How AI Agents Are Transforming Shopping: Appier Proposes Dual Marketing Strategy for the AI Agent Commerce Era

Appier Group Inc. (Appier, headquarters: Minato-ku, Tokyo; CEO: Chih-Han Yu; Tokyo Prime: 4180, hereinafter "Appier") proposes a dual marketing strategy for the era of AI agent commerce. As AI evolves from answering questions to executing autonomous tasks, it is increasingly playing an active role across the entire consumer purchasing journey—from product discovery and recommendation comparison to completing purchases. For retailers, the challenge is no longer merely capturing consumer attention, but ensuring their brand is recognized, correctly understood, and ultimately chosen as AI agents gain influence over purchase decisions.

As a native Agentic AI as a Service (AaaS) company, Appier believes AI agent commerce is fundamentally transforming how consumers discover and purchase products. To remain competitive, businesses must combine AI agents, omnichannel customer data, and real-time decision-making to gain deeper insights into customer intent and achieve sustainable growth in the next generation of retail.

From Search to AI-Assisted Purchasing: The Rise of Hybrid Customer Journeys AI-driven search and autonomous AI agents are redefining the very starting point of product discovery. Previously, consumers would begin with a web search, proceed through ads and brand websites, and eventually make a purchase. Brands could manage traffic, first-party customer data, and customer relationships directly. However, consumers are now shifting from asking "what should I buy?" to asking AI "how can I accomplish this task?"

Increasingly, AI systems generate personalized recommendations based on individual circumstances, budget, and preferences, then redirect consumers back to brand websites or physical stores to complete the purchase. As product discovery, comparison, and purchase stages become distributed across multiple touchpoints, brands must ensure they remain visible, understandable, and selectable at every stage of the customer journey.

According to McKinsey, 50% of consumers already use AI search during shopping, and 44% cite AI as a primary source of product information—surpassing traditional search engines (31%). Additionally, Morgan Stanley Research estimates that AI agent-led commerce currently accounts for about 1% of retail transactions in 2026, but could grow to 10–20% within the next five years.

As AI agents take on more roles in product comparison and purchase decisions, brands risk losing first-party behavioral data and future retargeting opportunities if consumers no longer return to their owned media. To prepare, brands must strengthen their first-party data assets and customer intent intelligence, ensuring AI agents can accurately understand their products, services, and brand values.

Marketing Enters the Era of "Dual Strategy" As AI agent commerce creates hybrid customer experiences involving both humans and AI agents, Appier recommends retailers adopt a dual marketing strategy. Brands must continue investing in storytelling, compelling creatives, and memorable brand experiences for human consumers, while simultaneously optimizing structured, AI-readable content—such as product metadata, FAQs, specifications, inventory data, reviews, and conversion signals—so AI agents can accurately understand, evaluate, and recommend their products.

As marketing shifts from the "attention economy" to the "intent economy," success hinges on optimizing content for both humans and AI agents. With the purchase cycle shortening from the traditional 7–14 days to just 1–3 hours, brands must leverage first-party data in real time to connect customer insights to decisions before purchase intent fades.

Personalization, LTV, and Data Integration Remain Key Retail Challenges Retail marketers continue to face three major challenges: delivering large-scale personalized experiences, increasing customer lifetime value (LTV) through repeat purchases, and integrating online and offline customer data.

Although many companies have adopted CRM or CDP solutions, customer data remains fragmented across websites, mobile apps, social media, POS systems, e-commerce platforms, and physical stores—limiting real-time personalization.

Appier’s autonomous AI platform addresses this by providing an integrated data foundation that enables brands to own, utilize, and continuously extract value from their customer data. Built on a unified data infrastructure spanning Customer Data Platforms, Marketing Automation Platforms, and Conversational AI Platforms, Appier’s AI agents integrate customer information into a single decision engine, enabling seamless audience analysis, journey orchestration, budget planning, creative generation, and A/B test optimization within one workflow.

Furthermore, the "Data Quality Booster" enhances data quality by transforming fragmented information into standardized, business-friendly insights, reducing data inconsistencies and the risk of AI hallucinations (false information generation). This empowers marketers to build audiences, run campaigns, and design customer journeys using natural language—without heavy reliance on IT support.

The Value of Autonomous AI: Learning, Remembering, and Acting in Real Time Enterprise-grade autonomous AI requires three core capabilities: real-time learning, real-time memory, and real-time action. By continuously understanding customer intent and maintaining a shared understanding of customer profiles, sales records, campaign history, and brand knowledge, AI can proactively recommend the best next marketing actions—enabling brands to deliver large-scale personalized engagement.

In personalized marketing, AI combines off-site behavioral signals, past campaign performance, brand guidelines, and channel-specific formats to identify high-intent customers and automatically generate personalized messaging, creative assets, and customer journeys. Additionally, to maximize customer lifetime value, conversational AI shopping assistants can deeply understand customer needs through dialogue and recommend optimal products.