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SEO Guide for SaaS Companies

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Key Takeaways

  • SaaS SEO requires a strategy built for long sales cycles and global competition.
  • Map your content to every stage of the buyer journey, from awareness-stage blog posts to decision-stage case studies and comparison pages. 
  • AI search has reshaped how buyers research software, with 51 percent of B2B software buyers now starting with an AI chatbot more often than Google. That means optimizing as much for AI citations as search rankings. 
  • Build authority through technical SEO, high-quality content, digital PR, and a strong presence on third-party review sites like G2 and Capterra. 

There are few better traffic sources for software-as-a-service (SaaS) companies than organic search. 

Recent data shows that B2B SaaS companies see a 702 percent return on investment (ROI) from their SEO efforts. That’s a huge number, and it makes a ton of sense. 

With the right SaaS SEO campaign, you can acquire users for free and at scale. Best of all, many of those users arrive intending to make a purchase. 

So, how do you get your website to dominate the search engine results pages (SERPs)? And how do you keep those visitors on your site and convert them to customers?

SEO for SaaS companies has to account for the unique features of the industry. SaaS brands have a very different target market and customer journey than, say, a local business or a law firm. Your SaaS SEO strategy needs to reflect this. 

In this SaaS SEO guide, I’ll show you how to build a strategy that checks all the right boxes. We’ll cover the benefits of generating organic traffic for SaaS businesses and walk through actionable steps you can use to improve your SEO positioning today. 

What Is SaaS SEO?

SaaS SEO is search engine optimization that helps software-as-a-service brands rank higher in Google. It covers the activities that drive search engine traffic, including:

  • Keyword research
  • Content creation
  • On-page SEO
  • Link building

The difference is how you approach each one. 

Google’s ranking factors are the same across industries, but SaaS brings its own opportunities and challenges.

The SaaS Industry and What Makes It Unique for SEO

The SaaS industry has its own quirks, and you’ll want to keep them in mind when building your SEO strategy.

For one, competition is fierce. Plenty of businesses offer similar services, and some are working with much larger marketing budgets. Comparison sites like Capterra and G2 pile on, too. Their domain authority is strong enough that they regularly outrank individual SaaS brands for product-related queries.

Oh, and your competition is worldwide, not local, which means global SEO considerations come into play from the start.

AI search has reshaped how SaaS buyers research and discover tools. ChatGPT, Perplexity, and Google’s AI Overviews now answer many top-of-funnel questions directly in the results. 

That means fewer informational clicks reach your site. According to GrowthSRC, Google’s average click-through rate (CTR) for the No. 1 ranking position dropped 32 percent year-over-year, falling from 28 percent in 2024 to 19 percent in 2025 after the AI Overviews rollout.

For SaaS marketers, the goal is now to rank in traditional search results and be cited in the AI answers that show up in front of your buyers.

The SaaS Buyer Journey

The SaaS customer journey often involves weeks or months of research and decision-making. 

That means your focus should be on long-term SEO strategies, such as content marketing and technical optimization. A “quick fix” approach won’t be as effective. 

You’ll also need content for every stage of the journey. It’s not enough to create top-of-the-funnel content to attract people and hope they convert. You need product-focused content to show readers why they should choose your tool over a competitor’s.

You probably won’t get a conversion the first time someone visits your site, as the image below from Salespanel shows. Your SEO plan needs to build sustained authority in your space over time. That’s how you stay visible in search results and keep attracting potential customers to your site.

An infographic showing multiple touchpoints across the four stages of the customer journey: Awareness, Consideration, Acquisition, Service, and Loyalty. 

The Salespanel graphic maps the full lifecycle (awareness, consideration, acquisition, service, and loyalty). For SEO planning, though, marketers will more likely work from a simpler model focused on where content does the heavy lifting. 

HubSpot’s three-stage framework is a good starting point:

  • Awareness: Educational blog posts and guides that answer the problem your tool solves.
  • Consideration: Comparison pages, alternative roundups, and integration content that helps buyers shortlist you.
  • Decision: Case studies, ROI calculators, and product-led landing pages that close the gap between interest and signup.
saas SEO 003

Source: https://blog.hubspot.com/marketing/content-for-every-funnel-stage

It’s not just new customers you have to think about, though. 

Because SaaS brands use a subscription-based business model, you need to be constantly thinking about how to retain customers. Your B2B SaaS SEO strategy will need to reflect this by creating content that simultaneously attracts new users, educates existing customers, and showcases your authority. 

Benefits of SEO for SaaS Businesses

Done well, SEO supports SaaS marketing efforts. It puts you in front of potential customers who are actively seeking what you sell.

The global SaaS market reached $315.7 billion in 2025, is projected to hit $375.6 billion in 2026, and could reach nearly $1.5 trillion by 2034. Capturing even a sliver of that growth means investing in SEO.

If you’re not sold yet, here are more benefits of SEO for SaaS brands:

  • Increased brand visibility and credibility. The more your content appears in the search, the more reputable your brand will become. This can be a deciding factor when potential customers are choosing between you and a competitor. 
  • Scalable traffic. There is a huge compounding factor with SEO. The more content you create and the more links you build, the more your month-on-month traffic will grow. 
  • Lower-cost acquisition. Putting more effort into your SEO reduces your dependence on paid marketing. As is the case in an industry with high ad costs, anything to alleviate some of that spend will be helpful for SaaS companies.
  • Increased product awareness. The more your product shows up in search engines, the easier it will be for your sales team to talk about your product because customers will have probably seen it before.
  • Helps to convert prospects from other channels. The SEO content you create is multipurpose and can be used by sales teams to convert prospects from other marketing channels, such as social media and pay-per-click (PPC) ads. 
  • Improve your user experience. Many SEO tasks also improve user experience, such as optimizing for speed, navigation, and readability. This helps ensure that potential customers stay on your website longer and are more likely to convert.
  • Stronger brand health. Organic search data can flag early signs of shifting brand trust or product satisfaction, giving SaaS teams a real-time read on how the market perceives them and a chance to course-correct before issues show up in churn or pipeline.

Regular SEO work also helps you hold on to the rankings you’ve earned. Search engines constantly update their algorithms, so refreshing content and earning new links will keep you ahead of the game.

More and more buyers are searching online for products, too. More than two-thirds (67 percent) of B2B buyers now prefer a rep-free buying experience, and 45 percent say they used AI during a recent purchase. That means you need to appear in search engines and AI Overviews if you want your product on your audience’s shortlist. 

SEO also plays well with the B2B industry’s long sales cycles. A single paid ad isn’t going to be enough to keep your prospects on the hook. You need a regular stream of content that showcases your authority and keeps your prospects in your funnel if you want to succeed. Only SEO can achieve that. 

Best Practices for Improving Your SaaS SEO Strategy

SEO for SaaS companies takes more than publishing a few blogs and hoping for the best. It succeeds when keyword research, on-page optimization, technical SEO, and off-page authority all work together. 

Here’s how to approach each one.

Best Practices for SaaS Keyword Research

Before optimizing your website for SEO, it’s important to do keyword research to identify the terms and phrases potential customers use when searching for your SaaS tool.

Start by creating a list of relevant keywords related to your business and the products or services that you offer. This can include words and phrases related to your software’s features and general topics in your industry.

You’ll also want to choose keywords that map to each phase of the buyer journey. That’s even more important now that AI search favors content that closely matches user intent.

Think beyond the keyword itself. Ask what the searcher is trying to accomplish, then create content that answers that need. The better your content matches intent, the more likely it is to rank in search results and appear in AI-generated answers. 

Once you have a shortlist of keywords, use a tool like Ubersuggest to compare their search volume and competition. That will help you prioritize the opportunities with the greatest potential.

For example, searching for “customer data management platform” shows you how often people search for the term and which related keywords are worth exploring next.

Ubersuggest Keyword Summary for “customer data management platform.

Don’t worry if your initial keyword list is short. SaaS companies often operate in highly specialized, competitive markets where obvious keyword opportunities are limited.

That doesn’t mean the opportunity isn’t there.

First, don’t get hung up on search volume. A keyword that brings in even a dozen visits per month can result in a sale that delivers a return on your investment. 

Second, focus on your product’s features and the problems they solve for customers. These kinds of queries may be hyper-specific, but they also tend to have strong purchase intent. You can also create vs. pages that compare your product with your competitors. 

Again, these bottom-of-the-funnel keywords are great at driving high-intent traffic.

An example of a comparison article that would be a great bottom-of-funnel piece to help your customers during the Decision phase of their buyer’s journey.

Source: https://www.rock.so/blog/asana-vs-monday

Third, look at what your competitors are ranking for. Use a tool like Ubersuggest to mine your competitors’ websites and find relevant keywords you can rank for, too. 

The more you understand your competitors’ content strategies, the easier it will be to identify gaps you can fill with better, more useful content.

Strong keyword research lays the foundation for every other part of your SaaS SEO strategy. 

Best Practices for On-Page SaaS SEO

On-page SEO involves optimizing the pages on your website so they’re easy for both search engines and people to understand.

To improve on-page SEO, start with the fundamentals. Write descriptive page titles and meta descriptions, organize your content with clear headings, optimize your images with descriptive alt text, and add internal links that guide readers to related resources. These elements help search engines understand your content while making it easier for visitors to navigate your site.

The more relevant information you provide on a single page, the better your chances of ranking in the SERPs.

Great on-page SEO, however, starts with the quality of your content. If your page doesn’t answer a searcher’s question better than what’s already ranking, no amount of optimization will make up the difference.

So, what separates great content from average content?

First, it fully addresses the reader’s problem. It goes beyond the basics and answers the follow-up questions they’re likely to have instead of forcing them back to the search results.

Second, it provides an exceptional user experience. It’s well-structured and easy to read, broken into sections with headings, lists, images, and other elements to help users scan and digest information quickly.

This blog post from ClickUp is a good example of clear structure and strong formatting.

An example blog from ClickUp showing on-page SEO best practices like header structure, formatting, and readability.

Best Practices for Technical SaaS SEO

Technical SEO involves making changes to your website’s back end that are not visible to users but can still have a major impact on search engine visibility.

To optimize your technical SEO, start by optimizing site speed so your pages load quickly. Next, create an XML sitemap and submit it to search engines so they can easily crawl and index your pages.

Infographic explaining how Google’s web crawler uses your XML Sitemap to index your website’s pages.

Also, make sure to set up structured data, such as schema markup, to better understand the content on your page and help improve Google rankings. This helps search engines determine what each page is about, which in turn helps them return more relevant results for user queries.

It’s also important that your website is mobile-friendly and works properly on all devices. This will improve the user experience and enhance your chances of ranking in SERPs.

Best Practices for Off-Page SaaS SEO

Off-page SEO builds your website’s authority beyond your own domain. The biggest ranking signal is still high-quality backlinks from trusted, relevant websites that show search engines your content is worth recommending.

Below is an example of Twilio backlinking to SpotHero’s website.

A Twilio blog article showcasing an example of a backlink to their customer, SpotHero.

Source: https://www.twilio.com/en-us/blog/insights/data/customer-data-platform-roi

How do you find SaaS link-building opportunities?

One of the easiest ways to find link-building opportunities is to analyze your competitors’ backlink profiles. Tools like Ubersuggest can show you which websites already link to competing SaaS companies, giving you a shortlist of publications and blogs worth targeting. 

Here’s an example using Twilio.com:

An Ubersuggest screenshot showcasing how you can use your competitors’ backlink profile to discover potential outreach opportunities.

Note: If you click the orange “one link per domain” button, it will remove repeat domains and give you a much more manageable list. 

You don’t have to rely on outreach alone, though. Original research, industry surveys, reports, free tools, and useful resources naturally attract links because people want to reference them. Building relationships with industry publications and influencers can amplify that effect even further.

The goal is to earn links from websites your audience already trusts. Those links strengthen your authority and drive qualified referral traffic at the same time.

Creating Your SaaS Strategy: Step By Step

Now that we’ve covered the core pillars of SaaS SEO, it’s time to put them into action. Use the framework below to build a content-based SEO strategy that attracts qualified traffic and supports long-term growth.

Graphic displaying the steps of a content strategy.

This process breaks down into eight steps:

1. Set Goals and KPIs

Start by defining measurable goals for your SaaS SEO strategy. SaaS companies typically focus on attracting prospective buyers, driving traffic to free demos and converting them into paid users, and educating and retaining current users.

Next, choose the key performance indicators (KPIs) you’ll use to measure success. Track metrics like organic search visibility, keyword rankings, website traffic, and lead generation rate. That way, you’ll know what’s working and where to adjust your strategy as you go.

2. Identify Your Target Audience

Next, determine your target audience. Are they engineers? Marketing operations leaders? Business owners? Knowing who you are targeting helps you create content tailored to their needs.

3. Identify Their Pain Points

Once you know your target audience, focus on their needs and the kind of content they want. Identify topics that are relevant to them and create content that offers value, solves their problems, and answers their questions.

4. Analyze Best-Fitting Keywords

Now it’s time to put your keyword research into action. Use the terms you’ve identified to build content that matches search intent and supports each stage of the buyer journey. Then optimize your titles, headings, body copy, and URLs so search engines can clearly understand what each page is about.

In the example below, Gartner targets the keyword “customer data management platform” consistently across the title, meta description, and URL, reinforcing the page’s relevance for that search.

Google search results for “customer data management platform,” showing how Gartner’s use of that keyword in their title and meta description helped land them Position 1 in the SERPs. 

5. Set Campaign Goals and Tracking Abilities

Now that you’re ready to start creating content, you’ll need to revisit goal-setting on the campaign level. Set measurable goals for each campaign to track performance. You should also establish a tracking system (Use my SEO templates if you’re not sure where to start) to measure the success of each piece of content you produce and make changes accordingly.

6. Produce Content

Create high-quality content that matches search intent and gives readers a reason to choose your page over the competition. Use clear headings, visuals, examples, and other formatting elements to make complex topics easier to scan and understand.

7. Distribute Content

Publishing is only the beginning. Promote your content through your website, email newsletters, social media, and other marketing channels to maximize its reach. Then monitor its performance and refine your approach based on what’s driving traffic and conversions.

8. Monitor Results and Optimize Based on Findings

Your job isn’t over once your content goes live. Regularly optimizing your content based on analytics data can significantly improve your organic search visibility and get closer to achieving your SaaS SEO goals. 

Once a year is a good baseline timeframe for content review and refreshing, but ultimately, you should review your traffic and make adjustments based on what’s being read and what’s not.  

Successful SaaS SEO Strategies

Looking for inspiration? Here are a few SaaS brands that have built strong organic visibility and the SEO strategies that helped them get there. 

Adobe XD

To grow awareness of Adobe XD, its interface design and prototyping software, Adobe created a content-focused community targeting design professionals. 

Screenshot of the landing page for XD Ideas, the newsletter that has become the central hub of Adobe XD’s community of creatives.

Source: https://www.adobe.com/subscription/xdideasnewsletter.html

With the help of my agency, Adobe created an intent-based content strategy, backed by in-depth keyword, competitive, and content gap research. We also incorporated technical best practices, optimizing page speed, schema markup, and internal linking. 

The results? A 648 percent increase in page one rankings and over 25,000 downloads in the first six months, with just under half of all traffic coming from search engines. 

Canva

If you want an example of how to improve your SaaS SEO through backlinks, look no further than Canva.

The design platform has created hundreds of backlink magnets through its template pages. Covering everything from letterheads and invoices to coupons and invitations, these pages give users everything they need to create a great-looking design. And this means that websites provide a lot of value to their readers by linking to them.

Screenshot of Canva’s library of poster template

Source: https://www.canva.com/posters/templates/

As a result, Canva has acquired backlinks from highly authoritative domains such as The Next Web, Yahoo, and Thrillist.

But it doesn’t just wait for the links to roll in. Canva engages in personalized outreach to win those backlinks from target websites. 

The result?

They have over 25 million backlinks from 280,000 domains and rank for over 5 million keywords, according to Ahrefs.  

Smartlook

Product analytics and insights tool Smartlook used a product-focused keyword research strategy to break into the U.S. market and take on established goliaths like Hotjar. 

They achieved this via several strategies. 

The first was to improve existing landing pages that targeted competitor-style keywords like “Hotjar alternative.” While these pages were well-designed, they lacked sufficient depth of content to rank well on Google. Smartlook added much more information to these pages to better meet searcher intent and created new pages that comprehensively covered each topic. 

Cisco has since acquired Smartlook and continues to operate as part of its portfolio, but the SEO strategy remains just as relevant today. Building comprehensive comparison pages around high-intent keywords is still an effective way for SaaS companies to capture buyers evaluating their options.

Verint

A clear example of what a strong SaaS SEO strategy can achieve is our work with Verint, a customer experience software company serving more than 9,800 brands across 175 countries.

As Verint prepared for a major website migration, including multiple blog and acquired business unit migrations, the challenge was preserving existing rankings while growing non-branded organic traffic in a highly competitive market.

We partnered with Verint’s product and marketing teams to build an SEO strategy around the migration. That included technical fixes for Core Web Vitals, mapping redirects, and content audits using our 6Rs framework to determine what content should be reformatted, repurposed, refreshed, retired, redirected, or simply remain as is. 

The broader strategy also incorporated localization for global markets and a digital PR initiative to strengthen domain authority.

A screenshot of Verint’s CX Automation blog, the vehicle of most of NP Digital’s SEO wins for the brand.

Source: https://www.verint.com/blog/

Within 30 days of launch, Verint achieved a:

  • 210 percent increase in non-branded organic search clicks year over year
  • 33 percent increase in keywords ranking in positions one through 10
  • 32 percent increase in total organic search clicks year-over-year

The results show what’s possible with a coordinated SaaS SEO program.

SaaS and AI Search

AI search has become a fixture in the SaaS buyer journey. Prospects who used to start with a Google search are now asking ChatGPT to compare tools, prompting Perplexity for vendor shortlists or scanning Google’s AI Overviews before clicking anywhere. 

According to G2’s 2026 AI Search Insight Report, 51 percent of B2B software buyers now start their software research with an AI chatbot more often than with Google, and 71 percent rely on AI chatbots somewhere in the research process.

For SaaS marketers, this changes what visibility means. Ranking on page one still matters, but getting cited inside an AI answer matters, too. 

If a buyer asks ChatGPT for the best project management tool for remote teams and your product isn’t mentioned, you’ve lost the discovery moment entirely. The same G2 research found that 69 percent of buyers reported AI chatbots surfaced information that led them to choose a different vendor than expected.

To keep your audience engaged, your SaaS SEO strategy should now account for:

  • Clear, well-structured content that LLMs can parse and pull from with confidence.
  • Strong E-E-A-T signals like author bylines, original data, and expert quotes that build trust with both search engines and AI tools.
  • Presence on trusted third-party sites such as G2, Capterra, and respected industry publications. A recent SE Ranking study found that sites with profiles on review platforms like G2, Capterra, and Trustpilot were three times more likely to be cited by ChatGPT.
  • Branded content that defines your category so your name surfaces when buyers ask about the problem you solve.

FAQs

What is SaaS SEO?

SaaS SEO is the practice of optimizing a software-as-a-service company’s website to rank higher in search engines for keywords that matter to its buyers. It accounts for the industry’s specific nuances, such as long sales cycles and a subscription-based revenue model.

How important is SEO in a SaaS business?

SEO is one of the highest-leverage growth channels available to SaaS companies. It compounds over time, lowers your dependence on paid acquisition, and meets buyers where they’re already researching solutions. With B2B’s stretched-out sales cycles, a steady stream of search-visible content can keep prospects engaged from first touch to close.

How SaaS companies improve their SEO?

Start with a technical audit to fix page speed and indexing issues. Then build topic clusters that map to each stage of the buyer journey, target bottom-funnel terms like comparisons and alternatives, and earn authority through digital PR and third-party reviews on G2 and Capterra. 

What’s more important, a SaaS content strategy or SEO strategy?

They aren’t competing approaches. Your content strategy defines what you publish and why, and your SEO strategy ensures that content gets found. The strongest SaaS programs treat them as a single integrated effort, where keyword research informs content topics, and content quality drives rankings and conversions.

How do I combine SEO and PPC for SaaS lead generation?

Use PPC to capture high-intent commercial queries while SEO builds long-term authority on informational and comparison terms. Share keyword data between teams to identify which paid terms convert well enough to warrant organic investment, and use retargeting to re-engage organic visitors who didn’t convert on the first visit.

Conclusion

SaaS SEO is one of the most effective ways to attract qualified buyers throughout the customer journey. The stronger your strategy, the more opportunities you’ll have to build awareness and convert prospects into long-term customers.

Optimizing your SaaS website starts with a content strategy tailored to your target audience. That means researching relevant keywords and creating high-quality content that appeals to both users and search engines.

While your industry may be more technical than others, following the advice in this SaaS SEO guide will help you achieve better organic search visibility and get closer to your goals.

At the same time, search itself is changing. 

AI tools like ChatGPT and Claude are changing how buyers discover software, while Google’s AI Overviews are reshaping what visibility looks like on the SERP. 

The SaaS brands that win going forward will be the ones building content for both traditional search and AI answers, while mapping every piece to a real stage in the buyer journey

Read more at Read More

What Claude Design Could Mean for UX

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Key Takeaways

  • Claude Design is an experimental AI-powered design workspace from Anthropic, built on Claude Opus 4.7.
  • The tool is built for rapid ideation and early-stage prototyping, not final production design. 
  • A standout capability lets the tool ingest a company’s codebase and design files to apply brand guidelines automatically. 
  • Users can iterate through conversation, direct edits, and comments, then export to PDF, PPTX, Canva, or Claude Code workflows. 
  • Claude Design is a complement to tools like Figma and Canva, not a replacement for them. 

Most design tools are built for designers. That’s been the assumption for decades, and it’s created a bottleneck that marketers, product managers, and content teams know well: you have an idea, but getting it into a visual form means waiting on someone else’s schedule. 

Claude Design is Anthropic’s attempt to change that. Launched in April 2026, the AI-powered design workspace lets you turn a natural language prompt into a polished mockup, prototype, slide deck, or one-pager. No design background required. For user experience (UX) teams and the non-designers who work alongside them, the implications are worth paying attention to. 

What Is Claude Design?

On April 17, 2026, Anthropic rolled out Claude Design as an experimental AI-powered design workspace. The tool runs on Claude Opus 4.7 and targets two audiences: professional designers who want to move faster during early-stage work, and non-designers who need to turn ideas into visual assets without going through a full design cycle. 

The outputs it can produce include prototypes, slide decks, mockups, and one-pagers. All of it is driven by natural language prompts, meaning you describe what you want and the tool builds a working draft. 

Iteration happens through conversation. You can refine outputs by typing follow-up instructions, making direct edits, leaving comments, or adjusting built-in controls. When you’re ready to move the work downstream, export options include PDF, PPTX, Canva, and Claude Code workflows. 

Anthropic is positioning this as a complement to existing design tools, not a replacement. The pitch is efficiency at the ideation stage, not a takeover of the production workflow. 

How Claude Design Fits Into a UX Workflow

UX work has always had a front-loaded bottleneck. The ideation phase, where teams explore directions before committing to a concept, is time-intensive and resource-heavy. A designer has to be involved from the start, even when the work is exploratory and could change completely by the next round. 

A typical UX workflow.

Source 

Claude Design targets that specific problem. The ability to generate a working mockup or wireframe-level prototype from a prompt shortens the gap between having an idea and being able to react to something concrete. Teams can evaluate directions earlier, kill weak concepts faster, and give designers cleaner briefs when they do get involved. 

The ability for Claude Design to operate at any point during the UX workflow means design teams can focus more on optimizing their product for real user needs. 

That’s the practical value. Not replacing the designer, but moving the starting line. 

The Brand Consistency Feature

The most notable capability in Claude Design for teams working at scale is its ability to ingest a company’s codebase and design files to automatically apply brand guidelines to all outputs. 

For anyone managing campaigns across multiple channels or coordinating between an in-house team and external agencies, visual consistency is a constant problem. Guidelines get interpreted differently. Templates drift. New team members make judgment calls. 

Claude Design in acton.

Source 

Claude Design’s ingestion feature gives the tool a reference point so that outputs stay on-brand from the first draft. That matters most during ideation, when speed tends to come at the cost of consistency. 

What It Means for Non-Designers

Marketing teams, product managers, and founders regularly need to communicate visual ideas without a designer in the room. A competitive analysis needs a one-pager. A product pitch needs a slide. A new campaign concept needs something more than a paragraph in a doc. 

Claude Design lowers the barrier for producing those assets. You don’t need to know how to use Figma. You describe what you need, refine it through conversation, and export something usable. That’s a real change in how quickly a non-designer can get an idea out of their head and in front of other people. 

The caveat worth noting: Claude Design does consume tokens at a meaningful rate. For teams working within usage limits, it’s worth being selective about where you deploy it. Early-stage ideation and internal presentations are good candidates. Final client-facing deliverables probably aren’t, at least not without a designer’s review. 

Where Claude Design Fits in the Broader AI Design Space 

AI design tools have been proliferating fast. Canva has added generative features. Adobe has Firefly. Figma has integrated AI into its core workflow. Claude Design is entering a crowded category. 

What sets it apart is the conversational interface and the brand ingestion capability. Most AI design tools work from templates or style presets. Claude Design works from a description, and can theoretically hold the logic of your brand in memory for a session. That’s a different kind of flexibility. 

The Claude design interface.

Source 

The broader signal here is Anthropic’s push into enterprise productivity. Claude Design is part of a larger pattern: AI moving beyond text generation into the full range of knowledge work, including design, code, and creative assets. For UX practitioners and the teams they work with, that shift is going to keep accelerating. 

How to Start Using AI Design Tools in Your Workflow 

If you want to test Claude Design or similar tools in a practical context, here’s a straightforward approach: 

  1. Start with low-stakes assets. Internal presentations, early-round mockups, and concept sketches are good starting points. These are outputs that benefit from speed and don’t require production polish.
  1. Feed in your brand guidelines upfront. If the tool supports file ingestion, use it. Starting with your brand reference reduces back-and-forth in the iteration phase. 
  1. Treat outputs as drafts, not finals. AI design tools produce starting points. Plan for a designer review before anything goes to a client or ships publicly. 
  1. Test it as a brief-writing tool. Even if you don’t use the visual output directly, generating a mockup can help you communicate to a designer exactly what you’re looking for. That alone can cut revision cycles. 
  1. Track time saved in the ideation phase. The value is in speed, not in replacing human judgment. Measure it there. 

FAQs

What is Claude Design?

Claude Design is an AI-powered design workspace from Anthropic. It runs on Claude Opus 4.7 and lets users create mockups, prototypes, slide decks, and one-pagers through natural language prompts. Outputs can be exported to PDF, PPTX, Canva, and Claude Code workflows. 

Who is Claude Design built for?

Anthropic designed it for both professional designers who want to move faster during ideation and non-designers who need to produce visual assets without full design expertise. Marketing teams, product managers, and founders are natural users. 

Does Claude Design replace Figma or Canva?

No. Claude Design is positioned as a complement to existing tools, not a replacement. Anthropic is targeting the ideation phase of design work, not production-level output. Final deliverables will still benefit from a professional designer’s review and tools built for that purpose. 

What can Claude Design export?

Outputs can be exported to PDF, PPTX, Canva, or Claude Code workflows. This makes it straightforward to hand off early-stage work to existing design or development pipelines. 

What is the main limitation of Claude Design?

The tool consumes tokens at a meaningful rate, which is worth keeping in mind for teams working within usage limits. For now, it works best for early-stage ideation rather than high-volume production work. 

Conclusion

AI design tools are no longer a future consideration. Claude Design is live, and it’s targeting one of the most persistent inefficiencies in creative work: the time between having an idea and being able to react to it visually. 

For UX teams, the case for testing it is straightforward. If it shortens ideation cycles and helps non-designers communicate more clearly, it earns its place in the workflow. For the rest of us, it’s another signal that the tools available to knowledge workers are changing faster than most teams are adapting. 

Want to understand how AI tools fit into a broader digital marketing strategy? My guide to AI in marketing is a good place to start. You can also consult with the NP Digital team to figure out which emerging tools are worth prioritizing for your specific goals. 

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LinkedIn Is Cracking Down on AI Slop. Here’s What That Means for Your Strategy

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Key Takeaways

  • LinkedIn announced algorithm changes on May 20, 2026, targeting low-quality AI-generated posts, comments, and automation tools.
  • Content flagged as AI slop will not be removed but will be suppressed beyond a user’s immediate network, limiting reach significantly.
  • LinkedIn’s detection systems claim 94 percent accuracy in identifying generic AI content, though false-positive data has not been disclosed.
  • Content creation on the platform is up 14 percent year over year, driven largely by AI-assisted creation.
  • AI-assisted content is still welcome if it contains original perspective, expertise, or meaningful contribution.
  • Brands that combine AI efficiency with authentic subject matter expertise will gain a competitive distribution advantage as the algorithm matures.

LinkedIn has a problem, and it is one the platform created for itself.

After years of integrating AI writing tools directly into its product, LinkedIn is now fighting to contain the flood of low-quality AI-generated content those tools helped produce. The result is a content algorithm that is actively suppressing posts it identifies as “AI slop”: polished-sounding, generic, and hollow.

For brands and marketers who have been using AI to scale their LinkedIn presence, this is a signal worth taking seriously.

What LinkedIn Is Actually Doing

LinkedIn VP of Product Laura Lorenzetti announced the changes in a May 20 blog post. Three types of content are in scope: generic AI-generated posts that lack original perspective, bot-generated and generic AI comments, and automation tools that create AI content at scale.

A post from LinkedIn VP of Product Laura Lorenzetti.

Posts flagged by LinkedIn’s detection systems will not be removed, but their distribution will be suppressed. They will remain visible to a user’s direct connections and followers, but the broader recommendation engine will not amplify them. In practice, this means the platform reach that makes LinkedIn valuable for brand awareness and thought leadership becomes inaccessible to content that fails the originality check.

LinkedIn built its detection capability using an “AI solving AI” approach. Human editors annotated thousands of posts as either generic or original, those examples trained machine learning models to identify patterns at scale, and the system now runs on the feed continuously. The 94 percent accuracy figure comes from LinkedIn’s own testing, which means false positive rates are unknown. Some legitimate content will likely be caught in the net.

The business logic behind the crackdown is straightforward. LinkedIn sells premium subscriptions and advertising on the promise of a high-value professional audience. A feed filled with content nobody wrote undermines that promise, reduces engagement, pushes premium members out, and eventually costs advertisers. Protecting content quality is protecting the revenue model.

What Gets Flagged and What Does Not

LinkedIn has been specific about what it is targeting. Posts that feel generic or repetitive, even if they appear polished on the surface. Comments that simply summarize the post they are replying to without adding anything. Content created through bulk automation tools. Posts that use construction patterns associated with AI assembly, including phrasing like “it’s not X, it’s Y.”

What is explicitly not being targeted is AI-assisted content that contains original thinking. LinkedIn has been careful to draw this line. If a writer uses AI to research, structure, or polish a post built around a real professional insight, that post is welcome. What is not welcome is a post where AI is doing all of the intellectual work.

The practical distinction is whether the content contains something that only the author or their organization could provide. A post built around a first-party data point, a client case study, an executive’s direct experience, or a genuinely specific industry observation has something AI cannot fabricate. A post built around generic claims about industry trends or leadership principles, however well-written, has nothing that differentiates it from the thousands of similar posts already in the feed.

A Linkedin article post from NP Digital.

Why This Changes the Distribution Math

For teams that have been using AI to increase posting frequency on LinkedIn, the suppression mechanism changes the math significantly. Volume without quality now actively works against reach. A suppressed post still consumes the posting slot without delivering the visibility that made posting worthwhile in the first place.

The brands and individuals who will gain distribution advantage as this algorithm matures are the ones that understand what LinkedIn’s algorithm actually measures. The system does not detect AI-written text directly. It detects whether anyone cared enough to stop scrolling. Near-zero dwell time, no saves, and no meaningful comments are the behavioral signals that reduce distribution. Content without a specific professional insight at its core produces exactly those outcomes.

What Content Actually Passes LinkedIn’s Test

The behavioral signal LinkedIn’s algorithm reads is whether people engaged with the content meaningfully: dwell time, saves, substantive comments, and shares. These behaviors correlate strongly with one thing: whether the post contains something specific that only the author or their organization could provide.

A post built around a generic observation about industry trends, however well-written, will produce near-zero dwell time because it says nothing the reader has not already seen. A post built around a specific client outcome, a first-party data point, or a direct professional experience produces engagement because it contains information that does not exist elsewhere in exactly that form.

LinkedIn’s detection system cannot read AI-generated text as such. What it can detect is whether the behavioral signals suggest anyone cared enough to read past the first two lines. That is the actual test. The brands and individuals who will outperform in this environment are the ones producing posts that earn saves and real comments because they contain something worth returning to.

The practical audit for any LinkedIn content program is simple: read the last ten posts and ask, for each one, what specific information does this contain that only our brand could provide? If the answer is “nothing,” the content is at suppression risk regardless of how it was produced.

What to Do Now

Continue using AI as a production tool, not a thinking tool. Research, drafting, editing, formatting, and refinement are all appropriate uses of AI in the LinkedIn content workflow. The step AI cannot replace is identifying the specific professional insight that makes the post worth reading.

Prioritize writing LinkedIn articles with executive thought leadership and first-party perspectives. Our guide to covers how to build that kind of content at scale. 

An example of a Linkedin article from NP Digital.

These are the content types most resistant to AI suppression because they contain information that genuinely cannot be replicated at scale. An executive’s direct experience with a business problem, a client outcome with specific context, or an internal data point with genuine industry relevance will consistently outperform generic posts on the same topic.

Audit your current LinkedIn content mix. If a meaningful percentage of recent posts would pass for content from any other company in your category, that is a suppression risk worth addressing. The fix is not less AI. It is more raw material from the people and experiences that are actually specific to your brand.

FAQs

Will LinkedIn remove AI-generated posts entirely?

No. Posts identified as AI slop will be suppressed in the recommendation feed but remain visible to a user’s direct connections and followers. Removal is not currently part of the announced changes.

How does LinkedIn detect AI slop?

LinkedIn uses a machine learning system trained on human-annotated examples of generic versus original content. The system identifies patterns in language, structure, and engagement behavior. It claims 94 percent accuracy, though false-positive data has not been disclosed.

Is AI-assisted writing still allowed?

Yes, explicitly. LinkedIn has drawn a clear line between AI-generated content that lacks original perspective and AI-assisted content that uses AI tools to support human thinking. The latter is welcome. The former is what the suppression system targets.

Does this affect LinkedIn ads as well as organic content?

The announced changes target the organic recommendation feed. LinkedIn’s paid advertising products operate separately and are not currently in scope for these content quality restrictions.

Conclusion

LinkedIn’s crackdown on AI slop is a predictable consequence of the content inflation that AI writing tools have produced on every major platform. The platforms that survive on professional audience quality will protect that quality, even when it means limiting the reach of content created using their own tools.

For brands, the competitive advantage in this environment goes to those who treat AI as a production accelerator for content that starts with genuine expertise. The brands already doing this will benefit from the suppression of lower-quality content filling the same feeds. The brands relying on AI as a substitute for original thinking face a meaningful distribution penalty.

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The Page 2 Podcast: What SEOs must know to get chosen by AI

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The Page 2 Podcast: What SEOs must know to get chosen by AI



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Hosts & Guests

Alex Moss

Principal SEO

Jon Clark

The Page 2 Podcast Host

As AI agents reshape how people discover and buy products, SEO is evolving beyond rankings and clicks. In the latest episode of The Page 2 Podcast, Yoast Principal SEO Alex Moss joined Jon Clark and Joe DeVita to explore what it takes for brands to be selected by AI agents, from structured data and entity relationships to markdown, llms.txt, and creating a machine-readable source of truth.

In this episode, you’ll learn:

  • What it means to optimize for AI agents instead of search clicks
  • Why markdown can outperform HTML for LLMs
  • Whether llms.txt is worth implementing today
  • How entity maps and structured data help AI understand your content
  • What businesses should prioritize to prepare for the next generation of AI-powered search and commerce

Whether you’re just starting to explore AI search or already experimenting with new optimization standards, this conversation offers practical insights into where SEO is headed and how to prepare for what’s next. Give it a listen to future-proof your SEO strategy for the age of AI.

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The SEO Update by Yoast – August 2026
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How AI visibility adds context to PPC performance

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How AI visibility explains PPC performance

AI visibility metrics help uncover pre-click influences that might not be obvious from search terms, conversion tracking, landing page analysis, or other conventional data points. They add context that helps explain why campaigns attract the right customers, the wrong customers, or no customers at all.

Performance marketers are paid to find the most valuable customers with the most efficient spend. The baseline is understanding what happened after someone searched, clicked, or converted. But focusing exclusively on cleaning up existing demand is a short-sighted goal.

AI experiences (and what goes into serving them) are just as important to performance workflows because AI can shape what customers know, which brands they consider, and the language they use before they ever reach an ad or website.

What AI visibility metrics reveal

AI visibility metrics help answer two questions:

  • What information did an AI system retrieve to support its response?
  • Was your brand part of the information that shaped that response?

Three signals are especially useful:

  • Grounding queries show the retrieval searches AI systems use to gather supporting information and the broader topics your brand appears in.
  • Citations show when your content is referenced in an AI-generated response.
  • Share of authority shows how much citation activity belongs to your domain compared with other cited domains for the same topic or query set.

See exactly how your competitors win.

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How to put AI visibility to work

Grounding queries show how AI interprets human intent

Search terms show what a person typed. Grounding queries show the information an AI system retrieved to help answer that person’s question.

A single prompt can generate several grounding queries covering comparisons, pricing, reviews, product details, availability, implementation questions, or other supporting topics. Those queries show how AI translates a human need into retrievable information.

It gives you another way to evaluate whether landing pages, product descriptions, and campaign messaging communicate the right value.

If AI systems consistently associate your brand with services you don’t offer, audiences you don’t want, or use cases that don’t convert well, that mismatch may eventually show up in paid campaigns as traffic that looks relevant but performs poorly.

For example, a B2B company offering executive coaching may not want to be interpreted the same way as a company offering tactical sales training. Both ideas may be semantically similar, but they can attract different buyers, budgets, expectations, and conversion paths.

If grounding queries consistently associate the company with lower-value training searches instead of higher-value advisory or coaching needs, that’s a signal worth investigating.

The useful signal isn’t whether one phrase is universally better than another. It’s whether grounding queries, search terms, landing page behavior, and conversion quality point to the same interpretation of the same offer.

What to do with this insight 

If you see a lot of relevant grounding queries, that can be a strong opportunity to test AI-powered query matching with Performance Max, AI Max, or other AI-supported campaign types.

At minimum, grounding queries can inform keyword testing, search themes, creative ideas, and landing page updates. The key is to treat grounding queries as inputs, not instructions.

A grounding query isn’t the same as a keyword. It’s a clue about how AI systems interpret intent. Before acting on it, ask:

  • Does this query reflect a product or service we actually want to sell?
  • Does it match the customers we want more of?
  • Do we have a landing page that supports this intent?
  • Would this work better as a keyword, search theme, creative test, or content update?
  • Can we measure whether the test improves conversion quality?

If grounding queries and search terms overlap in valuable ways, that may validate that AI systems and customers are interpreting your offer similarly

If they don’t overlap, investigate before changing bids or budgets. The issue may be messaging, landing page clarity, or content gaps rather than campaign settings.

Dig deeper: SEO vs. PPC vs. AI: The visibility dilemma

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See terms.

Citations and topics help you preview how AI understands your brand

A citation doesn’t mean you won the customer, the final answer, or the conversion. It means your content helped shape the AI experience. That matters because consideration can happen before a measurable click.

If your brand is cited for topics that align with your paid campaigns, your content and campaign messaging may reinforce each other. If your brand is cited for topics that don’t match what you sell, who you serve, or where you win, that may explain why some campaign traffic looks relevant on the surface but doesn’t convert well.

Topics add another layer by showing the themes, attributes, and categories AI systems associate with your brand. That can be especially useful when you’re trying to understand whether AI systems accurately represent your brand.

For example, if a cybersecurity platform wants to be known for enterprise identity protection but AI visibility reporting consistently associates it with small-business antivirus comparisons, that mismatch could be leading to lower-quality “conversions” that translate into bad data for the ad platform. What looks like a campaign settings or targeting issue could actually be a phrasing or content problem.

This is why landing page content matters. AI-powered campaign features such as final URL expansion, asset optimization, and broader matching systems also rely on how your brand, products, services, and pages are interpreted.

If AI visibility data shows that AI systems misunderstand your brand, there’s a reasonable chance campaign automation could inherit some of that confusion.

What to do with this insight

If citations and topics are misaligned, audit your landing page content before assuming the issue is bidding, budget, or audience targeting.

Look at how your priority pages describe:

  • The problems you solve and whether you attempt to tackle more than one.
  • The proof points that support the claims made in your advertising.
  • The customer types you want more of based on how you describe your services.

Then compare that language with the topics where AI systems cite or associate your brand.

If AI systems are pulling your brand into the wrong topics, tighten the content. Make the product category, audience, proof points, and next step easier to understand. If the right topics appear but your citations are weak, look for places where your content may be too thin, too generic, or missing the evidence needed to be useful.

Better content can strengthen paid media campaigns because AI-powered campaign tools are increasingly tasked with interpreting pages, assets, and customer context. The clearer your landing pages and content are, the easier it is for those systems to understand where your offer is relevant. 

Dig deeper: 4 CRO strategies that work for humans and AI

Share of authority reveals where competitors are beating you in AI recommendations

Citations show whether your content is referenced in a given grounding query.

Share of authority shows how much citation activity belongs to your domain compared with other cited domains for the same topic or grounding query. This can be a useful competitive signal because it shows where other brands may be influencing the customer’s research journey before a search, click, or conversion happens.

If competitors are cited more often on a priority topic, they may be doing a better job answering the questions customers ask before they convert. This might mean:

  • More accessible landing pages.
  • Better proof points, such as reviews and awards.
  • More useful product information that aligns with their feed.

Fundamentally, ask:

  • Are competitors doing a better job proving their value in the places AI systems use to shape recommendations?

If the answer is yes, the issue may not be that your campaigns are broken. Your content may not be giving AI systems enough clear, useful, or verifiable information to understand why your brand belongs in the recommendation set.

What to do with this insight 

Use share of authority to identify where competitors are outperforming you on topics that matter to the business. Then invest in the content and landing page experiences that prove your value.

That might mean:

  • Building new landing pages for high-value use cases.
  • Expanding product or service details to include ideas mentioned in grounding queries.
  • Adding clearer proof points, such as reviews and awards.
  • Updating creative and messaging to reflect what customers and AI systems are already asking about.

Not every share-of-authority gap deserves action. If you’re underrepresented on a topic that doesn’t matter to your business, that may be fine.

But if you’re underrepresented on a high-value category, core product, branded initiative, or use case that your paid campaigns depend on, that gap deserves attention.

The performance question isn’t: “How do we become visible everywhere?” A better question is: “Where do we need stronger evidence so qualified customers are more likely to consider us?”

Every click they win is a customer you lose.

See where competitors are investing, which keywords drive their results, and how to capture more of the market.

See who’s stealing your traffic

What AI visibility adds to PPC reporting

AI visibility reporting isn’t a replacement for conversion reports, bidding reports, placement reports, or behavioral analytics. Those tools still tell you whether campaigns are working and where performance is coming from.

AI visibility adds a different perspective. It helps explain how customers, AI systems, and competitors interact with information before traditional performance metrics exist.

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Google expands Data Manager API with smarter audience management

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Google updated the Data Manager API with new audience management tools, more flexible data ingestion and expanded support for user-provided address data, making it easier for developers to maintain Customer Match lists and improve data quality.

The release is aimed at reducing manual work while providing better visibility into data issues that don’t require an ingestion request to fail.

What’s new. The headline addition is a new RemoveAllAudienceMembers method, which allows developers to clear an entire audience list in a single operation. An optional timestamp parameter also lets advertisers remove only members added before a specified date, making full audience refreshes much easier.

Google has also introduced field-level ingestion warnings. Rather than failing an entire request when optional fields contain invalid data, the API now processes valid records while returning detailed warnings that identify the problematic fields and explain why they failed validation.

The update also expands the address information that can be sent to Google Analytics destinations. Developers can now include street address, city and state or province alongside existing fields such as name, postal code and region, while user-provided data can also satisfy identifier requirements for certain multi-source events when other identifiers aren’t available.

Google has additionally released new AI agent skills in its Google Skills GitHub repository to help developers build Data Manager API integrations more efficiently within AI-assisted coding environments.

Why we care. The update removes several friction points for developers managing first-party data. Audience refreshes become simpler, ingestion issues are easier to troubleshoot without interrupting workflows, and the expanded address fields provide more flexibility when sending user data to Google Analytics.

Bottom line. The latest Data Manager API release streamlines audience management, improves error handling and expands data collection capabilities, giving developers more efficient tools for managing customer data across Google Ads, Display & Video 360 and Google Analytics.

Every click they win is a customer you lose.

See where competitors are investing, which keywords drive their results, and how to capture more of the market.

See who’s stealing your traffic

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Microsoft Clarity adds branded and non-branded AI queries

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Microsoft has updated its analytics tool, Clarity, to add branded and non-branded breakdowns to the AI Citations dashboard and AI reports. Clarity lets you filter branded versus non-branded information across query analysis and filtering.

What Microsoft said. “To make that analysis easier, Microsoft Clarity now adds branded query segmentation to the AI Citations dashboard,” Microsoft wrote. “You can now distinguish branded and non-branded grounding queries AI systems use to look up supporting information for a response,” Microsoft added.

What it looks like. Here is a screenshot from the Microsoft Clarity blog of these new updates:

What is new. Microsoft added branded vs. non-branded view across query analysis and filtering to the Clarity reports including:

  • Branded labels in the queries card: Individual queries are now clearly marked as branded in the queries view, so you can quickly identify brand-specific queries and understand what the AI systems looked up at a glance. 
  • Share of Authority breakdown by query type: The Share of Authority card now breaks out results by branded and non-branded queries, giving you a clearer view of where your authority is strongest: queries that mention your brand versus more general queries.  
  • Branded and non-branded filters: Filter dashboard data by branded or non-branded queries to compare how your visibility performs when an AI system looks up your brand directly versus broader, more general topics. 
  • More precise citation analysis: By separating brand-led demand from generic discovery, you can better assess brand strength, identify discovery and consideration opportunities, and interpret changes in citation performance with greater confidence. 
  • Branded labels in the queries card: Individual queries are now clearly marked as branded in the queries view, so you can quickly identify brand-specific queries and understand what the AI systems looked up at a glance. 
  • Share of Authority breakdown by query type: The Share of Authority card now breaks out results by branded and non-branded queries, giving you a clearer view of where your authority is strongest: queries that mention your brand versus more general queries.  
  • Branded and non-branded filters: Filter dashboard data by branded or non-branded queries to compare how your visibility performs when an AI system looks up your brand directly versus broader, more general topics. 
  • More precise citation analysis: By separating brand-led demand from generic discovery, you can better assess brand strength, identify discovery and consideration opportunities, and interpret changes in citation performance with greater confidence. 

Why we care. Being able to break down and filter your analytics reports by branded and non-branded queries can help you zone in to the data that matters to you. Having more clarity on how people find your content through AI search and chat experiences are important and useful. This update gives you a little more information to work with when it comes to understanding your AI visibility.

Be the brand AI recommends.

See where your brand appears in AI search, where competitors are winning, and what it takes to become the answer AI recommends.

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AI Search Data Is Now in Google Search Console: Here’s What You Need to Know

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Key Takeaways

  • Google launched dedicated Generative AI performance reports in Search Console on June 3, 2026, initially rolled out to a subset of UK-based sites.
  • The report tracks impressions across AI Overviews, AI Mode, and AI features in Discover, broken down by pages, countries, devices, and dates.
  • Data begins from May 18, 2026; there is no historical backfill.
  • Click data is not included in the current version, which is the most significant limitation.
  • Google also introduced an opt-out toggle allowing sites to block content from AI features without affecting traditional organic rankings.
  • AI visibility and traditional organic visibility are now two distinct, separately measurable channels.

For two years, AI search performance has operated as a black box. Brands could see that AI Overviews were growing, that click-through rates were falling, and that something had changed in how Google surfaced content. But first-party data on what was actually happening inside AI features was not available.

Google changed that on June 3, 2026.

What Google Actually Launched

Google’s Search Generative AI performance reports give site owners a dedicated view inside Search Console showing how their content appears within generative AI features. Previously, any traffic or impressions from AI Overviews and AI Mode were folded into the standard Performance report with no way to isolate them. That separation is now possible.

GSC's Search Generative AI performance reports.

Source

The report covers five dimensions: impressions (how often your URLs appeared in AI features), pages (which specific URLs were cited), countries (geographic breakdown of AI visibility), devices (for Search results), and dates (with hourly through monthly granularity).

The rollout launched June 3, 2026, initially for a subset of UK-based site owners. This is a direct response to the UK Competition and Markets Authority mandate, which requires Google to share this data under the Digital Markets Act. A global rollout is planned but no timeline has been confirmed. If your Search Console does not yet show the report, it is coming.

Alongside the reporting launch, Google introduced an opt-out toggle that allows site owners to exclude their content from AI Overviews, AI Mode, and Discover AI features entirely. Critically, opting out carries no organic ranking penalty. Google has confirmed the toggle will not be used as a ranking signal for traditional search results. The toggle takes effect from June 17, 2026.

The opt-out toggle in GSCS.

Source

What the Data Does and Does Not Tell You

The most significant limitation of the current report is that it tracks impressions only. There is no click data, no CTR, no average position, and no query-level breakdown. You can see that your content appeared in an AI feature, but not whether anyone clicked through to your site as a result.

For performance marketers accustomed to building decisions around clicks and conversion, this creates a measurement gap. AI systems are designed to answer questions directly, which means click rates from AI features are likely structurally lower than click rates from traditional results. An impression inside an AI Overview does not confirm a visit, and given how AI Overviews behave, it may not produce one at all.

The right way to read this data, at least in its current form, is as a resonance signal. A page that earns high AI impressions is content the model finds worth citing. Cross-reference AI impressions against the organic clicks that same URL earns in the standard Performance report, and you get a genuinely useful picture: pages that perform strongly in both channels are your highest-value content assets. As one analysis put it, high AI impressions and high organic clicks on the same page is the clearest signal of what non-commodity content actually looks like. For a deeper look at how to interpret and act on AI brand visibility data, see our recent breakdown.

The data also starts from May 18, 2026, which sits inside the May 2026 core update window. Early trend interpretation should account for that overlap, since ranking shifts from the update will be mixed into the initial AI impression patterns.

Reading AI Impressions as a Strategy Signal

The absence of click data in the current report does not make AI impressions useless. It changes how they should be read.

An impression inside an AI Overview or AI Mode response means Google’s system selected your content as a grounding source for its answer. That selection reflects the same quality signals that drive strong traditional rankings: factual accuracy, topical authority, clear structure, and relevance to the specific question being answered. Pages that consistently earn AI impressions are your content assets that the model finds most citable.

AI impressions in Google Search Console.

Cross-referencing AI impressions with organic clicks from the standard Performance report creates a genuinely useful diagnostic. Pages with high organic clicks and high AI impressions are performing in both paradigms simultaneously. 

These are your non-commodity pages: content that earns traffic from users who click through and citation from AI systems answering related questions. Pages with high AI impressions but low organic clicks may indicate content that answers queries well enough to be cited but does not generate sufficient click intent on its own. These are worth examining for conversion optimization. Pages with low AI impressions and high organic clicks may represent ranking-driven traffic that is increasingly vulnerable as AI Overviews expand into their query categories.

Should You Opt Out of AI Features?

Almost certainly not, for most brands.

The business case for opting out is narrow. Publishers with content-licensing models, paywalled material, or specific legal reasons to restrict AI citation may have legitimate reasons to consider it. For the vast majority of brands, opting out means losing a growing visibility channel, with no ranking benefit to offset it.

The opt-out toggle is most useful as a signal that Google has acknowledged the tension between feeding AI systems with publisher content and compensating those publishers for it. Having the control is meaningful. Using it defensively, without a clear strategic rationale, is not recommended.

What to Do Now

Even before your Search Console account gains access, prepare the reporting infrastructure now.

Define how AI impressions will feed into your performance reporting.  AI visibility and traditional organic visibility are now distinct channels, and they need separate measurement frameworks. A page ranking well in traditional search is not the same as a page earning AI citations, even though both matter.

Identify which pages are likely candidates for AI citation in your current content mix. Content that directly answers specific questions, uses clear structure, and is factually accurate and current is more likely to surface in AI features. Those pages should be audited for completeness and optimized before the report gives you data to react to.

Set a baseline as soon as access arrives. The report currently has no historical data before May 18, 2026, which means the earlier you establish your first benchmarks, the more useful comparative data you will have going forward.

FAQs

When will I get access to the new AI report in Search Console?

The current rollout is limited to a subset of UK-based site owners. Google has said a global rollout is planned but has given no specific timeline. Monitor your Search Console account for the Generative AI section to appear.

Can I see which queries trigger AI impressions for my content?

Not in the current version. The report does not include query-level data. This is one of the most significant gaps and may be addressed in future updates.

Does opting out of AI features help my rankings?

No. Google has confirmed that the opt-out setting will not be used as a ranking signal for traditional organic search. Opting out affects AI Overviews, AI Mode, and Discover AI features only.

Should I treat AI impressions the same as organic impressions?

No. AI impressions indicate your content was cited inside a generative feature, not that a user was shown your link in a traditional results format. The downstream behavior is different, click rates from AI features are likely lower, and your measurement framework should reflect that distinction.

Conclusion

AI search visibility has been growing for two years with no way to measure it directly. The new GSC Generative AI performance report closes that gap, partially. Impressions data without clicks is an incomplete picture, but it is a meaningful start. The brands that build reporting frameworks around this data now, before a global rollout makes it standard practice, will be better positioned to interpret performance and make decisions as the reporting matures.

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Ana Kostic shared why “best practice” cost her client 40% of revenue.

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Watch this video on YouTube

On the latest episode of PPC Live the Podcast, Ana Kostic shared how one account restructure taught her a lesson that still shapes how she manages paid media today: successful PPC isn’t about building the perfect campaign structure—it’s about protecting the business behind it.

The mistake: A “perfect” account restructure

Early in her career, Kostic inherited a Google Ads account with a messy structure. Determined to apply PPC best practices, she rebuilt the account from the ground up, confident that cleaner campaigns and better keyword organisation would improve performance. Instead, traffic and sales dropped by around 40%.

The hidden cost of starting over

The issue wasn’t the new structure itself—it was wiping away years of historical performance data that Google’s systems had learned from. The account eventually recovered, but only after roughly two and a half months, with the full benefits taking closer to six months to materialise.

The lesson wasn’t about Google Ads

For Kostic, the biggest takeaway wasn’t technical. It was recognising that businesses can’t afford major revenue dips while platforms relearn campaign performance. “You have to think about the business first,” she said.

Ask business questions before platform questions

Today, Kostic starts every onboarding by understanding the business rather than the account. She asks about cash flow, margins, growth goals and how much short-term disruption the company can realistically absorb before recommending major structural changes.

Why slow beats perfect

Rather than replacing campaigns overnight, Kostic now introduces changes gradually, allowing new structures to learn while existing campaigns continue delivering results. Her philosophy is simple: “We like it slow and boring.”

Communication is part of optimisation

Kostic also credits her former manager for helping navigate the difficult conversations that followed. Instead of assigning blame, the agency focused on transparency, created a recovery plan and supported both the client and the team until performance stabilised.

PPC doesn’t stop inside Google Ads

One of Kostic’s biggest pieces of advice is to spend more time talking to sales teams. Those conversations often reveal the language customers actually use, helping advertisers build better campaigns than platform data alone can provide.

AI doesn’t change the fundamentals

While she’s a strong advocate for products like Performance Max, Kostic says AI shouldn’t replace good decision-making. Advertisers should still put guardrails in place, test gradually and prioritise business stability over chasing every new feature.

Bottom line

The biggest PPC mistakes aren’t always campaign mistakes—they’re business mistakes. For Kostic, rebuilding an account too aggressively became the experience that transformed her from a platform specialist into a business-first strategist.

See exactly how your competitors win.

Uncover the keywords, ads, landing pages, and strategies driving your competitors’ paid search success—and find your next opportunity to outperform them.

Analyze your competitors

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How to Get the Most Out of the New AnswerThePublic (2026 Guide)

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If you just logged into the new AnswerThePublic for the first time, there’s a lot more going on than the tool you might remember.

The version most people know was a keyword visualization tool. Type in a topic, get a wheel of questions people are searching. That version was useful. The new AnswerThePublic is a different category of product entirely.

It’s an AI content engine. You give it your website. It understands your business. It surfaces the keyword opportunities most likely to drive results for you specifically. It researches the competition. It writes the article. It publishes it to your WordPress site. And it repeats that process on whatever schedule you set.

This guide walks you through every part of the product in the order you’ll actually use it, so by the end, you’ll know where to start, what each feature does, and how to get your first published article live.

Two Ways to Start a Search

AnswerThePublic gives you two entry points, and the one you choose shapes everything that comes after.

The first is Analyze My Website. You drop in your URL, and AnswerThePublic reads your site to understand your business before you search for anything. The second is Search Keywords, the classic mode. Type in a keyword, get insights.

Both work. But if you use your website URL, every keyword idea, every content recommendation, and every article the tool generates gets filtered through your actual business context. Instead of generic volume-ranked results, you see the opportunities that actually move the needle for your site specifically.

For most users, starting with Analyze My Website is the right call.

The AnswerThePublic homepage.

The Business Summary: The Brain Behind Everything

When you analyze your URL, AnswerThePublic builds a Business Summary for you automatically. It includes your business name and type, your target customers, your key features and unique value proposition, your geographic focus, and the topics you should focus on for content.

You can view and edit this anytime, from the Business Summary button at the top right of the Suggested For You page, or inside Settings. This profile is what makes every recommendation specific to you rather than generic. If something’s off about how we characterize your business, fix it. Everything downstream improves when you do.

A completed Business Summary profile page, with the business name, target customers, key features, and topic focus areas visible.
A completed Business Summary profile page, with the business name, target customers, key features, and topic focus areas visible.

The Left Sidebar: Your Four Main Surfaces

Home is your search history. Every keyword you’ve ever searched, across every source: Google, Bing, ChatGPT, Gemini, YouTube, Amazon, and Instagram. Filter by region, language, provider, or date to find anything fast.

Suggested For You is your content command center. It’s where most users should spend most of their time.

Content Schedule is your publishing calendar. Set how many articles you want generated per week or per day, drag and drop to reorganize, and see status, ranking position, and search volume next to each item so you’re scheduling with context, not guessing.

All Content is your full article library. Every article generated, published, or sitting in queue. Searchable and sortable.

The left sidebar with all four sections visible: Home, Suggested For You, Content Schedule, and All Content.
The AnswerThePublic sidebar.

Suggested For You: Where Most Users Should Spend Most of Their Time

This page is designed to eliminate the blank-page problem. When you land here, you see three things.

At the top: AI-generated content ideas, each tagged with one of three opportunity signals: Best for AI Visibility, Best Short-tail Opportunity, or Best Long-tail Opportunity. Each idea includes a reason why it’s a good opportunity for you specifically, based on your Business Summary. These are often the hidden gems that most keyword tools would never surface because they require understanding your business, not just your niche.

Below the suggestions: The classic AnswerThePublic keyword wheel, now grouped by topic cluster so related ideas stay together visually.

Below the wheel: A full table of your next 50 content ideas, ranked, categorized, and ready to generate. Click any one to start the content process.

AI suggested content ideas from AnswerThePublic
AI suggested content ideas from AnswerThePublic
AI suggested content ideas from AnswerThePublic

Clicking Any Keyword Opens the Full Research View

When you click into a specific keyword, you get a complete research page in a single view: search volume, CPC, and your Content Studio rank for that keyword.

Then the Top Ideas wheel, now with two layers. The inner ring shows what people are asking AI models about this topic. The outer ring shows what they’re typing into search engines.

Below that, the AI Prompts table: every AI prompt related to your topic, paired with three signals new to this version:

  • Intent: what the user is trying to accomplish with this query
  • Sentiment: the emotional tone of how AI models are answering this question
  • Brands: which companies are being mentioned in AI responses for this query

Keep scrolling and you’ll see Organic Searches from Google and Bing, a People Also Ask mindmap you can expand into a full tree view, Social Media results from YouTube, TikTok, and Instagram, and Shopping data from Amazon. Every source. One view. No tab-switching.

he keyword detail research page for a sample keyword, with the Top Ideas wheel (inner AI ring and outer search ring visible), the AI Prompts table with Intent/Sentiment/Brands columns, and the Organic Searches section below.
he keyword detail research page for a sample keyword, with the Top Ideas wheel (inner AI ring and outer search ring visible), the AI Prompts table with Intent/Sentiment/Brands columns, and the Organic Searches section below.

One-Click Content Generation from Anywhere

You can generate content with one click from anywhere on Suggested For You, and from anywhere on a keyword detail page. From the top suggestions. From the keyword wheel. From the table. From a People Also Ask node.

Before you hit generate, you get a review screen to check the target keyword, edit the content idea, pick a title, and see why this topic is relevant to your business.

When you click Generate Now, Content Studio runs a five-step editorial pipeline:

  1. Researches the top-ranking pages for your keyword
  2. Pulls the facts and statistics that matter
  3. Builds an outline based on what’s already winning in the SERP
  4. Writes the article in your brand voice
  5. Refines it for natural language and flow

This isn’t a raw AI dump. It’s a structured editorial process trained on real ranking data. What used to take an SEO team days, Content Studio produces in minutes.

Full Control Inside the Article Editor

Once your article is generated, you have full control. Add images. Replace the cover image with the AI generator or upload your own. Rewrite any paragraph by hand.

And this is the capability most new users don’t discover: select any section and ask the AI to regenerate just that part. Not the whole article. Just the paragraph or sentence that needs a different angle. That single feature alone saves hours of editing in a typical workflow.

A plagiarism score runs in the background the entire time. By the time you hit publish, you know the content is uniquely yours.

The Ubersuggest article editor.
The AnswerThePublic article editor.

The Right-Panel Tabs

Overview shows your target keyword, monthly searches, difficulty, plagiarism score, word count, and all internal and external links in one place.

SERP shows the top Google results currently ranking for your keyword, without opening a new tab.

Research shows every fact the article cited, with numbered sources and clickable links. Verify any claim before you publish.

Exporting, Saving, and Publishing

From the top of the editor: export to PDF, export to Markdown, save a draft, or publish directly. The Share button auto-generates captions for Twitter/X, LinkedIn, Instagram, and email, following best practices and character limits for each platform. Write once, distribute everywhere.

Settings: Set It and Forget It

Project Settings in AnswerThePublic.

Publish Controls connects your WordPress site. Choose Draft or Published as your default, or enable Auto Publish to send new articles live without manual review. For teams running a daily content cadence, this is transformative.

Image Styles gives you sixteen visual templates (corporate illustration, cartoon, photographic, and more), or let the AI Style Picker choose the best style for each article based on the topic.

Brand Voice lets you pick from templates like The Straight Shooter, The Grounded Guide, or The Curious Storyteller, or build a fully custom voice profile. This is what makes generated articles sound like your brand instead of a generic AI output.

Article Generation controls structure: set length from 500 to 5,000 words, toggle external links, add your sitemap for automatic internal linking, and schedule daily generation at a specific time.

Business Summary is the editable brand profile. Come back here anytime your business evolves: a product launch, a new service, a new audience segment.

Localization sets your content language and target country. Content quality improves significantly when these match your actual audience.

The Fastest Path to Your First Published Article

Four steps in order:

  1. Add your website on first search, so the Business Summary gets built automatically.
  2. Go to Suggested For You and pick an idea tagged Best for AI Visibility or Best Long-tail Opportunity.
  3. Generate your first article with one click. Review the title and keyword, then hit Generate Now.
  4. Connect WordPress in Settings under Publish Controls and publish.

The Bigger Picture

The thesis behind the new AnswerThePublic is simple: your website already tells us what your business is, what you sell, and who you’re selling to. Your customers are already searching for it, in Google, in AI tools, on YouTube, on Amazon.

The gap between what your customers are searching for and what you’ve published is your content opportunity. AnswerThePublic closes that gap automatically.

You just needed a tool smart enough to connect the dots.

Frequently Asked Questions

What’s different about the new AnswerThePublic vs the original?

The original was a keyword visualization tool. The new version adds AI content generation, a Business Summary that personalizes every recommendation to your site, a publishing calendar, WordPress auto-publish integration, and a full article editor with plagiarism scoring. It’s a complete content production system built on top of the original keyword research foundation.

Does AnswerThePublic write the full article, or just an outline?

It writes the full article following a five-step editorial pipeline: competitor research, fact gathering, outline, drafting, and refinement. You can edit any section manually or use selective regenerate to rewrite specific paragraphs.

Can I use AnswerThePublic if I don’t have a WordPress site?

Yes. Export articles as PDF or Markdown, or copy and paste the content directly to any platform.

Does it work in languages other than English?

Yes. AnswerThePublic supports multiple languages and regions. Set your content language and target country under Localization in Settings for best results.

The new AnswerThePublic is live, and you can try it free, no credit card required. Try the new AnswerThePublic.

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