The research phase of your buyer’s journey has changed. We’ve moved from a list of links on Google to a network of answer engines and AI-generated overviews. These systems summarize, compare, recommend and shape decisions for the user before they ever reach your website.
As buyer influence moves into new channels, B2B demand generation programs are less about early email capture and more about educating and influencing perception early in the buyer journey. Here’s how these shifts change the reality (and strategy) for demand generation in 2026.
From Content Engine to Expertise Translator
Demand generation teams are shifting away from producing volume toward creating clarity and authority, a shift that aligns with Noble Studios’ B2B strategy guided by a business-to-people perspective, where understanding real human behavior is foundational to sustainable growth.
Instead of planning solely around keywords, planning increasingly centers on intent and the entity relationships AI systems use to connect concepts. Understanding how structure, entities and intent work together is now part of demand gen’s core skill set.
At Noble Studios, our Head of AI Strategy, Tom Duffy, works closely with our Associate Director of SEO, Amanda Tietjen, and the broader SEO team to examine how AI systems process information rather than focusing solely on surface-level behavior.
That approach has helped us adapt faster and strengthen our AI-led search strategy. For several clients, this has translated into measurable lifts in conversion rates from LLM-driven traffic, in some cases up to a 48 percent increase.
As SEO teams make this shift, several steps tend to have the greatest impact:
- Audit content for AI readiness by ensuring clear headings, structured markup and schema that accurately describe what each page represents
- Rebuild long-form assets such as eBooks, webinars and blogs into concise, openly accessible formats supported by proprietary examples or data
- Deepen understanding of ICP needs using social listening, on-site search data and chatbot transcripts to identify real question patterns
- Coordinate closely with Product, Sales and subject matter experts who hold the knowledge that becomes your public-facing content
In this model, demand gen acts less as a content factory and more as a translator of internal expertise into structured information AI systems can reliably use.
Gated eBooks Don’t Earn Visibility in an AI-Led Journey
Gated content has always been a tradeoff. Sure, you capture an email for your nurture program or increase your contact engagement score, but you lose organic reach because Google can’t crawl a form-protected asset. With a well-optimized landing page, you might still be able to capture that interest through an organic ranking.
Like traditional search engine crawlers, large language models (LLMs) can read PDFs, but not if they are behind a form. AI assistants are also far less likely to surface a landing page with a gated PDF asset as HTML is easier for LLMs to chunk, attribute and quote reliably. Users don’t want to download files in an AI workflow anyway. They want direct, specific answers and quick comparisons to help them research and shortlist more quickly.
Gated content still has a place in enablement and nurturing, but it’s no longer the front door for awareness. Your best content has to be openly accessible if you want to be included in an AI-generated answer. Leverage:
- Expert Q&A written in concise, citation-ready “chunks.”
- Topic clusters built around your ideal clients’ biggest questions
- FAQ pages, deep dive explainers and clear POVs that act like reference material, not marketing copy
- Unique data points from proprietary research, benchmarks or simple original calculations
The role of TOFU thus shifts from email capture to expertise and presence. Most gated downloads were never true buying signals anyway, but rather a low-commitment action that often reflects information gathering rather than intent. Ungating improves MQL quality because when someone raises their hand without a forced exchange, it signals genuine interest and readiness to engage.
Keyword-Driven SEO Blogs Don’t Serve AI-Led Research
Search has moved from exact-match keywords toward semantic understanding and intent. As a result, content performs best when it is organized into modular, well-structured chunks:
- Shorter, denser passages built for quoting
- Schema-backed definitions
- Clear “what/why/when/how” answers
- Problem/solution patterns
- Pros and cons
- Straightforward comparisons
LLMs also place greater weight on recency than traditional search engines. Regular updates and accurate byline dates signal content freshness and reinforce reliability.
Measurement Changes from Clicks to Share of Mention, Sentiment and Accuracy
SERP features such as People Also Ask boxes, featured snippets, and knowledge panels have increased zero-click behavior. While clicks, CTR, and rankings are still relevant, they tell only part of the story.
Understanding how to measure success through share of mention, sentiment and accuracy addresses concerns about new attribution models. Track metrics like “share of model,” which reflects how often your brand appears in AI-generated answers during the research phase. Enterprise SEO tools like BrightEdge, Conductor and SEMrush can help with this analysis. Equally important, monitor the sentiment and the accuracy of how answer engines represent your brand.
Where This Leaves B2B Demand Gen Marketers
AI rewards brands that make their expertise easy to access, understand and quote. The goal of demand generation is no longer to maximize downloads or chase raw traffic. It is about being present when buyers form their understanding of the problem space and available options.
When clarity and accessibility are built into content from the start, AI assistants can carry that thinking into the earliest stages of research. That is where durable advantage begins.
If you want to better understand how AI-led search is already affecting your demand generation performance, contact Noble Studios to start the conversation.
