Influencing $3 billion in Black Friday sales, AI shopping agents automate both product discovery and price hunting. This ushers in an era of "self-driving shopping" that forces radical price transparency on retailers, as AI can instantly find the absolute cheapest option online for any product.

An AI agent can be configured to run persistent, daily checks on competitors' websites for pricing changes, new features, and blog posts. It can also monitor social media for mentions and deliver a summary via push notification or email only when changes occur, creating a low-noise, automated intelligence feed.

To increase deal size and escape the limitations of per-user pricing, embed AI into specific, productized use cases. This allows you to create new value-based pricing levers, such as AI credit consumption or custom AI agents, boosting average deal size.

Marketers can leverage AI browsers to automate competitive research. By opening tabs for multiple competitors, you can prompt the AI to instantly analyze and synthesize their pricing models, lead capture methods, and go-to-market strategies, replacing hours of manual work.

Amazon's "Buy For Me" feature uses AI agents to purchase products from third-party websites, including competitor Shopify stores. This strategy allows Amazon to expand its product catalog by absorbing others' inventory while simultaneously blocking its own site from rival AI crawlers, creating a powerful competitive moat.

The rise of AI agents enables a move away from traditional per-seat SaaS pricing. Instead of selling access to a tool, entrepreneurs can sell a specific, guaranteed outcome delivered by an agent (e.g., a daily brief of competitor activity), transitioning to an outcome-based revenue model.

AI-powered browsers can instantly open tabs for all your competitors and then analyze their sites based on your prompts. Ask them to compare pricing pages, identify email collection methods, or summarize go-to-market strategies to quickly gather competitive intelligence.

The next major evolution beyond solving individual use cases (like content or pricing) with discrete AI agents is orchestration. The true unlock will be linking these agents to work together as an autonomous team, passing insights and tasks between them to manage the end-to-end e-commerce process.

Snowflake moved beyond basic AI tools by building proprietary agentic models. One agent analyzes campaign data in real-time to optimize ad spend and ROI. A second 'competing agent' provides on-demand talking points for sales and marketing to use against specific competitors, solving a massive enablement challenge.