AI search has a subtle impact on trust, sales velocity, and potential client shortlists, which often isn’t reflected in analytics data. These insights came to light through a series of revealing experiments I’ve been involved in.
It was a chance encounter with a new prospect who mentioned, “I actually found you via Grok.” That was a pivotal moment for me. Not only had we not attempted to rank on Grok, but we also weren’t monitoring it. Yet, here it was, influencing potential buyers’ search and evaluation processes.
This realization permeated conversations with other clients; fascination with AI search was rampant, but there was skepticism regarding data credibility. Many wanted visibility on platforms like ChatGPT but hesitated due to unclear attribution.
So, I embarked on structured testing using resources I could control entirely—our agency website, personal experiments, e-commerce ventures, and select domains for testing purposes. The goal wasn’t to attain AI rankings but to decode which elements remain crucial once AI integrates into buying decisions.
These inquiries involved figuring out if AI search altered purchasing preferences or merely the ranking of brands. Additionally, I wanted to understand if revenue metrics could be influenced by AI visibility without hitting the analytics tracking radar and how AI-driven recommendations might affect performance across other channels.
I realized early conversations around AI search revolved around visibility metrics—think brand citations, visibility screenshots from AI tracking platforms, and more. I believed that the primary role of search remains to aid decision-making. My experiments aimed to determine if AI search retained this capability and transformed business outcomes.
Focusing on measurement was crucial. Instead of just relying on API data—which often diverges from user interactions—I observed live interfaces of ChatGPT, Perplexity, Gemini, and Google AI Overviews. Prompt tracking aided in identifying patterns but was not a definitive gauge of success.
During my first experiment, the creation of self-promotional ‘best of’ lists on my own website revealed fascinating insights. Agencies frequently leveraged a tactic where they placed themselves atop ‘best X’ lists, allowing AI systems to inadvertently amplify their prominence.
Inspired by Glen Allsopp’s extensive research, which highlighted how ‘best’ lists were frequently cited by ChatGPT, I tested the findings on my brand webpage. I was intrigued by the rapid visibility of my site, LawrenceHitches.com, across AI platforms for queries like “best SEO agency Sydney.”
However, ranking visibility alone lacked significance. Similarly, when I fabricated a landscaping site to further test self-promotional tactics, it also appeared swiftly in AI responses, reaffirming visibility alone’s limited value.
Through these experiments, it became evident that while AI simplifies appearing on search radars, building and sustaining trust remains pivotal—a sentiment ringing true from the likes of Wil Reynolds. Self-lauding across one’s platform may catalyze skepticism rather than assurance.
I’ve also seen how prompt tracking tools became popular, with demand from clients ever-increasing. Yet, reliability remained a challenge. Surfer SEO research suggested brands often appeared differently in API outputs versus real user sessions. With overlap sometimes as low as 24%, discrepancies remind us that prompt appearances could be misleading.
This is where the narrative eases away from where brands show up and involves questioning efficacy: How did AI influence sales velocity? Did consultations eliminate the need for education? Was buying speedily initiated?
I discovered that signals—where leads factored AI tools into decision-making without prompting—started appearing, shaking traditional attribution’s foundation. A telling instance was Kadi, an e-commerce brand we support, encountering a buyer who, influenced by AI, engaged in a thorough purchasing journey yet showed attribution through Instagram.
For Kadi, digital PR efforts garnered visibility spurt, but gaps in fundamentals meant traditional SEO foundation work was essential to move past quick traction and truly compete. AI played a silent role in buyer decisions, even when attribution data failed to capture its essence.
My journey with StudioHawk provided another layer of understanding. Post a rebranding and digital migration, SEO emerged as a potent channel, complemented by AI leads that became more recurrent.
Sales processes further illustrated the transformation, where AI-affected leads saw reduced education requirements and minimized objections, closing deals notably faster than traditional SEO leads. The blend of ChatGPT, Perplexity, and Grok-influenced conversions stood testament to AI’s influence, even as traditional paths remained evasive in attribution reporting.
Throughout these endeavors, I’ve realized that while AI doesn’t redefine discovery, it compresses consideration significantly. The buyer’s journey is evolving beyond static funnels. AI provides succinct answer summaries, reshaping the ‘messy middle’ where amenities like risk reduction, vendor shortlisting, and trust assurance occur.
It’s evident AI aids decision-making once foundational trust is laid. Traditional SEO confirms search engines recognize your entity, but its real value is now within supporting thoughtful content that pre-sells your services.
So, as I reflect, brands need to realign focuses. Record where AI’s footprints actually land beyond mere appearances. Prioritize intelligibility over creativeness in content. Opt for consistency in entity-driven narratives and prioritize content resonating with comparison and risk evaluations.
Inspired by this post on Search Engine Land.
