3 Common mistakes to avoid when investing in AI search

SEO is at an inflection point due to the rise of LLMs as search platforms. This has led to a lot of contradictory information about how we should approach LLMs and whether we should even continue to call their optimization “SEO.”

As a consequence, I’ve been dedicating much of my day-to-day as an SEO consultant to clarifying countless questions about AI search, many of them coming directly from decision-makers. I’ve helped them establish AI search optimization roadmaps that make sense, are realistic, and are cost-effective based on each company’s context and current SEO process.

My goal: to avoid fundamental AI search optimization mistakes triggered by misinformation circulating on social media.

Interestingly, many of the AI search strategy doubts and issues I’ve fielded have been similar, despite the heterogeneity of my clients—who range from established multinationals with in-house SEO teams and mature processes across countries to startups with relatively new SEO practices that operate in highly aggressive industries like financed in competitive markets like the US.

Here are the most common mistakes I’ve seen when starting to optimize for AI search—and how to avoid them:

1. Not aligning AI search optimization efforts with existing SEO initiatives

Working in silos wastes resources and creates inconsistencies.

AI search optimization and traditional SEO differ significantly in terms of user search behavior, the way information is retrieved, and how results are formatted and displayed. Because of these differences, each optimization approach requires its own specific metrics and goals.

Despite these differences, the core pillars of traditional search optimization still apply to AI search. Failing to align these efforts is a mistake, as it can lead to duplicated work, missed compounding benefits, and inconsistencies.

Each of the traditional search optimization principles has an AI search optimization counterpart. 

For example:

• Expanding and re-prioritizing our search optimization efforts to target the technical base for crawlability and indexability, taking into account the level of access we want to give to the different AI bots 
• Ensuring the indexability of key content with the understanding that unlike Google, AI bots don’t render client-side JavaScript
• Coordinating and aligning with PR or community management teams to incentivize and monitor positive mentions of the brand in relevant platforms and publications that AI platforms take into account

2. Expecting the same goals and using the same metrics as traditional search

While traditional search is purely a performance channel, AI search functions as both a branding and a performance channel. Because these channels differ in search behavior, results formats, and their role within the user’s search journey, they require distinct metrics and goals.

If you treat AI search solely as a performance channel—expecting…