Reinventing ABM for the AI Search Era

Rebecca Colwell, SVP of Marketing at Yext, explains why AI search adoption is surging and how it reshapes visibility. She shares research on usage and click behavior. She outlines three strategic shifts for marketers and shows how trust and accurate brand facts drive citations.

AI search is transforming how B2B marketers build visibility and trust. Rebecca Colwell, SVP of Marketing at Yext, explains how large language models (LLMs) are changing traditional SEO by replacing search links with direct answers. As AI systems reshape discovery, marketers must focus on data accuracy and brand trust to earn citations that determine whether their companies appear in results.

“If you’re not visible in that first moment, you miss the entire opportunity. The goal isn’t just to rank for a keyword, it’s to be cited and trusted by the model so you appear in the answer.” – Rebecca Colwell

The interactive podbook below contains:

Rebecca Colwell’s introduction video
Full Episode video, audio, transcript, and article
9 Q&A Videos + 1 Q&A Article
1 YouTube Short Post
1 Episode Highlights Video Post

Article: “Reinventing ABM for the AI Search Era”

This article is based on an interview with Rebecca Colwell, SVP of Marketing at Yext

The Acceleration of AI Search

Rebecca explains that the rise of AI search has happened faster than any previous digital transformation. “The pace of acceleration in the adoption of AI is unlike anything we’ve seen before in marketing,” she says. ChatGPT reached 100 million users in just two months, a milestone TikTok took nine months to get. This rapid adoption reflects how buyers now expect immediate, synthesized answers instead of browsing multiple links.

For marketers, this shift means visibility strategies must adapt. Google’s dominance is being challenged by new AI-driven models that process and deliver information differently. Instead of ranking for keywords, marketers need to understand how their brands are represented within these AI-generated answers. The companies that adapt early will define the standards for visibility in this new landscape.

Why Google Can’t Just Flip a Switch

Rebecca highlights a key reason for Google’s cautious transition to AI search. “The challenge is that when you search on Google, you don’t get an answer. You get ten blue links,” she says. AI-driven platforms give users what they want instantly, but that approach conflicts with Google’s ad-based revenue model. AI overviews have been shown to reduce click-through rates by nearly half, which affects the core business of paid search.

For marketers, this presents both a challenge and an opportunity. While paid search remains valuable for high-intent keywords, organic visibility now depends on being referenced within AI-generated answers. Companies that build authoritative, trusted data sources will gain an advantage as the search landscape transitions from advertising-driven visibility to answer-driven relevance.

From SEO to Answer Engine Optimization

Rebecca introduces a new discipline that reflects this change. “The goal isn’t just to rank for a keyword, it’s to be cited and trusted by the model so you appear in the answer.” Visibility in the AI era depends on data consistency, credibility, and technical structure. Brands must publish information that LLMs can read and interpret accurately across multiple platforms such as ChatGPT, Perplexity, and Claude.

This evolution from search engine optimization to answer engine optimization means success is measured not by clicks but by citations. Trustworthiness and clarity replace keyword density as key metrics. Marketers must collaborate with technical teams to ensure data accuracy and structure. Being cited by AI models is now an essential measure of credibility and influence.

Data as a Strategic Asset

Rebecca emphasizes that visibility now depends on how brands manage and structure their data. “Measure, publish to trusted sources, and structure your data to remain accurate across those sources.” She explains that LLMs rely on structured, fresh, and interconnected facts to validate which answers to present. Outdated or inconsistent data can remove a brand from consideration entirely.

Marketers need to treat data as part of their core brand strategy. Maintaining accuracy across directories, websites, and listings strengthens model confidence. Rebecca also reminds marketers that “LLMs don’t want to hallucinate. They want accurate, consistent information.” By creating data systems that align with AI expectations, organizations can ensure their brands remain visible and credible across emerging search ecosystems.

Conclusion

The AI search era rewards precision, trust, and structure. Rebecca Colwell’s framework of measure, publish, and structure, provides a practical roadmap for marketers who want to remain visible in a world where answers replace ads. Brands that align their data strategy with AI expectations will not only sustain visibility but also lead the new era of search-driven growth.

ELEVATING CONTENT MARKETING STANDARDS RAISES A CRUCIAL QUESTION:
Why shouldn’t the very act of creating thought leadership content also spark new conversations with in-pipeline deals, top prospects, and high-priority customers for revenue expansion? Or serve as the cornerstone for learning and applying voice-of-customer strategies? That’s the power of ABM Podcasting—engaging B2B buyers and customers in meaningful conversations that uncover essential go-to-market insights, support long sales cycles, reinforce your market perspective, build peer-level trust, and dismantle objections that kill deals.

Steven MacDonald

Steven MacDonald is the founder of Content Strategies, CEO of a MarTech SaaS Company, fractional CMO consulting with leading B2B companies and former Director of Strategy and Client Service at top ten digital marketing agencies.

Follow Steve on LinkedIn.

Steven MacDonald

Follow Steve on LinkedIn.