In 2026, Google AI has expanded shopping and booking features directly on its search platform, posing a significant challenge for businesses to maintain visibility and customer interaction. Most businesses observe a decline in traditional website visits as customers interact more through Google's AI agents. This shift requires understanding and adapting to how AI agents reshape the customer journey and engagement.
New Challenges in Online Customer Engagement
In 2026, Google AI introduced a new challenge as users shift to shopping and booking through AI agents rather than visiting business websites. Most businesses report a noticeable drop in traditional traffic and engagement since AI agents handle much of the purchasing journey on behalf of users. This means businesses must compete not only for clicks but also for recommendations within the new AI-driven experience. The change in search and buying behavior demands businesses update their customer engagement strategies, focusing on data optimization and visibility within AI agents. This sets the stage for a deeper look at how Google AI agents reshape the customer journey.
How Google AI Agents Change Customer Journey
Features like Universal Cart and agentic booking enable users to shop or book services directly on Google without visiting business websites. The Universal Commerce Protocol, developed since late 2025, establishes an open standard for agentic commerce, allowing AI agents and merchant systems to communicate with a common language. Users describe their needs, and AI performs tasks like product monitoring or automatic booking. This shortens the customer journey and shifts business focus from website visits to being recommended by AI agents. This development leads into understanding key differentiators in Google's AI agent model.
Key Differentiators of Google's AI Agent Model
Google’s agentic commerce model allows businesses to remain transaction owners but without control over data related to purchase intent or product discovery. This forces SEO and marketing strategies to adapt by optimizing signals AI agents rely on for recommendations, such as accurate product data, clear pricing, and detailed content. Businesses no longer compete just for clicks but for recommendations. Data complexity and lack of transparency in AI agents’ selection criteria pose significant challenges for measuring and optimizing business performance in the AI agent era.
Practical Results and Lessons for Vietnamese Businesses
Vietnamese businesses in e-commerce and local services face challenges in traditional performance measurement as customer behavior shifts due to AI agents. For instance, in local services, if an AI agent’s call goes unanswered, businesses might lose opportunities without understanding why. This new approach requires investment in organization, data standardization, and continuous operation capability. Adaptation strategies in 2026 include closely monitoring AI signals, continuous updates, and long-term collaboration to maintain competitiveness in the growing AI agent environment.
Getting Started with MADIAD in the AI Agent Era
Businesses need to develop tailored AI strategies to adapt to the evolving AI agent model, including applying AI content generation and AI reporting technologies to enhance operational efficiency and customer engagement. Deployment requires long-term collaboration for continuous updates aligned with technological progress and practical results. MADIAD emphasizes custom design and being tool-agnostic, enabling businesses to flexibly apply AI according to their unique characteristics and goals. This is a crucial step for maintaining position in a market increasingly influenced by AI agents.
Conclusion
Google AI is profoundly changing how users search and interact with businesses in 2026, creating new challenges in measuring and managing brand visibility. The agentic commerce model requires businesses to quickly adapt to how AI agents recommend products and services instead of focusing solely on website visits. Business and marketing strategies must update to fit this new reality. Follow MADIAD Lab for updates on AI trends and new management approaches for Vietnamese SMEs. Source: https://www.searchenginejournal.com/googles-i-o-demos-reveal-the-new-business-visibility-problem/576217/
Frequently asked questions
Is multi-channel AI chatbot suitable for e-commerce businesses?+
Yes, multi-channel AI chatbot supports e-commerce businesses in effectively engaging customers across multiple channels, enhancing reach and purchase support. It is custom-designed for industry specifics to optimize customer service and boost sales. However, with AI agents changing the customer journey, businesses should integrate additional AI tools for comprehensive optimization.
How long does MADIAD take to deploy multi-channel AI chatbot? What is the process?+
Deployment of multi-channel AI chatbot typically takes 4 to 6 weeks depending on business scale and requirements. The process includes needs assessment, custom design, development, training, and pilot operation. MADIAD provides long-term support for continuous updates and optimization, helping businesses adapt flexibly to market changes.
Is MADIAD’s multi-channel AI chatbot pricing different from building in-house or other agencies?+
MADIAD’s multi-channel AI chatbot pricing reflects custom design, multi-layer integrated systems, and long-term collaboration, differentiating it from in-house builds or typical agencies. MADIAD is tool-agnostic, selecting the best tools for each task, optimizing costs and long-term usage efficiency.
Which channels can multi-channel AI chatbot integrate with?+
Multi-channel AI chatbot can integrate with popular channels like Facebook, Zalo, websites, email, and other messaging platforms. Custom integration capabilities allow businesses to flexibly expand and manage channels effectively, while coordinating with AI content generation and reporting systems to enhance customer experience.
Does MADIAD provide training and updates after deploying multi-channel AI chatbot?+
Yes, MADIAD offers long-term partnership post-deployment, including operational training, continuous updates aligned with technological advances, and system optimization based on real feedback. This helps businesses maintain effectiveness and quickly adapt to market and AI changes.