Enhancing Site Discovery with Product Recommendations, Optimization & Content: Key Takeaways from Paul Dabrowski, Shawn Lynam, Kim Brolet, and Matt Eisnor at B2B Online Atlanta 2026

At B2B Online Atlanta 2025, the panel “Enhancing Site Discovery with Product Recommendations, Optimization & Content” brought together Paul Dabrowski, Kim Brolet, Shawn Lynam, and Matt Eisnor for a practical discussion on how B2B brands can improve discovery across onsite search, AI search engines, and content experiences. The session focused on the growing importance of structured product data, machine learning, and question-based content strategies as buyers increasingly use both traditional search and generative AI tools to find products and solutions.

Key Takeaways

1. Better data is the foundation of better discovery

Across the panel, the most consistent message was that clean, structured product data determines whether search and recommendation tools can perform well. Kim Brolet described how improved PIM data and clearer PDP structures helped increase direct landings on product pages, while Shawn Lynam compared underfeeding a powerful system to giving a race car low-quality fuel. The point was clear: AI and machine learning can only help if the underlying catalog, attributes, and content are accurate and complete.

2. Recommendation engines work best when they reflect real buyer behavior

Kim Brolet explained that machine learning was already improving synonym suggestions, ranking, and product boosts based on customer behavior, even before deeper AI capabilities were added. Shawn Lynam added that recommendations on homepages, PDPs, and checkout flows became more useful once the system had more relevant inputs. The practical lesson is that recommendation quality depends less on flashy AI branding and more on whether the engine has enough context to support real buying decisions.

3. Search needs to evolve from keywords to answers

Several speakers emphasized a shift away from rigid keyword strategies toward answering customer questions directly. Shawn Lynam said his team had to move from a keyword-focused sitewide search model to an AI answers approach, while Kim Brolet noted that customers now arrive with more specific product requirements and specs. This reflects a broader trend in search intent: B2B buyers want fast, relevant answers, not just a list of matching terms.

4. AI search engines reward accessibility and authority, not just rankings

Matt Eisnor explained that tools like ChatGPT and Perplexity are unlikely to crawl catalogs often enough to stay current, which makes API exposure and structured access more important than traditional crawling alone. Shawn Lynam added that external AI systems tend to rely on high-authority sources and well-structured brand content when answering user queries. The opportunity for brands is to strengthen AI discoverability by making their data easy to retrieve, interpret, and cite.

5. Onsite search remains one of the highest-value features in B2B

Paul Dabrowski highlighted that search is the most-used feature on many B2B sites and can materially lift conversion when it works well. Matt Eisnor added that search should be optimized across the entire experience, from query understanding to retrieval and ranking, rather than treated as a single tool. The takeaway is that onsite search is no longer a utility; it is a core revenue driver that directly shapes buyer confidence and conversion rates.

6. Content strategy should anticipate buyer questions before they are asked

Shawn Lynam described his team’s “research platform” mindset, built around answering every possible customer question before the buyer needed to ask it. Kim Brolet similarly pointed to the value of using internal sources such as Salesforce records, Q&A libraries, and application engineer responses to build richer experiences. This approach aligns with modern content optimization, where the goal is to create helpful, authoritative resources that support both internal search and external discovery.

Why It Matters

This session underscored a major shift in B2B commerce: discovery is no longer limited to a site’s search bar or a keyword ranking strategy. Buyers now move across onsite search, AI assistants, and answer engines, which means brands need complete product data, flexible content structures, and discovery experiences that can adapt to intent. For leaders, the competitive advantage will come from connecting marketing, eCommerce, and technical teams around the same goal: making the right information easy to find, trust, and act on.

Actionable Insights

• Audit your product data: Check for missing attributes, inconsistent naming, and weak taxonomy before investing in new AI tools.
• Improve PDP structure: Put critical specs, differentiators, and answers near the top so search systems and buyers can find them faster.
• Test answer-based content: Rework SEO pages into question-led resources that support both onsite discovery and external AI search.
• Use existing tools better: Ask vendors what AI and machine learning features are already available before buying new platforms.