Generative AI Guide For SEO Practitioners

As FOMO and uncertainty about generative AI spread, I’m getting two sorts of questions about the technology’s impacts on SEO: 1) speculation about generative AI’s threats to Google’s revenue from search marketing; and 2) curiosity about how SEO practitioners can realize the technology’s benefits. Lately, the two questions have merged as AI-integrated search engine results pages (SERPs), such as Google’s Search Generative Experience and the new Bing, affect search engines’ business models and search marketers’ workflows. To alleviate some uncertainty, here are answers to a couple of FAQs:

How should SEO practitioners use generative AI to automate workflows?

A few practitioners I’ve met use ChatGPT to automate technical SEO tasks like generating schema markups, issuing directives for robots.txt, creating redirect codes, writing title tags, and facilitating link building. I recommend that all practitioners, whether in-house or at agencies, consider automating such crucial yet tedious tasks. Generative AI’s ability to rapidly complete these tasks eases SEO’s persistent bottleneck: organizations’ lack of cross-functional resources and buy-in to manually fulfill technical SEO requirements.

Delegating technical SEO tasks to generative AI tools affords marketers time and energy that they can invest in strategic, creative initiatives — moving beyond implementing best practices. For instance, rather than spending time creating alt text for image files, marketers can practice holistic search marketing, which unifies paid and organic search to maximize ownership of the SERP. Holistic search marketing is imperative as the rise of zero-click searches and interactive natural language processing on increasingly visual SERPs exacerbates silos between paid, organic, and local search.

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How should SEO measurement adapt to AI-integrated SERPs?

Adaptation begins by recognizing that generative AI accelerates the development of conversational search. Thus, metrics like ranking and click-through rate lose importance to models like ChatGPT and Bard that apprehend content quality.

Because these models are patterned on natural language, your content’s quality depends on its humanity. Ironically, the more closely your content’s syntax and sentiment resemble human speech, the more intelligible it is to AI chatbots.

More intelligible content benefits consumers and AI but complicates attribution. As consumers’ dialogue with search engines grows increasingly complex and multimodal (spanning videos, local listings, shopping results, and more), it’s harder to attribute audiences’ responses to a single source of information. To solve this, SEO practitioners must reduce their reliance on outmoded metrics like average position or search volume. Instead, they should feed AI models a variety of content, including copy, images, and videos, and consider inclusion in AI-powered snippets atop the SERP to signify their content’s relatively high quality.

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As AI-powered snippets prove their value on Google’s SERP, the company’s unique ability (given its index of the internet) to authenticate sources of information will sustain its search engine’s utility and market dominance.

This post was written by Forrester Senior Analyst Nikhil Lai and it originally appeared here.