3 AI Search Limits Every B2B SaaS Marketer Must Know

A recent BrightEdge study reveals a significant shift, with 68% of brands across industries actively changing their search strategies to adapt to the rise of Generative Engine Optimization (GEO). Since the debut of ChatGPT in late 2022, this trend has become impossible to ignore. However, many B2B marketing leaders are still figuring out the full scope of what GEO can achieve for their brands and, just as importantly, where its shortcomings lie. While AI search, whether called GEO or LLMO, is undeniably a critical component of a modern organic strategy, over-reliance on it creates vulnerabilities that savvy competitors can exploit. Here are three specific limitations of AI search in the B2B marketing world and practical ways to address them within your overall approach.

AI search struggles to build awareness for emerging verticals and solutions.

Traditional search marketing, including both SEO and PPC, operates on an intent-based model. This means it depends on a foundation of pre-existing user awareness and is not designed to introduce people to entirely new products, verticals, or solutions. AI search inherits this same limitation but adds another complicating layer: it is generally slower to index new content because it often relies on traditional search engines to do that work first. Consequently, it takes even longer for information about innovative products and solutions to appear in AI-generated results.

To counteract this, marketers should employ a Trojan horse strategy. This involves connecting your new product or service to an existing, well-established set of search queries and themes. If you have already built awareness around a related term, use that as a platform to subtly redirect attention toward your new offering. The key is to plant new seeds in the fertile ground of topics where awareness already exists.

AI search provides limited nuanced advice for industry experts.

The B2B purchasing journey is complex, requiring layered and highly contextual information to satisfy a diverse buying committee, from a CFO focused on ROI to an account coordinator concerned with usability. This is an area where AI search models frequently fall short. They are exceptional at solving “needle-in-a-haystack” problems, pinpointing a precise answer buried in vast amounts of data. For instance, they can quickly tell a homeowner how to tap into home equity without refinancing a low-rate mortgage.

However, these models are less effective with broader, more strategic “haystack” questions, such as, “What’s the best way to modernize my warehouse?” The output often feels generic because the AI cannot account for the unique context of a company’s specific size, budget, or strategic goals. In essence, AI search can find the needle, but it cannot design the haystack. Compounding this issue is the ongoing risk of hallucinations and misinformation. Newer models may aim for improved accuracy, but marketers must still treat their outputs with a degree of skepticism. In the high-stakes B2B environment, where depth is paramount, this risk leaves significant room for error when trying to impress a multi-party buying committee.

The adjustment for marketers is to create and distribute deep, authoritative content like whitepapers, detailed user guides, and comprehensive case studies that provide experts the confidence they need. Adopt a strategy of “triangulation” by building a strong presence across all the places your audience seeks information. This includes LLMs, but also extends to Google, Reddit, industry-specific listings, and, most importantly, your own owned media channels.

AI search lacks real and perceived objectivity.

Trust is a major hurdle for AI search tools. Data from properties like Google’s AI Overviews suggests their results are often more trusted than those from ChatGPT, but this itself hints at a deeper problem: the potential for bias. This sentiment applies broadly to AI search, especially since these tools do not always transparently cite their sources. For example, a query for a “list of the top industry providers” might pull data from the very providers listed, reflecting strong GEO tactics rather than an objectively earned reputation.

While users can ask an LLM to cite its sources and receive a list of links, this action contradicts the primary reason people use these tools, to get a single, quick, and digestible answer without the need to cross-reference multiple sources. This doesn’t negate AI search’s utility for generating a quick consideration set, which people are certainly using it for. The information is neatly organized and easy to read, but it lacks critical verification and social proof, forcing both the user and the brand to undertake important supplemental research steps.

Marketers should think like their users. When researching tools, many people, including the data from our B2B and SaaS clients, use AI search to narrow down options but then immediately turn to Google to investigate each brand more deeply. They look for case studies, specific use cases, and reviews, assets that are far easier to find in traditional search engine results pages than within LLM outputs. Work closely with your sales team to understand the information-gathering habits of your prospects. Ensure your owned media comprehensively answers the complex questions that ChatGPT cannot. Furthermore, build a robust strategy for third-party reviews and listings on platforms like G2 and Capterra. Without these foundational elements, you risk leaking conversions between the middle and bottom of the sales funnel.

Building a complete strategy that extends beyond AI search is essential.

The landscape of AI search is not fixed; model engineers may eventually integrate reviews and third-party rankings directly into their outputs. However, until LLMs can truly replicate the established strengths of traditional search engines, they will continue to possess fundamental limitations. For B2B marketers, the imperative is clear: plan for these gaps within any holistic organic strategy to ensure sustained growth and competitive advantage.

(Source: Search Engine Land)