How To Optimize Your Site for AI Visibility and Agentic Features

When I speak with SEOs about AI search, I usually see three types of reactions:

• Some treat it as traditional search with a new interface.
• Others think we need to throw away the SEO playbook and restart entirely
• And some are still waiting to see if it becomes “important enough” to take action

The reality is more nuanced. AI search doesn’t replace SEO — instead, it expands what SEO needs to influence.

The fundamentals still matter, but the way we prioritize them needs to evolve.

In this article, I will share a cost-effective strategy to boost AI search visibility and prepare for agentic commerce.

P.S. This is part two of an article repurposed from my Moz webinar. I recommend reading part one if you want the full context.

The ten characteristics I covered in part one help you diagnose what may be limiting your AI visibility, but diagnosis is only useful if it leads to prioritization. 

The challenge now is knowing where to focus first, how to avoid wasting resources chasing isolated prompts, and reporting progress in a way that connects visibility to business impact.

That’s where these three principles come in.

While traffic remains relevant, it is not a sufficient indicator of visibility, influence, or commercial success in AI search.

Users can now discover, compare, and validate your brand within AI platforms before visiting your site. 

While some journeys still result in clicks, AI influence often occurs earlier and is more difficult to attribute. Consequently, measured AI referral traffic should be viewed as a minimum baseline of impact, not the maximum.

Instead of evaluating AI search success mainly through sessions, separate your reporting into two groups:

Tier 1: Core business KPIs (the real north stars)

• Revenue share from LLMs
• Purchases from LLMs
• AI conversion rate
• AI-assisted conversions

Tier 2: AI visibility and referral metrics (directionally helpful)

• AI share of voice per platform
• Mentions and citations in relevant AI answers
• Sentiment of AI mentions

From there, measure your AI presence using these KPIs:

• Prompt coverage: Are we showing up where we need to?
• Recommendation rate: Are we being endorsed, or just included?
• Linked citation rate: Is this visibility capable of driving visits or purchases?
• Comparative win rate: Are we winning the shortlist?
• Representation accuracy: Are we being described correctly?

Track prompts at a topical and journey level, not as isolated one-off checks. AI answers may vary, so the goal is to identify recurring patterns across representative prompt sets. 

Next, build prompt groups by product line, audience, journey stage, market, and commercial priority. Then, monitor your brand presence across those groups.

You can do this with the Moz AI Research toolkit. Click Prompt Suggestions under AI Research and enter a topic.

The first thing you’ll see is an organic snapshot of the topic, including monthly volume, search intent, and prompts related to the topic.

Scroll down to see a list of suggested prompts relevant to your topic. Select the prompts you want to track and click Track prompts in AI Visibility.

You’ll be directed to the Moz AI Visibility tool, where you can set up your dashboard. Enter your brand name alongside three competitors. 

You also have the option to add prompts manually if you want to track specific prompts you’ve already identified. 

On the dashboard, you can choose AI engines like ChatGPT or Gemini to compare brand performance across tracked topics.

Whatever tool you use, the main thing is to track topical coverage versus single-prompt performance, especially when accounting for query fan-out.

Use these metrics to assess AI business impact

Measuring AI business impact requires separating signals by confidence level. This is important because not all AI visibility can be directly attributed as a referral in analytics.

Metrics include:

Observed (highest confidence, lowest coverage): 

How many users clicked and converted from an AI answer? 
These come from platforms that pass a referrer or UTM parameter, giving you directly attributable data. 

Start by tracking these to measure and assess your AI impact:

• AI sessions by platform, landing page, and device
• Top AI landing pages
• Engagement rate and average engagement time versus your organic benchmark
• AI conversion rate and revenue per visit, segmented by platform, where volume allows
• AI-assisted conversions

I highly recommend building your own observed tracking layer using data from your CRM and analytics to segment AI-driven activity.

Dana DiTomaso has a step-by-step guide for AI traffic analysis in GA4 that includes an embedded video walkthrough.

Proxy (medium confidence, broader coverage)

Proxy signals complement observed data by correlating with AI influence. These directional metrics from internal analytics or external modeling tools help track AI traffic and visibility without direct proof.

They help answer questions like: 

• Is there evidence that users are seeing us in AI answers even when they don’t click?
• How does our AI presence compare to competitors, and which prompts are driving AI traffic?

Start with what you own:

• Branded search lift via GSC
• Direct and unattributed traffic lift to pages surfacing in AI answers
• Demand for frequently surfaced pages

• Survey-based discovery questions added to signup, demo, or post-purchase flows

  Then layer in external signals from Bing Webmaster Tools and social listening on brand mentions.

  Survey questions are worth prioritizing. Add a question at the conversion point (demo requests, contact forms, etc) to understand how people find you, whether through LLMs, LinkedIn, or other sources. 

  A rising “Yes” rate among users attributed to direct or branded organic is one of the strongest first-party proxies that AI influence exists beyond what analytics can directly observe.

  Modeled (lowest confidence, planning only)

  These estimates apply assumptions to observed and proxy data, covering influenced pipeline, revenue, and incrementality. Use them to build an investment case, but never as definitive proof of performance.

  They help to answer questions like: If we assume X% of branded search lift is AI attributable, what’s the implied pipeline?

Do not blend these three layers into one single “AI impact” number. Report what’s observed, inferred, and modeled separately, with clear confidence levels and assumptions.

Tying your AI search presence and readiness status with these business KPIs turns optimization into accountable, ROI-positive work.

The era of mapping a group of queries to a single page is gone. Because of query fan-out and how content gets synthesized, AI can extract from any page across your site taxonomy. 

Your optimization work needs to go beyond ranking a landing page for a target query. The goal is to build topical and decision-stage coverage that makes your brand easy to understand, compare, cite, and recommend.

Here’s how to implement:

Cover the full customer journey

Every stage needs content that answers the questions users and AI systems need to resolve:

• Awareness: Educational guides, explainers, research, thought leadership, FAQs. 
• Consideration: Comparisons, alternatives, reviews, use cases, templates, benchmarks, buying guides. 
• Decision: Product details, pricing, availability, integrations, demos, case studies, compliance information. 
• Post-purchase support: Documentation, tutorials, troubleshooting content, support FAQs, community answers. 

Start by prioritizing topics already driving AI visibility or organic demand. Then, expand into related questions and user constraints to capture adjacent opportunities.

Expand keyword maps with decision-constraint matrices

Keyword maps are still useful, but for AI search, they need to include decision constraints.

Users are moving beyond generic terms like "best running shoes" and instead using specific constraints such as foot type, budget, location, terrain, and durability.

To earn visibility in these journeys, your site needs to make those attributes explicit, consistent, and easy to extract. 

Optimize by including:

• Product or service attributes clearly displayed on the page
• Structured data where relevant
• Comparison modules
• Natural-language filters
• Buying guides or use-case pages
• Internal links between related use cases and constraints
• Consistent terminology across relevant pages and supporting content

Invest in informational content

Informational content remains important, especially when it helps users evaluate options, understand trade-offs, and make better decisions. 

Even in commercial journeys, AI systems often rely on informational, comparative, and third-party sources to support recommendations.

REI is a useful example because its buying guides don’t only push products. They explain how to choose, what criteria matter, and which trade-offs users should consider. The content is useful to users and easier for AI systems to integrate into commercial decisions.

Also, you can build personalization resilience by covering multiple intents across the customer journey. It ensures that no matter how AI personalizes a response, you have content for that scenario.

Structure content for retrieval

Moz has a guide on adjusting your content strategy for AI Mode that covers this in depth, and the advice applies across AI platforms generally. 

The core principles:

• Lead with concise, answer-first summaries
• Use clear descriptive headings as signposts
• Ensure each section can stand on its own
• Use bullet points and tables for comparisons
• Add internal anchors or jump links for structure

E-E-A-T principles apply here because well-structured, expert-led content is also highly citable content. 

For AI visibility, expert-led content needs to be extractable, up to date, and corroborated by reliable external sources. 

That means combining content quality with technical accessibility, entity clarity, structured data, internal linking,  and freshness.

Avoid client-side rendered JavaScript for key content sections and links

Don’t assume AI crawlers will render JavaScript in the same way Googlebot can. Make critical content and key entity signals available in the initial HTML or through reliable server-side rendering.

This is especially important for e-commerce and other transactional sites, where AI systems may need to understand product attributes before recommending an option.

Images and videos also need to be crawlable and indexable. Use descriptive alt text, accessible media files, relevant surrounding copy, and clear page context so visual assets can support, rather than block, understanding.

Check and monitor crawlability toward AI bots

Use technical SEO validation tools, log file analysis, and crawler testing to monitor whether AI bots can access your important pages. This should become part of your recurring technical SEO checks, especially for high-impact pages.

There are now validators and readiness checkers built specifically for AI visibility. Glippy has a free Chrome extension you can download, and they recently launched an MCP server for bulk validations.

If you were already running sophisticated SEO processes, much of this will feel familiar. You’re simply reprioritizing and expanding your existing efforts to cover AI search visibility.

Third-party sources play a major role in how AI systems understand and describe brands. For example, AirOps research found that 85% of brand mentions in AI search came from third-party sources rather than brand-owned content.

The exact percentage will vary, but the implication is that your site is not the only source shaping your AI visibility.

Growing Brand Authority for AI visibility requires coordinated work across:

• Link building: Backlinks from related, authoritative sites reviewing relevant businesses.
• Digital PR: Relevant coverage from authoritative publications and industry sources.
• Community management: Real user conversations, reviews, recommendations, and feedback.

If you already have a great SEO program, you will tell me this should have been happening all along, and you’re right. 

The challenge is that many organizations still operate in silos, where SEO, PR, social media, and brand teams work separately, even though they influence the same visibility systems.

Positive sentiment and positioning matter more than ever

It’s no longer enough to earn mentions, links, or citations. The context of those mentions matters.

AI systems may use third-party sources to understand your brand positioning, how it compares with alternatives, and whether others validate those claims.

A few signals to look for when doing an AI search gap analysis include: 

• Sources repeatedly cited or referenced for your tracked topics
• Third-party pages that influence competitor visibility
• Publications, communities, directories, reviews, and comparison sites that appear across your priority prompts
• How LLMs describe your brand versus how you want to be positioned
• Whether your differentiators are being mentioned by third parties
• Whether outdated, incomplete, or inaccurate descriptions are being repeated
• Which gaps are best solved by SEO, digital PR, or product messaging

Expand your optimization efforts based on where the gaps are

The core SEO pillars still matter, but the questions we ask under each one need to expand:

• Crawlability: Ensure that search engines and AI crawlers can access important pages, feeds, and assets.
• Indexability and accessibility: Structure essential content so systems can discover, render, parse, and reuse it.
• Relevance: Cover topics, entities, use cases, comparisons, and decision constraints that users ask about.
• Authority and corroboration: Do reliable third-party sources validate the brand’s claims, positioning, expertise, and usefulness?
• Measurement: Track visibility,citations, sentiment, accuracy, referrals, conversions, and proxy impact separately.

Monitor and participate where your audience validates brands

Earning brand mentions and recommendations often happens outside your site (Reddit, LinkedIn, YouTube, etc). 

SEOs should help identify where these conversations influence AI visibility and collaborate with the teams who can act on them.

The goal isn’t to manipulate conversations, but to align with the relevant areas to ensure your brand is accurately represented in the places where users and AI systems may look for validation.

For inspiration on PR campaigns, here are some resources to explore:

• Trending Campaigns: A free weekly newsletter featuring the top PR campaigns and reactive newsjacking opportunities. 
• Finchling: Scans the news cycle to generate personalized campaign ideas.
• JournoFinder:  Helps you identify the right publications and journalists to contact for coverage.

We’re still early in AI search. The data available today is incomplete compared with what SEOs have built up over decades in traditional search.

That means AI search optimization needs to be practical, flexible, and evidence-led.

Use the readiness framework from part one to diagnose where your site and brand are currently weak. Then prioritize optimization that improves AI search visibility.