🚀 3 Critical Mistakes to Avoid as AI-Search Takes Off

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-X (formerly Twitter) has updated its “Handles Marketplace” to include new terms and conditions for users purchasing popular @usernames. Buyers must hold a Premium+ or Premium Business subscription, and they do not gain ownership of the handle — they receive a revocable, non-transferable license subject to X’s ongoing conditions (like regular account activity). The pricing for premium handles can range from five to seven figures in USD, but with these restrictions the value proposition is significantly changed.

-LinkedIn is rolling out a new AI-powered conversational search feature that lets users type natural language queries (e.g., “ex-coworkers who became founders in healthcare in NY”) and get matches for people, pages, or posts across their network.
The function taps into LinkedIn’s professional database and previously launched conversational job search tools, signaling a push by Microsoft to embed AI deeper into the platform.
Currently it’s available to Premium users in the U.S., with plans to expand to all members globally.

-Reddit’s new research shows that female car buyers increasingly use the platform to guide their vehicle purchases, with nearly half of Reddit’s 185 million U.S. weekly users being women. They trust real, community-driven discussions more than comparison sites and use Reddit to compare models, gather honest reviews, and validate decisions. Importantly, many of these conversations happen in lifestyle subreddits rather than car-focused ones, creating wider marketing opportunities for automotive brands.

The world of search is undergoing a major shift. With large language models (LLMs) powering new search experiences, the rules of the game are changing — and many websites are making avoidable mistakes. According to SEO expert Aleyda Solis in Search Engine Land, here are the top pitfalls you need to steer clear of — and how to stay ahead.

Even though AI-driven search behaves differently from traditional search, the fundamentals of SEO still apply. Treating AI search as totally separate from your organic search strategy leads to duplicated efforts, inconsistencies and missed synergies.
What to do: Map out how your site’s crawlability, indexability, entity authority and content structure serve both traditional search and AI-search scenarios. Align teams (SEO, content, tech-dev, PR) so your optimization efforts are coherent.

AI search isn’t just about traffic and clicks — it mixes branding, performance, and new forms of visibility. If you only measure direct conversions and traffic, you’re missing half the picture.
What to do: Define dual KPIs: branding visibility (e.g., mentions in AI-answers, citation share, brand sentiment) and performance (inclusions in AI responses, referral traffic, assisted conversions). Then tailor the weights of each based on business model (B2B vs B2C, transactional vs informational).

Many teams fall into the trap of optimizing for “sample prompts” that AI tools publish (e.g., “best CRM for SaaS startups”) without recognizing how actual users ask questions. That leads to chasing low-value keywords and misunderstanding user intent.
What to do: Treat the prompts as benchmarks, not targets. Dive into user-behaviour: real queries, phrasing shifts, context, user history, location. Build content that covers full user-journeys (pros vs cons, decisions, peer comparisons) rather than one-off optimized prompts.

A key insight: Some AI search results are retrieval-based (they cite real pages) and others are simply “model-generated” (based on pre-trained data). If you don’t know which you’re optimizing for, you may waste effort where it won’t move the needle.

What to do: Use tools or manual checks to understand: when your topic appears in AI search results, is it pulling your site’s content (grounded) or is it entirely generative? Prioritize efforts where “grounding” indicates your optimization has an influence.

Before diving full-steam into “AI search optimisation”, pause and reflect with these questions from Aleyda’s article:

SEO is at inflection point: as Aleyda writes, “We’re at an inflection point for search as a marketing channel.”
In other words: the brands that adapt now—by aligning strategy, metrics, content and optimisation for the era of AI-search—will gain the compounding benefit of visibility and authority. Those that wait risk being outpaced by competitors who already integrate AI-search into their marketing machine.

Thanks for reading — we hope this gives you a clear blueprint to adapt your content and optimisation strategy for the emerging AI-search era. If you found this useful, feel free to forward it to your team.

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