic of AI agents needs to be considered.
• Distribution and Fulfillment Choices: These will influence value and negotiations with retailers, as agents will likely prioritize efficient and cost-effective fulfillment paths.
• Trust Signals: Agents prioritize trust signals such as reviews, claims, and third-party certifications more than traditional branding. Brands must ensure these signals are strong and readily available.

AI agents prioritize a different set of data points and signals compared to human preferences when making recommendations, with a strong emphasis on trust and factual attributes.

Here's a breakdown:

• For AI Agents (Highest Priority):

  • Trust Signals: This is paramount and considered more important than branding. This includes:
    • Reviews: High volume and positive reviews are crucial.
    • Claims: Verifiable product claims and performance data.
    • Third-Party Certifications: Official endorsements or certifications (e.g., eco-friendly, safety standards).
    • Availability: Whether the product is in stock.
    • Fulfillment Speed: How quickly the product can be shipped.
  • Attributes and Metadata: Detailed and accurate product attributes are essential for agents to understand use cases and match them to prompts. Agents will actively seek this data and may make assumptions if it's missing.
  • Pricing: Competitive and clear pricing information.
  • Cross-Channel Consistency: Ensuring data is consistent across various channels where the brand is present.

• For Human Shoppers (Traditional Preferences):

  • Brand Reputation and Aesthetics: Humans are often influenced by brand recognition, marketing, and the visual appeal of products and content.
  • Inspiration and Emotional Connection: Shoppers seek products that align with their aspirations, lifestyle, or emotional desires, often discovered through social media or curated content.
  • Convenience and Ease of Use: While also important for AI, humans value ease of purchase and familiar shopping experiences.
  • Reviews and Social Proof: Humans also consider reviews, but often weigh them alongside personal taste and brand perception.
  • Discovery: Humans may enjoy the serendipity of discovering new or unexpected items.

Comparison:

While both AI agents and humans value information like reviews and pricing, AI agents are much more data-driven and less swayed by emotional or brand-centric factors. They rely on structured, verifiable data to make objective recommendations. Humans, on the other hand, incorporate subjective elements like brand loyalty, aesthetic appeal, and emotional connection into their decision-making process. The challenge for brands is to provide data that satisfies AI's need for trust and accuracy while also appealing to the human desire for inspiration and brand connection.

The shift to agentic commerce significantly impacts existing retail media networks and advertising strategies in several key ways:

• Shift from Influencing Human Behavior to Influencing Agent Behavior: Retail media networks have historically focused on influencing human shoppers' behaviors through targeted ads and promotions. Now, they must evolve to influence the behavior of AI agents. This means understanding how agents process information and make recommendations.
• Focus on Structured Product Data and "Product Cards": As AI agents become the new gatekeepers, the "product card" (the display of a product within an AI recommendation) becomes critically important. Retail media networks will likely focus on helping brands optimize their product data and content to ensure their products are featured prominently and favorably on these cards.
• Evolving Creative Formats and Placements: The way ads are presented will need to adapt. Retail media networks may need to develop new ad formats or placements that are more easily digestible and actionable for AI agents, moving beyond just visually appealing banners.
• Increased Complexity in Attribution and Measurement: Determining which entity gets credit for a sale becomes more complex. Retail media networks will face challenges in attributing sales to their efforts when an AI agent is the primary decision-maker. The question of "who compelled the agent to recommend your product" becomes central.
• Potential for New Monetization Models: While immediate ad placements on large language models (like ChatGPT) might not be the primary monetization strategy initially, the underlying data and capabilities of retail media networks (like product catalogs and shopper data) could be leveraged by agents. This could lead to new forms of partnership or data-driven recommendations.
• Integration with Instant Checkout: As AI agents facilitate instant checkout, retail media networks that are integrated with these systems (like Walmart) can benefit by capturing more direct sales rather than just referral traffic.
• Data Requirements Will Increase: Agents will demand even richer and more structured data from brands and retailers to make informed recommendations. Retail media networks will likely play a role in facilitating the collection and structuring of this data.

In essence, retail media networks need to transition from being solely advertising platforms for humans to becoming data and optimization hubs that empower AI agents to recommend products effectively. The focus shifts from capturing human attention to ensuring data quality and trust signals that agents rely on.

Topics and Tags for Deeper Exploration:

Core Concepts:

• Topics: Agentic Commerce, AI in Retail, Future of Shopping, Digital Transformation, E-commerce Evolution, Artificial Intelligence, Machine Learning, Consumer Behavior, Retail Technology, Marketing Automation.
• Tags: AgenticCommerce, AICommerce, FutureofRetail, Ecom, DigitalShelf, AIRevolution, ShopperJourney, RetailTech, CX, Personalization, ConversationalAI.

Specific Areas of Discussion:

• Topics: Product Discovery, AI-Powered Recommendations, Frictionless Commerce, Instant Checkout, AI Agents, Prompt Engineering, Data Optimization, Catalog Management, Retail Media Networks, Attribution and Measurement, Content Strategy for AI, Competitive Landscape in AI Commerce.
• Tags: ProductDiscovery, AIRecommendations, FrictionlessShopping, InstantCheckout, ShoppingAgents, PromptBasedSearch, DataStrategy, CatalogOptimization, RetailMedia, MarketingAttribution, AIContent, BrandStrategy, ChallengerBrands.

Implications and Future Outlook:

• Topics: Disruption in Retail, Impact on Brands, Impact on Retailers, Zero-Click Future, Consumer Decision-Making, AI-Driven Purchasing, The Metaverse and Commerce, Voice Commerce, New Business Models, Strategic Planning for AI.
• Tags: RetailDisruption, BrandImpact, RetailerStrategy, ZeroClick, ConsumerTech, AIadoption, BusinessInnovation, FutureTrends, StrategicPlanning, DigitalCommerce.

Technical and Operational Aspects:

• Topics: Generative AI in Commerce, AI Visibility, SEO vs. GEO, Agentic Commerce Management Tools, Data Quality for AI, Cross-Channel Data Integration, Machine-Readable Content, Trust Signals in AI.
• Tags: GenerativeAI, AIVisibility, SEO, GEO, AgenticManagement, DataQuality, Omnichannel, ContentStrategy, TrustSignals, RetailAnalytics.