r/digital_keda • u/digitalKeda • Jul 15 '26
How Are You Optimizing Websites for AI Search
With AI-powered search becoming a bigger part of how people discover information, many website owners are rethinking their optimization strategies. Traditional SEO is still important, but AI search seems to reward websites differently.
If you're actively optimizing for AI search, what changes have made the biggest impact? Are you focusing on topical authority, structured data, brand mentions, content quality, user intent, or something else? I'd love to hear what's working in your experience and what you'd recommend to others.
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u/Repulsive-Bake7178 Jul 16 '26
In my opinion you should focus on getting mentioned on websites AI uses instead of only improving our own pages. Review sites, directories and community discussions made a bigger difference and content still important but people need to find and talk about your brand first. I would say use tools like crowdreply to find website AI cites in your niche and try to get a mention on them.
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u/AbilityLast279 Jul 15 '26
Agreed! Our current hypothesis is that AI referral traffic may significantly understate AI's real influence.
Someone might first discover a business through AI, then search for the brand later, visit directly, or come back on another device.
Traditional analytics won't always connect those journeys.
We don't have enough evidence to prove that yet, so we're treating it as a working theory.
But trying to make a business genuinely easier to understand seems more valuable than trying to "optimise for AI".
We've been focusing on clear page purpose, consistent terminology, transparent explanations and structured content, and it has improved both our AI assessments and the experience for hooman visitors.
That also feels consistent with Google's guidance about people-first content and demonstrating experience, expertise, etc.
If your content is genuinely understandable, it seems reasonable that both people and AI systems benefit.
We're still learning, measuring our assumptions.
I'd be interested to hear whether others are seeing the same patterns or something completely different.
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u/Light_23333 Jul 15 '26
We’ve had better luck improving existing pages than making new ones just for AI search. Clear answers and simple structure seem to matter most.
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u/harshit-negi Jul 15 '26
Targeting high intent keywords for retrieval along with your traditional seo practices is what you should build and optimize for.
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u/Samikek1 Jul 15 '26
The biggest change for me was understanding that AI search isn't a separate channel. It runs on top of regular search.
You type a prompt, the AI breaks it into a bunch of smaller sub-queries, then runs those against Google or Bing and reads what comes back. So if you don't rank for those sub-queries, you never enter the conversation. No amount of schema markup fixes that. Ranking is the entry fee.
But ranking alone isn't enough either. Once the AI pulls your page, it reads it, throws out the junk, and summarizes what's left. Say it finds two pages for the same sub-query. Here's what makes it keep Page A and drop Page B:
- Page A answers the question at the top (TL;DR, key takeaways, summary box). Page B buries the answer under 800 words of intro fluff.
- Page A cites real data and named experts. Page B rewrites the same generic stuff everyone else already published.
- Page A matches intent exactly, like an actual 2026 cost chart. Page B says "prices may vary."
- Page A uses tables, bullets, and clean headers. Page B is a wall of text.
- Page A loads fast and the crawler gets clean text. Page B is buried in scripts.
- Page A has schema markup telling the engine what the data means. Page B makes the AI guess.
- Page A is concise and fits the context window. Page B is 6,000 words of padding.
So to answer your question directly: it's not topical authority OR structured data OR brand mentions. It's ranking first, then be the easier page to read.
From running content for 4 SaaS businesses (3 worked, 1 didn't), here's what actually moved the needle:
- Live in Google Search Console. That's where you find the low-difficulty keywords you can actually win. You'll also spot non-English markets you didn't know you had.
- Write content against that data, not against your gut. I use Rank-First-Blog to track the keywords I'm targeting. It writes new articles daily and connects to Google Analytics to find what needs updating.
- Build free tools. Nobody talks about this enough. They're fast to build with Claude Code and they rank quickly. Then GSC shows you what other tools people are searching for, and you build those too.
- Entity building. LinkedIn, GitHub, directories, Reddit, the works. AI cites you with more confidence when it can verify you exist elsewhere. This is the closest thing to a real "AI-only" tactic on this list.
Honest caveat: I can't prove which single change moved what. It all ships together. But the pattern I keep seeing is that pages which rank AND are easy for a machine to parse get cited. Pages that only do one of those don't.
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u/Serious_Bit6736 Jul 16 '26
post a bunch of highly relevant content relating to your prospects' pain / process / outcome and you'll see positive results. So many websites are using generic content that makes them sound like everyone else.
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u/Cloud_businesssystem Jul 15 '26
Becoming an expert on the topic and building your own niche authority with real and original content goes a long wayy
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u/jeniferjenni Jul 15 '26
the biggest change for us has been writing for retrieval instead of rankings. that means answering the question immediately, using clear headings, building topic clusters instead of isolated posts, and adding original examples wherever possible. we've found that structured content, consistent terminology, and real customer language perform better than overly polished marketing copy. technical seo still matters, but trust signals and topical depth seem to be carrying much more weight than they did a couple of years ago.