u/Real-Assist1833

Not here to panic about FAQ schema. Most of us saw this coming.

What I am actually doing with the freed up time and client attention:

First — strengthening Article schema with proper author markup. E-E-A-T signals matter more now and author entity markup is one of the cleaner ways to establish real authorship signals in the eyes of crawlers.

Second — Product schema for ecommerce clients. Still drives visible rich results and Google Shopping integration. Not going anywhere.

Third — HowTo schema where content genuinely fits it. Still seeing featured snippet correlation on instructional content.

What I am leaving alone — existing FAQ schema on pages where the FAQ section itself adds genuine user value. No point stripping markup that costs nothing to keep.

What I am not adding anymore — FAQ schema on pages where it was only ever there for the rich result. That bet has expired.

What is everyone else actually changing in their schema approach right now?

Been thinking about this since the FAQ rich result removal landed.

The practical SEO answer is easy stop expecting FAQ schema to generate SERP features, keep it if you have it, do not prioritize adding it.

The more interesting technical question is whether schema still influences AI search behavior.

My hypothesis: systems like Google AI Overviews and Perplexity use structured data signals when deciding what pages to cite and how to summarize content. FAQ schema in particular tells these systems which questions a page explicitly answers.

If that hypothesis is correct then schema strategy just became more important not less just for a different reason and a different output channel.

Has anyone run a structured test on this? Controlled pages, schema on vs off, monitoring AI Overview and third party AI citation rates?

I have been meaning to set this up but curious if anyone has data already.

Been doing SEO for several years and the FAQ rich result removal this month barely registered for me.

Here is my actual thinking on structured data in 2026:

Rich results were never the main value of schema. The main value was always helping crawlers understand page context — what a page is about, what entities it references, what questions it answers.

FAQ schema losing its SERP display does not change that function at all.

What I am actually focused on right now is schema that still drives visible results and entity signals — Product, Review, HowTo, Article with proper author markup, LocalBusiness for local clients.

The more interesting question to me is what role structured data plays in AI search. Google AI Overviews, Perplexity and others are parsing pages constantly. Whether schema influences those citations is something I have not seen clean data on yet.

Anyone actually tested this? Pages with schema versus without, comparing AI citation frequency?

"We don't need a website." "We don't need to be on social media." "We don't need to be in AI search results."

Each wave felt optional until it suddenly wasn't.

I'm not saying panic. I'm saying the window where this is still early is narrowing.

What if it's just making sure AI can find you, understand you, and trust you enough to mention you?

That sounds abstract until you realize:

Millions of people are asking AI for recommendations daily.

AI is answering without ever visiting your website.

It's building opinions about your brand from whatever it found across the web.

You're either in that picture or you're not.

And most businesses have no idea which one they are.

AI is incredible for:

• Explaining concepts
• Summarizing information
• Generating first drafts

But it's surprisingly unreliable for:

• Current events
• Niche expertise
• Anything that changed in the last 6 months

The people getting burned most are the ones who don't know where the line is.

Used to be:

"Here's a great article about this."

Now it's:

"I asked ChatGPT and here's what it said."

Screenshots of AI answers are replacing links in group chats, Slack, and DMs.

That's a pretty fundamental change in how information spreads.

I keep seeing the same pattern.

The brands getting recommended most often aren't always the best products.

They're the ones that show up consistently:

• in reviews
• in community discussions
• in comparisons
• across multiple platforms

It's almost like AI measures how often you're talked about, not how good you actually are.

The descriptions were generic, slightly outdated, and heavily skewed toward the biggest names.

Meanwhile the most innovative people in my space weren't mentioned at all.

AI summarizes what's already widely known.

It rarely surfaces what's actually happening on the ground.

That gap between "popular knowledge" and "real knowledge" is something I think about a lot.

Google had a hidden benefit nobody appreciated.

You'd search for one thing and accidentally discover something better.

A niche blog. A helpful forum thread. A tool you never heard of.

AI removes that serendipity completely.

You get exactly what you asked for.

Nothing more.

Feels efficient until you realize what you stopped finding.

For years the advice was:

Don't trust one source. Research deeply. Compare opinions.

Now AI gives one clean answer and people move on.

The habit of "digging deeper" is quietly disappearing.

And I'm not sure if that's the tool's fault or just human nature taking the easy path.

Anyone else notice their research depth shrinking?

Started thinking about this differently recently.

We optimize websites for Google crawlers. Schema markup. Structured data. Sitemaps.

But are we thinking about how AI systems read our content?

Clear entity definitions. Factual claim structure. Machine-readable relationships between concepts.

Feels like there's a whole layer of technical work that nobody has clean standards for yet.

Any devs here thinking about this? Would love to know what approaches people are experimenting with.

Tried this on a whim. Pasted my messy task list.

Asked: "What should I actually focus on today if I only have 3 hours?"

It cut through everything.

No ego. No sunk cost bias. No "I should finish this because I started it."

Just logic.

The answer was obvious in hindsight. I had been avoiding it.

There's something both useful and slightly unsettling about having a tool that thinks without your emotional baggage.

Old internet: search → thousands of options → beautiful chaos of discovery

New internet: ask AI → 4 recommendations → done

I found so many things I love by accidentally clicking wrong results or going down research rabbit holes.

That serendipity is disappearing.

AI is efficient. I genuinely love using it.

But something about the randomness of old-school web browsing felt human in a way that's hard to explain.

Anyone else nostalgic for accidentally finding random websites that changed your perspective?

Started this experiment when I noticed AI search affecting our inbound.

Tracked manually for 2 months (painful).
Switched to LLMClicks.ai for the last 4 months (much better).

What I found:

AI visibility fluctuates way more than Google rankings
Different platforms cite us differently for similar queries
The content that gets us cited is NOT our most SEO-optimized content
Reddit and community mentions directly correlate with AI citations

The brands winning at AI visibility are doing something fundamentally different than traditional SEO.

Happy to share more specifics if useful to anyone here.

And you'd have no idea.

Most small business owners check their Google Analytics.

Nobody's checking if ChatGPT recommends them when someone asks "best [your service] near [city]."

I started checking after noticing a dip in discovery traffic.

Found 3 competitors being consistently recommended in my category. My business: zero mentions.

Not because I was doing anything wrong. Just because I hadn't thought about this at all.

Has anyone figured out how to fix this for local/small businesses specifically?

Watched this happen in real time in a meeting this week.

Colleague asked ChatGPT about a topic.

Got an answer.

Someone else pulled up the original article ChatGPT was clearly summarizing from.

The article said something slightly different, more nuanced.

The room still went with the AI summary.

Source: right there. Still ignored.

The trust has transferred from "where did this come from" to "what did AI say about it."

That's a massive shift.

We have decent Google rankings. Good reviews. Strong backlinks.

But I ran 15 different AI queries in our product category.

Mentioned zero times. Competitors appeared in almost every answer.

Spent the next week trying to figure out why.

Conclusion: Google signals and AI citation signals are not the same thing. Not even close.

Anyone else done this exercise? What did you find?

I used to love going deep on topics.

Multiple tabs. Forums. Contradictory opinions. The messy truth of research.

Now I ask AI, get a clean summary, feel satisfied, and move on.

But "clean summary" often means "averaged out, nuance removed."

I'm learning faster in some ways. Understanding less deeply in others.

Trade-off I didn't realize I was making.

Is this just me or do others feel this happening?

AI gives wrong information sometimes. We all know this.

What I didn't expect: it gives wrong information with such complete confidence that I second-guess MY knowledge before I second-guess IT.

That's a weird psychological inversion.

Google results felt fallible — you saw the sources. You could question them.

AI responses feel authoritative — even when they shouldn't.

I now have a personal rule: if it matters, verify it manually. No exceptions.

Anyone else developed specific habits around this?