Internet Brands has already made meaningful progress on AI. It’s worth acknowledging what’s been shipped before discussing what might come next.
Health: Medscape AI serves 13 million clinicians globally with a citation-backed generative AI assistant. WebMD Ignite’s Pulse platform applies ML to healthcare marketing intelligence. HealthInteractive provides conversational patient education.
Dental: Henry Schein ONE’s partnership with AWS has produced Image Verify (real-time X-ray quality assessment), Voice Notes (ambient clinical documentation), Detect AI (diagnostic support via VideaHealth), and an MCP layer enabling third-party AI agent integration. This is arguably the most advanced agentic infrastructure in the portfolio.
Legal: Martindale-Nolo launched AI-enhanced lead generation in January 2024. The Authority Network, launched April 2026, unifies six legal properties for AI-driven search visibility. The team has published research documenting the shift from “search and click” to “ask and chat” in legal consumer behavior.
Automotive & Travel: No public AI initiatives were identified for CarsDirect or Fodor’s.
The pattern is clear: Health and Dental are well ahead, Legal is in an active transition, and Automotive and Travel are open ground. The company also appears to be standing up a centralized AI team — with engineering, data science, operations, and product roles all being hired into what job postings describe as a “Central AI team” with a “Tiger Team” for rapid deployment.
The AI work shipped so far is strong, but it’s weighted toward professional tools (Medscape AI for clinicians, Dentrix for dental practices) and B2B operations (lead gen, marketing intelligence, search optimization). The consumer-facing experience — what the 250 million monthly visitors actually encounter — has been less affected.
This is the vertical with the most immediate opportunity. Martindale-Avvo’s own research documents that consumers are shifting to conversational, AI-mediated legal search. But the consumer experience on Avvo and Martindale today is still traditional search-and-filter. There’s no conversational intake, no AI-assisted matching, no guided case preparation.
A conversational system that understands a user’s legal situation and matches them to the right attorney — by practice area, jurisdiction, case complexity, and fit — would bring the product in line with the consumer behavior the company is already measuring. This is the product I’m currently building independently, so I have direct experience with both the problem and the technical approach.
CarsDirect’s consumer flow would benefit from the same “ask and chat” pattern. A conversational agent that understands a buyer’s needs, budget, lifestyle, and trade-in situation — then surfaces relevant inventory with pricing context — would reduce decision friction and improve lead quality. This would extend what’s already working in Health (Medscape AI as a conversational expert) into a domain where IB has deep data but no AI-driven consumer interaction yet.
Henry Schein ONE’s MCP layer is an interesting precedent. If agent orchestration, data pipeline patterns, and safety/compliance frameworks can be extracted from what’s been built in Health and Dental, they could accelerate buildout in Legal and Automotive significantly. The alternative — building each vertical’s AI stack independently — is slower, more expensive, and harder to maintain.
The engineering team’s job postings reference LangGraph, Claude Code, MCP, vector databases (Pinecone, Qdrant), and a Python/Node.js/React/Next.js stack. This is the same toolchain I work with daily. I mention this because a product leader for an AI team should understand the tools well enough to make sound architectural tradeoffs with engineering — not just manage the roadmap from a distance.
The temptation in a role like this is to arrive with a grand plan. I think the better approach is to learn first, then build.
A note on approach: I’ve found that the best way to build credibility in a new organization is to ship something useful quickly, measure it honestly, and let the results inform the next step. Strategy documents matter, but shipped products matter more.