Diving into the evolving world of B2B SaaS marketing, we’re thrilled to chat with Aisha Amaira, a MarTech expert with a deep-rooted passion for blending technology and marketing. With her extensive background in CRM marketing technology and customer data platforms, Aisha has a keen eye for how businesses can harness innovation to uncover vital customer insights. In this interview, we explore the hype surrounding AI search tools, the challenges they pose for B2B marketers, and actionable strategies to bridge the gaps in organic marketing approaches. From building awareness for new solutions to ensuring credibility in a landscape of AI-driven results, Aisha shares her expert perspective on navigating this dynamic terrain.
How do you see the current excitement around AI search tools shaping the B2B marketing landscape?
I think the excitement around AI search tools, whether you call them GEO or LLMs, is palpable in the B2B space. There’s this rush to adopt them because they promise efficiency and a new way to connect with audiences organically. A recent study showed that 68% of brands are pivoting their search strategies to ride this wave, and it’s no surprise—since these tools burst onto the scene a few years ago, they’ve been positioned as game-changers. But while the buzz is real, I’ve noticed that many B2B marketers are still figuring out where these tools fit into their broader strategy and what they’re truly capable of delivering.
What do you believe is fueling this widespread shift toward AI search among brands?
A big driver is the allure of speed and precision. AI search tools offer the potential to cut through the noise and deliver answers or insights faster than traditional methods. For B2B marketers, who are often juggling complex buyer journeys and multiple stakeholders, that’s incredibly appealing. There’s also a fear of missing out—brands see competitors experimenting with these technologies and worry about falling behind. Plus, the promise of leveraging data in real-time to personalize outreach or optimize content is a huge draw, even if the reality doesn’t always match the hype just yet.
Do you think B2B marketers have a clear understanding of the strengths and limitations of AI search for their brands?
Honestly, not entirely. There’s a lot of enthusiasm, but I’ve spoken with many marketing leaders who haven’t fully grasped the boundaries of what AI search can achieve. They often see it as a silver bullet for organic growth, without recognizing the gaps—whether it’s building awareness for niche products or delivering deep, contextual advice. It’s critical to integrate AI search into a holistic strategy, but leaning on it too heavily without addressing its shortcomings can leave brands vulnerable to competitors who take a more balanced approach.
Why does AI search often struggle to create awareness for emerging products or verticals in the B2B space?
AI search, much like traditional SEO, operates on an intent-based model that relies on existing awareness. If a product or vertical is new, there’s little to no search volume or established queries to tap into. On top of that, AI search tends to be slower at indexing fresh content compared to traditional engines, since it often pulls from what’s already been indexed elsewhere. So, for emerging solutions, it’s an uphill battle to surface in results, and marketers can’t rely on AI search alone to drive that initial visibility.
How does the slower content indexing of AI search compared to traditional engines affect awareness campaigns?
It creates a significant delay in getting new products or ideas in front of potential customers. Traditional search engines are faster at picking up and ranking fresh content, which is crucial for awareness campaigns where timing can be everything. With AI search, that lag means your new solution might not even appear in results until long after your campaign’s peak momentum. This forces marketers to think beyond AI search and lean on other channels—like social media or paid ads—to build that initial buzz while waiting for organic traction.
Can you walk us through what you mean by the “Trojan horse strategy” for connecting new products to established search terms?
Absolutely. The idea behind the Trojan horse strategy is to piggyback on existing awareness. If you’ve got a new product or service with no search volume, you tie it to a related, well-established set of keywords or themes that already have traction. For instance, if you’re launching a niche software tool, you might align it with broader, recognized terms in your industry through your content. This way, you’re subtly redirecting attention from something users are already searching for to your new offering, planting seeds where the ground is already fertile.
Could you share an example of a brand that has effectively used this strategy to boost awareness through search?
I’ve seen this work well with a SaaS company launching a specialized project management tool for a niche industry. They didn’t have much direct search interest at first, so they created content tying their product to broader, established terms like “project management software for small businesses.” By focusing on blogs, guides, and landing pages around those popular queries, they drew in traffic and gradually introduced their unique offering. Over time, they built awareness for their specific solution by leveraging the existing demand for related concepts.
Why do you think AI search often falls short when it comes to providing detailed, strategic advice for B2B experts?
B2B buying is complex—it involves multiple decision-makers who need layered, contextual information to feel confident. AI search excels at narrow, specific queries, but it struggles with broader, strategic questions like “How should I modernize my operations?” The responses often lack depth or feel generic because the models can’t fully account for a company’s unique context—think budget constraints or specific goals. Unlike e-commerce, where answers can be straightforward, B2B requires nuance that AI search just isn’t equipped to handle consistently.
Can you explain the difference between “needle-in-a-haystack” and “haystack” problems in the context of AI search?
Sure. “Needle-in-a-haystack” problems are about finding a precise answer buried in a lot of noise. AI search is fantastic for this—like pulling up a specific product feature or a quick fact. But “haystack” problems are broader, more strategic challenges where you’re not just looking for one answer but trying to understand the whole landscape or design a solution. AI search often falters here because it can’t synthesize the big picture or provide tailored guidance, leaving B2B marketers with vague or incomplete insights for complex decisions.
How do issues like hallucinations or misinformation in AI search results impact trust among B2B decision-makers?
These issues are a real concern in B2B, where accuracy and depth are non-negotiable. Hallucinations—where AI generates plausible but incorrect information—can erode trust fast, especially when you’re dealing with a buying committee that’s scrutinizing every detail. If a CFO or IT lead spots an error in a result, it casts doubt on the entire tool or brand associated with that information. This risk makes it harder for AI search to be a standalone resource for B2B decision-makers, who often need verifiable, reliable data to move forward.
What kinds of content do you suggest B2B marketers focus on to address the gaps left by AI search?
I always recommend creating content that offers the depth AI search lacks. Whitepapers, detailed user guides, and case studies are gold because they provide the context and proof points B2B buyers crave. These assets can walk through real-world applications, address specific pain points, and build confidence in a way that generic AI responses can’t. Owned media like blogs or downloadable resources also help—anything that gives experts the meaty, actionable insights they need to make informed decisions.
What do you mean by “triangulation” when building a presence for B2B audiences across platforms?
Triangulation is about anticipating where your audience goes for information and ensuring you’re visible across those touchpoints. It’s not just about AI search or traditional search engines—it’s also about showing up on platforms like Reddit, industry forums, and even your own website with consistent, valuable content. The idea is to create multiple entry points for users to find and trust your brand, so if one channel like AI search falls short, you’ve got a strong presence elsewhere to reinforce your message and authority.
Why are AI search results often perceived as less objective than those from traditional search engines?
It often comes down to transparency. Traditional search engines, while not perfect, usually show a range of results with clear links to sources, giving users a sense of balance. AI search, on the other hand, frequently delivers a single, synthesized answer without always citing where the information comes from. This can make results feel less objective, especially if they seem to favor brands with strong optimization over those with a genuinely earned reputation. That lack of clarity breeds skepticism among users, particularly in B2B where credibility is everything.
How does the absence of source citations in AI search affect its value for B2B buyers seeking trustworthy information?
It’s a significant drawback. B2B buyers aren’t just looking for quick answers—they want to verify the information and dig deeper, especially for high-stakes purchases. When AI search doesn’t cite sources, it forces users to take extra steps to validate the data, which defeats the purpose of using these tools for efficiency. Many end up turning back to traditional search or third-party reviews for confirmation, meaning AI search alone often can’t close the loop for buyers who prioritize reliability.
What challenges do B2B marketers face when AI search results might prioritize brands with strong optimization over earned reputation?
It creates an uneven playing field. If AI search results lean toward brands that have mastered technical optimization rather than those with a proven track record, smaller or newer players with solid offerings can get buried. For marketers, this means you might invest heavily in building a great product and reputation, only to be outshone by competitors with better keyword strategies or AI-friendly content. It’s frustrating because B2B buyers value trust and expertise over slick presentation, but the results don’t always reflect that.
How can B2B marketers ensure their brand stands out when users revert to traditional search for deeper research?
Marketers need to think like their prospects and map out where they go for validation. Often, after using AI search for a quick overview, buyers turn to traditional search for case studies, reviews, or detailed use cases. Make sure your brand has a strong presence there with high-quality owned media—blogs, landing pages, downloadable guides—that answer specific questions. Also, invest in third-party platforms like review sites or industry listings. Work closely with your sales team to understand prospect behavior and ensure your content fills the gaps AI search can’t address.
What’s your forecast for the role of AI search in B2B marketing over the next few years?
I think AI search will continue to grow as a key part of organic strategies, especially as models improve in accuracy and source transparency. We might see better integration of reviews or third-party data, which could address some current limitations. However, I don’t believe it will fully replace traditional search or other channels in B2B marketing anytime soon—there’s too much need for depth and trust that AI search hasn’t mastered yet. My forecast is that the most successful B2B marketers will be those who blend AI search with a robust, multi-channel approach, using each tool for its strengths while offsetting its weaknesses.