Welcome to the AI Search Era: Competing in the Age of AI Agents
Missed the live session? Watch the on-demand recording and discover how AI agents are already transforming search, customer journeys, and digital strategy.
Pi Datametrics and Lumar have teamed up to bring you the ultimate playbook for the AI search era.
Speakers
- Jon Earnshaw, Chief Product Evangelist at Pi Datametrics
- Richard Barrett, Director of SEO Professional Services at Lumar
In this webinar you’ll learn:
- How agentic search is reshaping customer journeys
- The difference between personal agents and business agents
- What Model Context Protocol (MCP) is, and why it matters
- Practical steps businesses can take to stay visible and competitive
Whether you’re responsible for digital strategy, marketing, customer experience, or innovation, you’ll leave with a clear understanding of how AI agents are changing search and what your business needs to do next.
Move beyond prompt tracking
One of the biggest questions from the webinar was how businesses should measure visibility as AI search becomes increasingly conversational.
Discover why tracking individual prompts only tells part of the story and how understanding conversation spaces can help you measure your brand’s presence across AI search.
Overview
Search is transitioning from classic query refinement to Agentic Search—where personal AI agents (like Gemini Spark) and business AI agents negotiate, narrow down choices, and make purchases on behalf of users. Hosted by Jon Earnshaw (Chief Product Evangelist, Pi Data Metrics) and Richard (Director of SEO, Lumar), this webinar explores how brands must prepare for machine interpretation, leverage Model Context Protocols (MCP), and build scalable technical foundations.
Executive Summary
As AI search evolves into agentic execution, traditional brand experience (fluffy copy and rich visuals) is compressed into raw, machine-readable attributes. To maintain market share, organizations must prepare for personal and business agents that navigate multi-turn decision-making layers. Achieving long-term visibility requires combining structured data, robust server health, multi-turn conversational mapping, and open Model Context Protocols (MCP).
Key Takeaways
The Shift from Experience to Machine Interpretation: Personal AI agents compress rich visual and wordy content into fundamental product attributes. Brands must ensure their core data, features, and value propositions are cleanly structured for machine evaluation.
The Role of Business vs. Personal Agents:
Business Agents (e.g., Ask Macy’s): Merchant-controlled agents driving efficiency, engagement, and up to $4\times$ higher customer spend.
Personal Agents (e.g., Gemini Spark): Consumer-facing assistants executing continuous, background search tasks (e.g., price tracking, scheduling, and product comparisons).
Model Context Protocols (MCP) Are the New AI Gateway: MCPs act as open-source “instructions” allowing LLMs and external agents to query databases, SaaS platforms, and website inventory securely without needing open browser windows or manual inputs.
Technical SEO Remains the Foundation for Agentic Crawling:
Preventing Crawl Errors: AI bots (like Amazon Bot) crawl aggressively; hidden server failures (500 errors) or JavaScript rendering issues immediately remove a brand from the agent layer.
Internal Linking: Precise internal linking provides LLMs with explicit context and semantic pathways to evaluate page priority.
Track Conversational Spaces, Not Static Prompts: Consumers average 4.2 turns per AI interaction. Instead of chasing isolated, one-off prompts, map and optimize content to cover entire multi-step decision paths.
