SEO Strategy Archives SEO Strategy

May 1 2026

500M AI Searches Later: How To Actually Improve AI Search Visibility & Citations via @sejournal, @hethr_campbell

What signals actually drive AI search visibility?

Are competitors getting cited in AI Overviews while you’re watching from the sidelines?

How do you go from AI visibility gap alerts to a system that closes them?

Most SEO teams already have dashboards showing where they’re invisible in AI search. Few have a process to fix it.

Learn To Turn AI Search Visibility Data Into A High-Visibility System

Reconnect with Sam Garg, Founder and CEO of Writesonic, as he shares his practical framework for diagnosing citation gaps, prioritizing the right actions, and automating execution with AI agents and free open-source SEO & GEO tools.

You’ll Learn:

What drives AI citations: Visibility signal analysis from 500M+ AI conversations. You’ll learn which content types, sources, and placements actually get cited in ChatGPT, Perplexity, and Gemini.
GEO tasks that move the needle: Citation outreach, content refresh, and third-party placements, plus how to use AI agents and open-source tools to automate them.
Where AI search is headed next: Early signals on AI ecommerce and the shift from recommendations to transactions for your channel strategy.

This SEO webinar session covers what 500M+ AI conversations reveal about how citations are earned, which actions actually move the needle (citation outreach, content refresh, third-party placements), and how to use autonomous AI agents to execute at scale.

Watch on-demand now to get the most data-backed, actionable guidance available on improving your brand’s AI search visibility.

Ecommerce MGMT 0 Comments

May 1 2026

How AI Overviews Surface Negative Reviews, Without Anyone Searching for Them via @sejournal, @EraseDotCom

This post was sponsored by Erase.com. The opinions expressed in this article are the sponsor’s own.

Why is my brand appearing in AI comparisons I didn’t ask to be in?
How do I find out what AI tools are saying about my brand?
What’s the difference between traditional reputation management and AI reputation management?

Any issues with your brand’s reputation are what AI decides to show searchers, unprompted.

Throughout Q1 2026, we’ve seen a behavioral shift in how prospects discover brand reputation issues. AI-assisted research tools now autonomously surface negative content, such as reviews, complaints, forum threads, social media discussions, inside comparison queries, without users deliberately searching for problems.

When someone asks ChatGPT “which CRM should I choose,” these AI engines don’t just list features. They pull in user complaints, Reddit gripes, and years-old forum threads as part of their comparison. Your brand’s negative signal can appear in an answer about your competitor. Even more concerning, as Fast Company recently reported, there’s growing evidence of AI engines misquoting or misrepresenting brand statements, compounding the challenge of maintaining an accurate reputation in AI-generated summaries.

AI Comparison Queries Are Now Reputation Audits. Here’s What That Means.

Traditional reputation management focused on suppressing results when someone searched “[your brand] + reviews.” That’s still important, but it’s no longer sufficient.

It’s time for a reputation audit.

AI Overviews and LLM-powered search engines treat every product comparison as an opportunity to synthesize user sentiment. When evaluating options, these tools actively scan for negative reviews on complaint sites, Reddit discussions, forum threads, gripe site entries, and customer support complaints that made it into public view.

The critical difference: users aren’t asking about problems. They’re asking about solutions. But AI engines interpret “helping” as including negative signals from your brand footprint.

Why Some Complaints Show Up in AI Answers & Others Don’t

Not every negative mention gets pulled into AI-generated answers, but certain patterns increase surfacing likelihood:

Recency + volume: Fresh complaints with multiple corroborating sources rank high.
Specificity: Vague posts get filtered out. Detailed complaints that include product names and outcomes are weighted as valuable context.
Platform authority: Reddit, Trustpilot, G2, and industry forums get treated as trusted sources.
Recurrence across sources: If the same issue appears in multiple places, AI engines treat it as a verified pattern.

The 4-Step Framework: How to Audit, Remove, Rebuild, and Suppress Your Brand’s AI Reputation Signals

Understanding what’s in your negative signal footprint, prioritizing what can and should be addressed, and building a positive content layer that represents your brand accurately when AI tools pull information is the key to success.

Map what AI engines can access about your brand across platforms where complaints surface.

Open ChatGPT or Perplexity and type: “What are the pros and cons of [your brand] vs [top competitor]?” Take a screenshot of the response and note any negative claims.
On Google, search site:[key platform].com “[your brand name]” + “scam” OR “complaint”. This forces the search engine to show you only the filtered conversations AI models are currently scraping.
Search for your brand on Google and check the featured snippets for anything negative, other SERP features like People also ask for negative or adversarial searches.

Key platforms to check:

Review platforms (Trustpilot, G2, Capterra, Yelp, Google Business Profile).
Reddit (search your brand name + product category + complaint terms).
Industry forums (Stack Overflow for tech, niche communities for specialized services).
Facebook groups and community pages (particularly industry-specific or local groups where your customers congregate).
Social media (Twitter/X, LinkedIn discussions, TikTok comments).
Legacy gripe sites (RipoffReport, Complaintsboard); while largely deindexed, content may still be cited by AI engines.

Document these details:

Content type and platform.
Date posted.
Specific claims made.
Factual accuracy.
Current visibility in Google and AI summaries.

Focus on detailed complaints with enough context that AI engines might treat them as credible sources.

Step 2: Prioritize Based on Surfacing Likelihood

Focus on:

High priority: Recent complaints with specific details, issues mentioned across multiple platforms, content on high-authority platforms (Reddit, major review sites), complaints naming features or pricing specifically.
Medium priority: Older complaints (1-2 years) still in search results, isolated reviews without corroboration.
Low priority: Very old content (3+ years) with low engagement, complaints about discontinued products.

How To Create A Priority Matrix

Create a simple scoring matrix to decide what to tackle first:

High Priority: Content that appears in AI summaries AND has high organic visibility (check Semrush or Ahrefs for estimated monthly visits to that specific URL) or compare them against queries for those keywords that you have available in search console – if it’s a branded search, you should have full visibility on this from search console.
Verified Impact: For platform-specific reviews (G2, Trustpilot, Google Business), use your internal analytics to track how many users are clicking “Helpful” on negative reviews. A review with 50+ “Helpful” votes is a massive signal that AI engines will not ignore.

Step 3: Remove or Respond Where Possible

Some negative content can be removed outright. Some deserve a response, and some require both.

How to Get Negative Content Taken Down

If the content violates platform policies (false information, impersonation, harassment), request removal through the platform’s reporting process.

For legacy complaint sites and gripe sites, professional content removal services can often negotiate takedowns based on inaccuracies or policy violations, though as reputation defense strategies evolve for AI, the focus has shifted from simply removing content to building stronger positive signals.

For content that mentions you but doesn’t necessarily focus on your brand (like a Reddit thread comparing five tools where yours gets one negative mention), removal usually isn’t an option, but you can dilute its impact by ensuring positive mentions appear more frequently in similar discussions.

When Responding Publicly Actually Helps You

Legitimate complaints about real issues, misunderstandings you can clarify with facts, or service failures where an explanation adds credibility. Keep responses factual, non-defensive, and focused on resolution. AI engines can pull your response into summaries, giving you a chance to reframe the narrative.

When Engaging Makes Things Worse — Skip It

Fake reviews, emotional rants without substance, old complaints about discontinued products, or situations where engagement will amplify visibility.

Step 4: Build a Positive Content Layer That AI Engines Prefer

This is where ongoing reputation management becomes critical. You need owned and earned content that AI engines will preferentially cite when answering comparison queries.

What Goes Into A Positive Content Layer

Structured FAQ content: Create pages answering common objections and questions with clear headers and schema markup.
Case studies: Detailed examples with metrics, timelines, and direct customer quotes give AI engines concrete data to cite.
Community presence: Contribute to Reddit and forums where your audience asks questions. Build credibility through value, not promotion.
Third-party validation: Get featured in roundups and comparison articles on authoritative sites.
Regular content updates: AI models prioritize recent content. Keep your owned content fresh.
How this plays into broader online reputation management: What you’re building isn’t just an AI strategy—it’s a defensible reputation infrastructure. Comprehensive, recent, authoritative content across multiple touchpoints creates a buffer that makes it harder for isolated negative signals to dominate.

How To Build A Positive Content Layer

Turn your FAQ into a knowledge base that addresses common objections (e.g., “Is [your brand] worth the price?”). Depending on how much reach and authority your brand has, it can be worthwhile to publish these as their own pages with a clear H1 question as the headline and breadcrumb the Q and As in a format like /faq/[service area]/[objection] to create more internal linking opportunities and depth rather than just having everything on a massive FAQ page.
Reach out to some of your satisfied customers and ask for a 2–3 sentence quote about a specific outcome they achieved. Publish these as a case study snippet on your site. Specificity (metrics, timeframes) helps to ensure LLMs treat content as credible evidence rather than marketing copy. Link to their LinkedIn or business website, if possible, to help reinforce that it is a real review for a real customer.
Identify high-authority “Best of” lists or industry roundups where your brand is missing and email the editors to provide a unique expert insight or updated product data for inclusion. These seed high-trust citations that AI engines prioritize when synthesizing brand comparisons and reputation summaries. The higher they rank on Google, the better.

Monitoring becomes essential at this stage. Track which keywords trigger AI Overviews that mention your brand, watch for new complaints surfacing in high-authority platforms, and measure whether your positive content is getting cited in AI-generated comparisons. This isn’t a one-time project; it’s an ongoing program.

Start Here: Your Easy Steps to Managing Your AI Reputation

If you’re dealing with high-stakes reputation issues where missteps could amplify problems, specialized online reputation management services and experts like our team at erase.com can help you move faster and avoid pitfalls. The goal isn’t just reacting to what’s already out there; it’s building a system where positive signals consistently outweigh isolated negatives when AI engines scan for information.

The shift is already here. The question is whether you’re managing it proactively or discovering it reactively when a prospect mentions “something they saw in ChatGPT.”

LEARN MORE

Image Credits

Featured Image: Image by Erase.com. Used with permission.

Ecommerce MGMT 0 Comments

Apr 30 2026

ChatGPT vs. Perplexity vs. Gemini: Which LLMs Are Driving Real Conversions? [Expert Panel] via @sejournal, @hethr_campbell

AI search is sending high-intent traffic, but not equally across platforms.

Which LLM is actually driving conversions in your clients’ verticals?

Should GEO efforts be concentrated on ChatGPT versus Perplexity or Gemini?

How do you build an AI search reporting framework clients will actually trust?

▶ Watch the on-demand webinar now to get conversion data by LLM.

How To Identify & Focus On The LLM That Works For You

Not every LLM deserves equal optimization effort.

Misallocating that effort is costing your clients rankings, leads, and revenue.

In this on-demand GEO webinar, Natalie Ann and our expert panel for a breakdown of which platforms are driving measurable results, and how to build an AI search strategy backed by conversion data.

You’ll Be Able To:

Identify which LLMs drive the highest conversion rates in your clients’ industries
Prioritize GEO spend and content optimization based on platform-level performance data
Package LLM optimization as a billable service with reporting that proves impact to clients

Watch now, follow along below, and be ready to rethink how you’re allocating AI search effort.

Ecommerce MGMT 0 Comments

Apr 30 2026

How Brands Are Increasing AI Visibility By Up To 2,000% [Webinar] via @sejournal, @hethr_campbell

The answer is Reddit, and yes, this 90-day strategy is worth your time.

Most brands treat Reddit as an afterthought.

However, Reddit is where buyers finalize their purchase decisions.

Reddit is where human trust gets built.

Therefore, Reddit serves as a trust signal for how AI search tools determine which brands are worth recommending.

AI Mentions & Cites Brands Based On Trust Signals, Across Channels

When ChatGPT, Perplexity, or Google AIO recommends a brand, it’s drawing on a web of signals that indicate the brand is credible, relevant, and mentioned by real people in real contexts.

Reddit is one of the most authentic of those signals.

Your opportunity: not Reddit instead of other channels, but Reddit as a meaningful addition to the multi-channel trust footprint AI reasons from.

One brand OGS Media worked with saw 2,000% AI visibility growth in 90 days after building a genuine Reddit presence. That’s the strategy Bartosz and Brent are unpacking on May 5.

What You’ll Learn In This AI Search Webinar

How Reddit community content contributes to the multi-channel trust signals AI uses to evaluate and surface brands
The 5-stage framework behind OGS Media’s 2,000% AI visibility result
The 7 most common Reddit mistakes brands make
What authentic subreddit engagement looks like when it’s actually working
How to find and engage in Reddit conversations that influence both buyers and AI

About the Speakers

Bartosz Goralewicz is the CEO of OGS Media and one of the most experienced Reddit marketing practitioners in SEO. Brent Csutoras is a Reddit Official Advisor and the Owner of Search Engine Journal, with nearly two decades of hands-on Reddit strategy for brands across every major vertical.

Ecommerce MGMT 0 Comments

Apr 30 2026

The 90-Day GEO Playbook for Local Search: How To Show Up When AI Does The Searching

This post was sponsored by Uberall. The opinions expressed in this article are the sponsor’s own.

Local consumers have stopped searching the way we built our marketing around.

This significant change in buyer habits has been quietly happening in the last 18 to 24 months.

According to recent Uberall research into AI search behavior, an estimated $750 billion in consumer spend is already shifting toward AI-powered search. Roughly 60% of all searches now end without a single click to a website. And in a finding that should stop every marketer cold, or at least those working for multi-location businesses, 68% of brands are missing entirely from the recommendations AI engines generate in their category.

That problem goes beyond channels. It’s a fast-moving visibility problem that risks affecting conversions and revenue.

Generative Engine Optimization (GEO) is the discipline built for this moment. Where SEO optimized pages for a ranking, GEO optimizes entities for a recommendation.

The goal is no longer just to be found in Search Engine Results Pages (SERPs). It’s to be cited, summarized, and trusted when a model answers on your customer’s behalf.

Get Your Checklist & Follow Along

In GEO, three pillars carry the weight. If you’ve worked in SEO for any length of time, the shape will look familiar — compounding visibility isn’t new, it’s the surface that’s changed.

Source of truth. The basic facts about your brand (name, address, hours, services) need to match everywhere a model might look. Inconsistent signals train AI engines to trust you less.
Context engineering. Your content has to answer the questions customers actually ask, in the language they ask them. Of course, conversational answers should take priority over keyword clusters.
Orchestration. You measure citations, refresh content, and compound visibility over time.

Here is how those three pillars translate into a realistic 90-day plan teams can actually run.

Phase 1 (Week 1): Foundational Analysis

You cannot optimize what the model cannot parse. The first week is a data hygiene sprint, rather than a content sprint.

Start with the local SEO basics most teams assume are already clean:

Audit your NAP details (Name, Address, Phone) across Google Business Profiles, Apple Maps, Yelp, Bing Places, and the major data aggregators. Even small inconsistencies — a missing suite number, an old phone format, a rebrand that never propagated — train AI engines to treat your brand as a lower-confidence entity.
Check your location pages, about page, and product pages for structured data. Schema isn’t a magic AI switch — recent tests suggest LLMs largely read it like any other on-page text. What it does is reduce ambiguity about what your business is and does, and that clarity is what helps a model interpret and cite you correctly.
Type the questions your customers actually ask into ChatGPT, Gemini, Perplexity, and Google AI Overviews. Not branded queries – real ones like “best orthodontist near Lincoln Park,” “which EV charger works with a Ford Lightning,” “coffee shops in Berlin that allow dogs.” Note where you appear, where you don’t, and which competitors show up instead.

That gap list becomes your brief for the next 80 days. It’s also where most brands discover the blind spots they didn’t know they had.

Phase 2 (Days 7–30): Context Engineering And Targeted Content

Once you know which prompts you’re missing from, the work becomes specific. For each blind spot, you are building the content a model would actively want to cite.

A few patterns that hold up across industries:

One prompt, one page. If “best family dentist in Austin with Saturday hours” returns three competitors and none of your locations, build or optimize the pages that answer exactly that. Don’t bury the answer three scrolls down.
Write for the question, not the keyword. AI engines extract complete answers, not phrases. A well-structured FAQ with direct, factual responses often outperforms a 2,000-word, keyword-stuffed guide that dances around the point
Cite yourself credibly. Include dates, local details, original data, named authors, and explicit comparisons. Models reward specificity and downgrade vague claims.

This is the phase where content that actually gets cited starts to look different from content built for the old ranking game. It is tighter, more factual, and structured around how someone would ask a question out loud.

Phase 3 (Days 30–60): Surgical Placement & Off-Page Authority

Off-page authority still matters. The economics, however, have flipped.

The instinct is to chase top-tier publishers. For GEO, that is usually the wrong move.

The sites that generative engines pull from most often aren’t always the ones with the highest domain authority. These are the ones relevant to your business and are cited more frequently, even if they’re not huge publications.

A more effective approach:

Focus on sites that already rank in Google for the prompts your customers use — the kind of credible, topical sources you’d want them to find when they’re researching. Top-tier placement isn’t the goal; any authoritative site that actually serves your audience counts.
The publishers AI engines already cite in your category are the ones models trust enough to source from. Re-run your Phase 1 prompts, track which domains keep appearing in the citations, and that’s your shortlist.
Size and prestige aren’t reliable proxies for AI citation rates. A specialist publication with real topical authority in your category often earns more AI citations than a bigger, more generic name.

The goal isn’t link volume. It is being mentioned, in context, in the sources your category’s models already trust.

Phase 4 (Days 60–90): Orchestration And Compounding

By day 60, you should have new content live, citations starting to show up on publisher sites, and enough signal to measure. Phase 4 is where GEO stops being a project and starts being a system.

Three metrics worth tracking weekly:

AI citation rate — how often your brand is named in AI-generated answers for your priority prompts.
Share of Voice — your citation rate relative to competitors across the same prompt set.
Content decay — which cited pages are losing citations over time and need refreshing with new data, dates, or insights.

The compounding effect here is profound. Brands that treat GEO as an ongoing loop — audit, publish, place, measure, refresh — see substantially higher citations and conversion rates. A recent Search Engine Journal webinar, featuring Uberall with AthenaHQ, states that GEO-savvy brands see 2x as many citations and 3–9x higher conversion rates within 90 days compared to brands still optimizing purely for classic search.

That delta matters more than it looks. As zero-click behavior grows, the citation inside the AI answer is the conversion surface.

For a concrete example, Audika France, a multi-location hearing-care brand and Uberall customer, ran this orchestration loop as an early adopter. They used it to track how AI engines described their clinics, spot the attributes models were missing, and close the gap between visible and recommended. Their results show how one multi-location brand went from an AI blind spot to a consistent recommendation.

What To Do Next

The pattern is consistent across multiple industries, including retail and restaurants. Brands that start now build a structural advantage that is hard to unwind once the category catches up. The ones that wait end up explaining to their board a year from now why a competitor became the default recommendation in every model their customers use.

If you want a snapshot of how your locations are performing in AI search, check out our AI Visibility Grader tool. It gives you a quick view of your AI visibility and the factors shaping it.

Or if you want to take this further and get a higher definition picture of where you stand in AI search, GEO Studio’s free trial will map your brand’s presence across the major generative engines.

Local search has changed. This is how you become the default answer.

TAKE CONTROL OF AI SEARCH

Image Credits

Featured Image: Image by Michelle Azar/ Uberall. Used with permission.
In-Post Image: Image by Uberall. Used with permission.

Ecommerce MGMT 0 Comments

Apr 29 2026

Why Your Content Isn’t Being Cited in AI Answers (And How to Fix It) [Webinar] via @sejournal, @lorenbaker

When a customer asks ChatGPT, Gemini, or another AI tool a question, that system selects a short list of sources to cite in its answer. If your brand isn’t on that list, it’s not a visibility problem; it’s a brand and content strategy problem.

What AI Actually Evaluates

AI systems don’t cite randomly.

They evaluate content against specific criteria: topical authority, structural clarity, and brand trust signals they can measure. Most brands haven’t audited their content against these criteria, making the content of this upcoming SEO webinar an advantage for you.

What You’ll Learn

In this SEJ webinar, Wayne Cichanski, VP of Search & Site Experience at iQuanti, unpacks how AI systems generate answers and what determines whether your brand’s content earns a place in them:

How AI-powered search selects and cites content, so you know exactly what you’re optimizing for
Which topical authority and brand trust signals determine whether your content earns a place in AI-generated answers
Specific, practical tactics for creating and restructuring content that increases your brand’s AI visibility

Ecommerce MGMT 0 Comments

Apr 28 2026

AEO In 2026: Which Content Formats Earn AI Citations & How to Produce More [Webinar] via @sejournal, @hethr_campbell

AI-generated answers are capturing intent before the click, and that changes where to invest, what to measure, and which formats to prioritize. The question isn’t whether to adapt, it’s knowing exactly what to do first.

Answer Engine Optimization (AEO) Is A Core Discipline

AEO sits alongside SEO as a primary driver of how brands get discovered in 2026. The content formats, authority signals, and workflows that earn citations in ChatGPT, Claude, and Gemini are distinct from what drives traditional rankings.

What You’ll Learn

Which AEO and content marketing trends will have the most impact on AI citation rate and organic visibility in 2026.
How to reframe your success metrics when AI answers replace the click, and what to optimize for instead.
Which content formats generate the highest likelihood of AI citation, and how to build more of them into your editorial workflow.
How to integrate agentic workflows into your content operation to scale authority-building without losing quality.

About the Speakers

Shannon Vize is Sr. Content Marketing Manager at Conductor, focused on the intersection of AI and content strategy. Pat Reinhart is VP of Services & Thought Leadership at Conductor, with deep experience helping digital teams adapt their search strategies to emerging discovery behaviors.

This session delivers a practical, prioritized framework for operationalizing AEO and building AI search visibility in 2026.

Ecommerce MGMT 0 Comments

Apr 22 2026

AI Overviews & Local SEO: What Multi-Location Brands Must Do [Webinar] via @sejournal, @lorenbaker

Thanks to AI, local SEO has a new standard.

AI-powered search doesn’t just rank pages. It synthesizes answers from your site content, schema markup, listings data, and reviews, and then it decides whether your locations are worth citing. For brands managing 10, 50, or 100+ locations, that’s a significant exposure point.

What’s Actually Changing in Local Search

AI search experiences, from Google’s AI Overviews to other generative answer engines, are now drawing on a broader set of signals to determine which local businesses to surface.

Listing accuracy, structured data, review signals, and the quality of your actual location pages all factor in. If any of those are inconsistent or thin, your visibility takes a hit before a customer ever clicks.

What You’ll Learn in This Session

How AI-powered search engines pull local business data, and where your current setup may have gaps
What separates a high-performing location page from one that gets ignored by AI search
Which technical signals carry the most weight for local AI search
How to prioritize improvements across a large portfolio of locations without starting from scratch

Nick Larson, Product Manager and Local Pages Expert at Alchemer brings hands-on experience helping multi-location brands build local search visibility at scale.

This is a practical, framework-first session built for marketers and operators managing location-based brands.

Ecommerce MGMT 0 Comments

Apr 20 2026

How To Build AI Visibility In 90 Days [Webinar] via @sejournal, @hethr_campbell

AI search has changed how buyers discover solutions. Here’s how to make sure they find you.

Why AI Visibility Is Now a Growth Priority

Platforms like ChatGPT, Perplexity, and Google AI Overviews are now active discovery channels for buyers. Marketing leaders who understand those signals are building durable visibility. Those who don’t are quietly losing ground.

What You’ll Learn in This Free SEO Webinar

Which AI visibility signals actually drive discoverability in 2026
A phased 90-day framework that helps you audit your baseline, run AI-native experiments, then scale what works
How funded startups are restructuring teams and budgets around this shift

About the Speaker

Jason Shafton is Founder & CEO of Winston Francois, a growth consulting firm. He’s led growth and marketing at Google, Headspace, and Kajabi, and has built AI visibility playbooks across 10+ venture and PE-backed startups navigating this exact transition.

Register Free

This is one hour of tactical, experience-backed frameworks, built for founders, CMOs, and marketing leaders who are ready to act.

Ecommerce MGMT 0 Comments

Apr 15 2026

How To Become The AI Search Authority In Your Company [Webinar] via @sejournal, @lorenbaker

If you’re in an SEO role, there’s a good chance your job description quietly expanded over the last year. You’re now the de facto expert on how your company shows up in ChatGPT, Gemini, and Perplexity.

Your SEO Expertise Is Already the Foundation for AI Search Authority

Getting cited in AI outputs is table stakes.

The harder question is: when an AI model speaks about your brand, is it using your content as the source? Or is it synthesizing what third parties have written about you?

For most brands right now, it’s the latter. And that’s a fundamentally different problem than SEO has dealt with before, one that requires coordination well beyond the SEO team.

What You’ll Learn in This Session

How to lead the cross-functional effort (PR, product, content) that shapes what AI models are trained to trust
How to measure “Answer Certainty” instead of just visibility, so you can report on outcomes that leadership actually understands
How to identify where third-party narratives are overriding your brand’s own content in AI outputs
Why your existing SEO expertise is the foundation for all of this, and how to position it that way internally

About the Speakers

Chris Sachs is VP of Client Success at seoClarity, where he works directly with enterprise SEO teams navigating the shift from traditional search to AI-driven discovery. Tania German is VP of Marketing at seoClarity, with expertise in building brand authority frameworks that translate across organic and AI search channels.

This is a tactical session for SEO managers, growth directors, and CMOs who are already in the thick of AI search and need a system, not just a framework.

Ecommerce MGMT 0 Comments