Don’t Go Chasing AI Yet: A Framework for Prioritizing SEO vs. AI Search via @sejournal, @hethr_campbell

Everyone is scrambling to incorporate AI. But what takes priority?

Is generative engine optimization (GEO) replacing traditional SEO?

Should you shift budget from traditional SEO to AI content experiments?

Watch this on-demand SEO webinar to see how to prioritize SEO vs. AI search based on your business model.

Before You Reallocate SEO Budget, Validate Where AI Will Drive Incremental Growth In Channel Mix

In this session, DAC’s Alex Hernandez, Associate Director of SEO, and Orli Millstein, Director of Content Strategy, challenge the assumption that more AI optimization automatically equals more growth. Instead, you’ll see how business model, product complexity, and customer journey determine whether AI visibility should be accelerated, balanced, or deprioritized.

You’ll Learn:

You’ll walk away with a structured way to evaluate strategic fit, content readiness, and revenue impact before reallocating budget or rewriting your roadmap.

Watch the on-demand webinar now to build an AI search strategy that strengthens performance rather than dilutes it!

Breaking Content & SEO Silos To Build Entity Authority in AI Search

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

Improving search visibility across traditional and AI search requires evolving our methods and updating how teams work together to improve outcomes.

Content teams and SEO teams have always needed each other. But with AI search raising the bar on entity authority, the cost of operating in silos has never been higher. This framework is how you close that gap.

Why AEO Makes SEO & Content Collaboration Non-Negotiable

Historically, content and SEO teams have both pursued organic visibility, though they often worked independently. While it’s always been ideal for these teams to collaborate effectively, with answer engine optimization (AEO), it’s more critical than ever that they work together to strengthen a site’s entity associations and improve its retrieval opportunities.

What Is AEO?

AEO, which is also called generative engine optimization (GEO), is the process of improving a website’s content and technical foundations to make it easier for AI crawlers to read and extract content. AEO aims to improve brand citations and mentions and requires SEO and content teams to work together to improve entity targeting, semantic associations, content quality, content comprehensiveness, and content structure, among other things.

Without entity-level coordination, brands may fail to gain traction in AI search surfaces and lose AI citation and mention opportunities to competitors. Let’s break it down. AI Overviews (those AI generated snippets at the top of Google search results) cite websites that demonstrate concentrated authority (backed by external sources) on specific entities. Websites with consistent messaging around their core services and products backed by external corroboration like backlinks and PR mentions appear in knowledge panels and other search features. So, when content depth and external link validation operate independently, sites miss retrieval opportunities across AI-powered search.

Entities provide the framework for this collaboration. When content and SEO strategies align around building authority for the same entities, teams can execute coordinated work that strengthens both content comprehensiveness and external validation.

How Entities Provide a Shared Framework

Entities are distinct concepts that search systems can uniquely identify and connect. Unlike keywords, entities are semantic concepts with attributes and relationships. “Customer onboarding” as an entity connects to “user adoption,” “product activation,” “time to value,” and “customer success.” To get cited, brands need to build entity authority.

What Is Entity Authority?

Entity authority is the degree to which search systems recognize your brand as a credible, well-corroborated source on a specific entity. A site with strong entity authority for “resource planning” has comprehensive content on the topic, earns links from sources that also discuss it, and structures that content so search systems can map the relationships between related concepts.

Search systems evaluate entity authority on three dimensions:

  • Recognition: Can they identify which entities your content addresses?
  • Relationships: Do they understand how those entities connect?
  • Corroboration: Do external sources validate your entity representations?

These evaluation criteria create natural points of coordination. When both teams work toward the same entity authority goals, their work reinforces the same recognition, relationship, and corroboration signals that search systems use to evaluate expertise.

Why Neither Team Can Do This Alone

SEO teams could identify target entities and pursue entity-focused optimization independently. But without comprehensive content coverage, the technical infrastructure (schema, internal linking, site architecture) would connect thin, scattered content that doesn’t demonstrate depth. Conversely, content teams could create full-funnel entity coverage independently. But without the technical entity infrastructure and external corroboration through entity-relevant backlinks, the content lacks the structural and external signals that strengthen entity authority.

The coordination creates what neither discipline can build alone: comprehensive content backed by both technical entity infrastructure and external sources.

Putting Entity Authority Into Practice

Start by choosing 3–5 core topics your business wants to be known for, then consistently build content and links around those topics. Instead of spreading effort across dozens of disconnected ideas, SEO and content teams focus on reinforcing the same few areas until search systems clearly associate your brand with them.

Entities work as an organizing principle because they’re specific enough to guide both disciplines. Instead of content planning around vague topics and SEO chasing domain authority, both teams can focus on, say, “resource planning,” specifically.

Content creates guides, research, and comparisons on resource planning. SEO builds links from publications discussing resource planning. Both reinforce the same entity signals, and the compounding effect of that alignment is what separates brands that gain AI retrieval from those that don’t.

What an Entity-Focused Collaboration Workflow Looks Like

We propose a four-phase workflow that enables teams to test entity strategies and adapt based on performance.

Image created by Victorious, March 2026

Phase 1: SEO Conducts Entity Research

SEO begins by identifying entities aligned to the business’s services or products. Through vector embedding analysis (using tools like Google’s Natural Language API or Semrush to create a numerical representation of semantic associations), the team identifies related topics (entity associations) that would build authority for these main entities. This analysis reveals patterns of topic similarity and competitive gaps.

During this phase, SEO also analyzes link velocity requirements for each main entity, with the understanding that link building will be distributed across the entity cluster. This entity cluster would include pages with different search intents that cover different aspects of the same concept (entity). The output is a shortlist of main entities with their associated entities, aligned with business objectives and realistic resource constraints.

For a project management platform, the main entity might be “project management,” with associated entities like “resource planning,” “capacity management,” and “project forecasting.” Focusing on a limited number of main entities allows both teams to commit sufficient resources to build depth rather than scattering effort across too many targets.

Phase 2: SEO and Content Teams Analyze Content Gaps and Prioritize Impact

The teams review existing content coverage for each target entity together. They identify gaps across the buyer journey (awareness, consideration, decision) and prioritize which assets to create based on competitive need, business impact, and available resources. This isn’t content asking “what should we write?” or SEO saying “we need these pieces.”

Both teams evaluate comprehensiveness together:

  • Does the entity coverage span formats (research, guides, comparisons, how-tos)?
  • Does it address different stages of the buyer journey?
  • Does it create the depth that AI systems recognize as authority?

At this point, the teams also align on success metrics. Each team needs to agree on what entity authority looks like for the target entities and which signals will indicate progress, taking into account current content performance. This shared measurement framework ensures both teams work toward the same definition of success.

At the end of this phase, the teams should have a prioritized content plan showing which assets support which entities, target publication dates, and metrics for measuring entity authority growth.

Where Most Teams Break Down

Content and SEO often report into different leaders, operate on different timelines, and measure success differently. Content teams may focus on production and engagement, while SEO teams may focus on rankings and links. Without a shared framework, priorities drift and execution becomes fragmented.

Aligning around entities gives both teams a common target, so decisions about what to create, what to promote, and what to fix all point in the same direction.

Phase 3: Both Teams Execute on the Plan

Content creates and publishes the planned assets. SEO implements schema markup to highlight entity relationships, analyzes and fixes internal linking between entity clusters, and executes backlink building using entity-relevant anchor text and targeting publications that discuss those entities.

When prioritizing internal linking fixes, SEO focuses first on pages that already have topical relevance to the target entity but lack incoming links from related content, as these represent the fastest wins for entity cluster cohesion. For anchor text, the goal is to show natural variation rather than exact-match repetition to avoid over-optimization. Links also may not necessarily point to newly published content. What matters is that link velocity, anchor text, and link sources all reinforce the same entity associations that the content is building.

The goal here is entity-level coordination over piece-level coordination. Content and SEO teams work toward improving entity authority together.

Phase 4: Teams Assess Performance and Refine Plan

Together, the teams track implementation progress and entity authority signals to determine whether their efforts are improving brand visibility and ultimately, the bottom line for the business.

They’ll monitor ranking increases for related terms, since organic visibility influences AI citation opportunities. They also track AI Overview citations when users search entity-related queries (e.g., “[entity] best practices,” “[entity] solutions”) and frequency of brand mentions in AI-generated responses.

Traditional metrics like traffic and conversions emerge later as lagging indicators. Teams use the early signals to refine the plan: maintain the current approach, accelerate investment in high-performing entity clusters, or adjust tactics for underperforming entities.

Example: Resource Planning Entity in Action

Vector embedding analysis at a SaaS project management platform reveals “resource planning” as an entity association with strong similarity to their main “project management” entity. Building authority on resource planning would strengthen their overall project management authority. Competitive analysis shows they need consistent link velocity over six months to reach parity. (This six-month timeline assumes a moderately competitive landscape. In more saturated categories, building to parity may take longer, and teams should calibrate expectations based on their specific competitive environment before committing to a roadmap.)

A joint review of existing coverage reveals one surface-level blog post on resource planning basics. Competitive sites have research on resource allocation trends, comprehensive guides on capacity planning, comparison content evaluating resource planning approaches, and implementation how-tos. The gap is clear.

Together, they prioritize:

  • Awareness: Original research on resource planning practices
  • Consideration: A comprehensive resource planning guide
  • Consideration: A comparison of resource planning methodologies
  • Decision: Implementation guides for different team structures

Over three months, the content team publishes the planned assets while SEO implements schema, tightens internal linking across the entity cluster, and builds links from project management publications to pages across the site, not just the new content. They start looking for organic ranking changes, branded traffic changes, and AI citation rates.

After four months, visibility increases for resource planning queries across multiple pages, not just the newly published content. The research piece earns two AI Overview citations. These results reflect the entity strategy working as designed: content depth, technical infrastructure, and external corroboration all reinforcing the same entity signals together. Neither outcome would have happened on the same timeline if the teams had executed independently. That’s the compounding effect of entity-level coordination in practice.

It’s Time To Move Toward Structured Experimentation

Entity-focused collaboration isn’t a fixed formula, but rather, a framework for structured experimentation. Teams will need to test which entity associations drive the strongest authority signals, which content formats generate the most AI citations, and which link-building strategies accelerate entity recognition most effectively.

Though the workflow outlined here provides a starting structure, iteration is expected. You’ll likely find that entity clusters don’t build authority at the same pace, buyer journey stages that seem less critical may drive unexpected retrieval, link velocity requirements vary by competitive landscape, and the measurement signals themselves evolve as AI search capabilities change.

Flexibility is essential. Teams need space to test approaches, measure what works, and adapt quickly. Tighter coordination between content and SEO enables faster learning cycles. When both teams work from the same entity framework and shared success metrics, they can identify what’s working and shift resources accordingly. The brands that establish entity authority now, before AI search surfaces fully mature, will be significantly harder to displace later.


Image Credits

Featured Image: Image by Victorious. Used with permission.

How To Turn AI Search Visibility Data Into a GEO Strategy That Closes Citation Gaps [Webinar] via @sejournal, @hethr_campbell

AI search is dominating the strategy conversation right now, and every SEO director is fielding the same pressure from leadership: “What’s our AI search plan?”

The instinct is to optimize everywhere: close every citation gap, refresh every page, pursue every placement. But before you reallocate budget or rebuild your GEO roadmap, there’s a more useful question to ask first:

  • Which AI search signals are actually driving citations for your brand, and do you have a system to act on them?

Join us for an upcoming expert webinar where we’ll dive into exactly that.

What You’ll Learn

In this webinar, Sam Garg, Founder and CEO of Writesonic, will break down what 500M+ AI conversations reveal about citation signals,  and show how that data should shape your GEO execution strategy.

Specifically, you’ll walk away with:

  • The signals behind AI citations: which content types, sources, and placements actually get cited in ChatGPT, Perplexity, and Gemini, and why it differs from traditional ranking logic
  • A GEO prioritization framework: so you stop spreading effort equally across citation outreach, content refresh, and third-party placements, and focus on what moves the needle for your specific gaps
  • An execution model powered by AI agents: including free open-source tools you can deploy right away to automate GEO tasks at scale

Why Attend?

Most SEO teams already have dashboards showing where they’re invisible in AI search. Few have a process to fix it. This session gives you both the diagnostic framework and the execution playbook to close those gaps, and the data to make the case for AI search investment internally.

Join us live to get your questions answered directly by the expert.

ChatGPT Now Crawls 3.6x More Than Googlebot: What 24M Requests Reveal

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

Everyone assumes Googlebot is the dominant crawler hitting their website. That assumption is now wrong.

We analyzed 24,411,048 proxy requests across 78,000+ pages on 69 customer websites on Alli AI’s crawler enablement platform over a 55-day period (January to March 2026). OpenAI’s ChatGPT-User crawler made 3.6x more requests than Googlebot across our data sample. And that’s not even counting GPTBot, OpenAI’s separate training crawler.

A note on methodology: Crawler identification used user agent string matching, verified against published IP ranges. Request metrics are measured at the proxy/CDN layer. The dataset covers 69 websites across a variety of industries and sizes, predominantly WordPress-based. Full methodology is detailed at the end.

Finding 1: AI Crawlers Now Outpace Google 3.6x & ChatGPT Leads the Pack

Image created by Alli AI, April 2026.

When we ranked every identified crawler by request volume, the results were unambiguous:

Rank Crawler Requests Category
1 ChatGPT-User (OpenAI) 133,361 AI Search
2 Googlebot 37,426 Traditional Search
3 Amazonbot 35,728 AI / E-Commerce
4 Bingbot 18,280 Traditional Search
5 ClaudeBot (Anthropic) 13,918 AI Search
6 MetaBot 10,756 Social
7 GPTBot (OpenAI) 8,864 AI Training
8 Applebot 6,794 AI Search
9 Bytespider (ByteDance) 6,644 AI Training
10 PerplexityBot 5,731 AI Search

ChatGPT-User made more requests than Googlebot, Amazonbot, and Bingbot combined.

Image created by Alli AI, April 2026.

Grouped by purpose, AI-related crawlers (ChatGPT-User, GPTBot, ClaudeBot, Amazonbot, Applebot, Bytespider, PerplexityBot, CCBot) made 213,477 requests versus 59,353 for traditional search crawlers (Googlebot, Bingbot, YandexBot). AI crawlers are now making 3.6x more requests than traditional search crawlers across our network.

Finding 2: OpenAI Uses 2 Crawlers (And Most Sites Don’t Know the Difference)

Image created by Alli AI, April 2026.

OpenAI operates two distinct crawlers with very different purposes.

ChatGPT-User is the retrieval crawler. It fetches pages in real time when users ask ChatGPT questions that require up-to-date web information. This determines whether your content appears in ChatGPT’s answers.

GPTBot is the training crawler. It collects data to improve OpenAI’s models. Many sites block GPTBot via robots.txt but not ChatGPT-User, or vice versa, without understanding the distinct consequences of each.

Combined, OpenAI’s crawlers made 142,225 requests: 3.8x Googlebot’s volume.

The robots.txt directives are separate:

User-agent: GPTBot      # Training crawler — feeds OpenAI's models
User-agent: ChatGPT-User # Retrieval crawler — fetches pages for ChatGPT answers

Finding 3: AI Crawlers Are Faster & More Reliable, But Their Volume Adds Up

Image created by Alli AI, April 2026.

AI crawlers are significantly more efficient per request:

Crawler Avg Response Time 200 Success Rate
PerplexityBot 8ms 100%
ChatGPT-User 11ms 99.99%
GPTBot 12ms 99.9%
ClaudeBot 21ms 99.9%
Bingbot 42ms 98.4%
Googlebot 84ms 96.3%

Two likely reasons. First, AI retrieval crawlers are fetching specific pages in response to user queries, not exhaustively discovering site architecture. They know what they want, they grab it, and they leave. Second, while all crawlers on our infrastructure receive pre-rendered responses, Googlebot’s broader crawl pattern means it requests a wider range of URLs, including stale paths from sitemaps and its own legacy index, which adds latency from redirect chains and error handling that retrieval crawlers avoid entirely.

But there’s a catch: while each individual request is lightweight, the sheer volume means aggregate server load is substantial. ChatGPT-User at 11ms × 133,361 requests is still a real infrastructure cost, just distributed differently than Googlebot’s fewer, heavier requests.

Finding 4: Googlebot Sees a Different (Worse) Version of Your Site

Image created by Alli AI, April 2026.

Googlebot’s 96.3% success rate versus near-perfect rates for AI crawlers reveals an important structural difference.

Googlebot received 624 blocked responses (403) and 480 not found errors (404), accounting for 3% of its requests. Meanwhile, ChatGPT-User achieved 99.99% success. PerplexityBot hit a perfect 100%.

Image created by Alli AI, April 2026.

Why the gap? The most likely explanation is index age and crawl behavior, not site misconfiguration.

Googlebot maintains a massive legacy index built over years of continuous crawling. It routinely re-requests URLs it already knows about — including pages that have since been deleted (404s) or restructured (403s). This is normal behavior for a search engine maintaining an index of this scale, but it means a meaningful percentage of Googlebot’s requests are directed at URLs that no longer exist.

AI crawlers don’t carry that baggage. ChatGPT-User fetches specific pages in response to real-time user queries, targeting content that’s currently relevant and linked. That’s a structural advantage that produces near-perfect success rates.

Industry Reports Confirm AI Crawling Surged 15x in 2025

These findings align with broader industry trends. Cloudflare’s 2025 analysis reported ChatGPT-User requests surging 2,825% YoY, with AI “user action” crawling increasing more than 15x over the course of 2025. Akamai identified OpenAI as the single largest AI bot operator, accounting for 42.4% of all AI bot requests. Vercel’s analysis of nextjs.org confirmed that none of the major AI crawlers currently render JavaScript.

Our data shows this crossover may already be happening at the site level for properties that actively enable AI crawler access.

Your New SEO Strategy: How To Audit, Clean Up & Optimize For AI Crawlers

1. Audit your robots.txt for AI crawlers today

Most robots.txt files were written for a Googlebot-first world. At minimum, have explicit directives for ChatGPT-User, GPTBot, ClaudeBot, Amazonbot, PerplexityBot, Applebot, Bytespider, CCBot, and Google-Extended.

Our recommendation: Most businesses benefit from allowing both retrieval crawlers (ChatGPT-User, PerplexityBot, ClaudeBot) and training crawlers (GPTBot, CCBot, Bytespider), training data is what teaches these models about your brand, products, and expertise. Blocking training crawlers today means AI models learn less about you tomorrow, which reduces your chances of being cited in AI-generated answers down the line.

The exception: if you have content you specifically need to protect from model training (proprietary research, gated content), use granular Disallow rules for those paths rather than blanket blocks.

2. Clean up stale URLs in Google Search Console

Our data shows Googlebot hits a 3% error rate, mostly 403s and 404s, while AI crawlers achieve near-perfect success rates. That gap likely reflects Googlebot re-crawling legacy URLs that no longer exist. But those failed requests still consume the crawl budget.

Audit your GSC crawl stats for recurring 404s and 403s. Set up proper redirects for restructured URLs and submit updated sitemaps.

3. Treat AI crawler accessibility as a distinct SEO channel

Ranking in ChatGPT’s answers, Perplexity’s results, and Claude’s responses is emerging as a distinct visibility channel. If your content isn’t accessible to these crawlers, particularly if you’re running JavaScript-heavy frameworks, you’re invisible in AI search.

We’ve published a live dashboard showing how AI crawler traffic breaks down across a real site: which platforms are visiting, how often, and their share of total traffic; if you want to see what this looks like in practice.

4. Plan for volume, not just individual request weight

AI crawlers send light, fast requests, but they send many of them. ChatGPT-User alone accounted for more than 133,000 requests in 55 days. The aggregate server load from AI crawlers is now likely exceeding your Googlebot load. Make sure your hosting and CDN can handle it, the low per request response times in our data reflect the fact that Alli AI serves pre-rendered static HTML from the CDN edge, which is exactly the kind of architecture that absorbs this volume without taxing your origin server.

Methodology

This analysis is based on 24,411,048 HTTP proxy requests processed through Alli AI’s crawler enablement platform between January 14 and March 9, 2026, covering 69 customer websites.

Crawler identification used user agent string matching, verified against published IP ranges. For OpenAI crawlers specifically, every request was cross-referenced against OpenAI’s published CIDR ranges. This confirmed 100% of GPTBot requests and 99.76% of ChatGPT-User requests originated from OpenAI’s infrastructure. The remaining 0.24% (requests from spoofed user agents) were excluded.

Limitations: The dataset is scoped to Alli AI customers who have opted into crawler enablement. Crawlers that don’t self-identify via user agent are not captured. Response time measurements are at the proxy layer, not the origin server.

About Alli AI

Alli AI provides server-side rendering infrastructure for AI and search engine crawlers. This analysis was produced using data from our proxy infrastructure to help the SEO community better understand the evolving crawler landscape.

Want to see this data in action? See the breakdown firsthand by visiting our AI visibility dashboard.


Image Credits

Featured Image: Image by Alli AI. Used with permission.

In-Post Iamges: Images by Alli AI. Used with permission.

How AI Is Changing Lead Generation: 3 Key Things SEO & PPC Teams Need To Do Now via @sejournal, @CallRail

1. Identify Which AI Platforms Are Driving Your Visitors

Each LLM and answer engine has different logic, leading to different outputs for the same prompts. It’s important to understand which AI chatbots are aligned with your brand before making decisions that inform a larger AI search or SEO strategy.

Different LLMs Are Driving Leads In Different Industries

Not all AI platforms send leads the same way.

  • ChatGPT = Speed. ChatGPT dominates overall lead volume at 90.1% of AI-referred leads, with especially strong numbers in healthcare and automotive industries, where people want instant options.
  • Perplexity = Research. Perplexity accounts for 6.3%, but it punches well above its weight in high-consideration sectors. In Travel & Hospitality and Manufacturing, nearly one in ten AI leads comes from Perplexity, roughly ten times the rate seen in other industries.
  • Google’s Gemini holds 2.4% of AI-referred leads and is gaining traction in Business Service and Manufacturing, likely because users lean on its Google Workspace integration.
  • Claude, with 1.2% of lead generation, is carving out a niche in both Real Estate verticals and also with Marketing Agencies. Especially in areas where consumers tend to do more specific and detailed research before reaching out.

How To Accurately Track AI Prompt Visibility

AI search isn’t one channel. It’s a set of distinct platforms, each with different behaviors and industry strengths. So, repeat this AI prompt research phase for each LLM.

  1. Identify the LLMs that matter most for your vertical. Use the data above as a starting point. If you’re in healthcare or automotive, prioritize ChatGPT visibility. High-consideration service? Pay attention to Perplexity. B2B or manufacturing? Gemini should be on your radar.
  2. Test how each platform describes your business. Go to ChatGPT, Perplexity, Gemini, and Claude and ask them questions your customers would ask. “Who’s the best [your service] in [your market]?” See if you’re being recommended. If not, note who is and what content those competitors have that you don’t.
  3. Create content that answers the questions AI platforms are fielding. LLMs favor well-structured, authoritative, fact-rich content. Publish service pages, FAQs, comparison guides, and local content that directly answer the kinds of questions consumers ask these platforms.

2. Connect AI Traffic To Actual Conversions

Connecting AI-driven leads to actual revenue in your reporting is key to understanding how to prioritize your marketing activities. Without visibility into AI lead attribution, you’re making decisions in the dark, which is an expensive place to be.

However, if you can identify AI as the source of your best leads, you instantly know how to pivot your SEO strategy.

How To Track AI Traffic & Attribute Conversions Across ChatGPT, Gemini, and Perplexity

As more money flows through AI search, the ability to attribute leads from specific LLMs isn’t a nice-to-have. It’s the difference between knowing what’s working and throwing budget at a black box.

What you need is the ability to trace a lead from the AI platform where it originated, through the call, form, or chat where it converted, all the way to the revenue it generated. That full-funnel visibility is what separates data-driven teams from everyone else.

  1. Implement LLM-specific attribution. Use a platform that can identify which AI model referred each lead. CallRail’s AI search engine attribution, for example, automatically tags whether an inbound call came from ChatGPT, Perplexity, Gemini, or Claude, not just “AI.” That level of granularity is what makes it possible to actually optimize by channel.
  2. Create custom GA4 channel groups for AI traffic. In Google Analytics, go to Admin > Data Display > Channel Groups and create a custom channel group that isolates AI referral traffic by source. This lets you compare AI-driven sessions and conversions against your other channels.
  3. Add “How did you hear about us?” to your intake process. Self-reported attribution (SRA) is a simple but powerful complement to digital tracking. Add it to your intake forms and train front-desk or sales staff to ask on calls. CallRail’s SRA feature lets you capture this data at the conversation level, so you can compare what callers say against what your analytics show. The gaps will reveal exactly where your tracking is falling short.

See what’s changing: The 2026 Outlook for Marketing Agencies

Connect AI Traffic to Calls, Forms & Sales Pipelines

Call tracking lives in one platform. Form submissions in another. Text conversations somewhere else entirely. Sound familiar?

When your lead data is fragmented like that, it’s surprisingly hard to answer basic questions. Which campaigns drive your best leads? Is AI search actually improving results? Where are leads falling off between first contact and conversion?

Make sure you are monitoring every lead interaction for complete funnel visibility. Teams need clear insight into every conversation-whether it comes through calls, forms, texts, or chats. And by channel- Paid Search, Video, SEO, Paid Social, and Content, for example.

Unifying those touchpoints isn’t just a reporting upgrade. It’s the foundation for any AI-ready lead strategy. Without it, every optimization decision you make is based on an incomplete picture. And in a landscape moving this fast, incomplete data leads to costly missteps.

How To Attribute Calls & Form Fills To AI Search

Take a good look at what is happening with your Voice Assistants. Are forms going to a shared inbox and being missed? Are calls not being answered while another line is in use or after business hours? How long is it taking to follow up with leads? Are those leads going to the competition after you miss the first call?

  1. Consolidate your lead tracking into one platform. If calls, forms, texts, and chats are living in separate tools, you’re creating blind spots. CallRail’s unified lead intelligence platform captures every touchpoint in a single dashboard, so you can see the full customer journey from first AI search to closed deal, and finally answer the question: which channels are actually driving revenue?
  2. Map every conversion point to a marketing source. For each way a lead can reach you -phone call, web form, text, live chat- make sure you can trace it back to the campaign, channel, or keyword that drove it. Use dynamic number insertion for calls and hidden fields on forms to capture source data automatically.
  3. Build a weekly reporting cadence around lead quality, not just volume. Don’t just count leads, score them. Review which sources produce leads that actually convert to appointments and revenue. This is the reporting your clients care about, and it’s how you prove the value of your work

Build the foundation: The Agency Roadmap for 2026 and Beyond

3. Respond Faster To High-Intent AI Traffic

28% of business calls go unanswered. Many of those leads never call back.

Take a good look at your Voice Assistants here. Are your forms going to a shared inbox where they sit unread? Are calls going unanswered because another line is busy or it’s after hours? How long does it take your team to follow up with a new lead? And if you miss that first call from an AI-referred prospect who already has high intent and is ready to buy. Are they going straight to your competitor?

Right now, AI search can understand your customers in real time and answer any question they need, making them perfectly ready to convert into a lead.

Now, it’s you who has to be ready.

Dig into the full data: What 20M Leads Reveal About AI Search and High-Intent Calls

AI Leads Convert Faster. Respond Immediately.

Think about how the traditional funnel used to work. Someone searches, browses a few sites, reads some reviews, maybe sleeps on it, then reaches out. There were days, sometimes weeks, of consideration built into the process.

AI has collapsed that timeline dramatically, and AI-directed callers skip the browsing phase entirely.

They’ve already done their research inside the LLM. By the time they call, they’re ready to make a decision. And they expect you to be ready, too. When a prospect has been pre-qualified by an AI recommendation, every minute of delay costs you revenue.

And the stakes go beyond individual calls.

On platforms like Google, answer speed directly impacts your ad rankings. Faster response times earn better placements on Local Service Ads and PPC -meaning slow follow-up doesn’t just lose you a lead, it quietly erodes your visibility and drives up your cost per lead over time. The agencies winning in an AI-search world aren’t just the ones showing up in LLM recommendations. They’re the ones ready to convert the moment the phone rings -day or night.

Get the playbook: 6 Ways To Prepare Your Business for AI in 2026

Apply AI Where Your Team Is Stretched Thinnest: Use AI to Capture & Qualify Leads Automatically

You can’t automate everything. But knowing where to apply AI, specifically, where your agency or internal team is most stretched, is the difference between using it effectively and adding technology for its own sake.

For most agencies and SMBs, the highest-impact bottleneck is follow-up.

If your clients are missing calls, responding slowly, or losing leads somewhere between the first touch and a booked appointment, that’s exactly where AI can deliver immediate, measurable value.

The key to success here is utilizing AI-powered platforms that can answer inbound calls around the clock, qualify leads in real time, capture intake details, and even book appointments automatically. Early adopters have seen answered calls increase by 44%. That’s not a marginal improvement. It’s the kind of shift that directly impacts revenue and client retention.

How To Set Up AI-Assisted Lead Handling

When you can connect your AI-assisted lead handling back to attribution data and revenue outcomes, you’re no longer just reporting on activities. You’re proving ROI. And that’s what earns long-term client trust- and moves agencies from being seen as just a lead source to being a true growth partner.

  1. Deploy an AI voice agent for after-hours and overflow calls. Start with the windows where your team is least available -evenings, weekends, and lunch hours. CallRail’s Voice Assist answers, qualifies, and captures lead details automatically, so no high-intent caller falls through the cracks. Early adopters have seen answered calls increase by 44%.
  2. Automate follow-up texts immediately after missed calls. If a call does go unanswered, trigger an automatic text within seconds: “Hi, we just missed your call -how can we help?” This simple automation recovers a meaningful percentage of leads that would otherwise be lost.
  3. Connect your AI lead handling back to attribution. Make sure the leads captured by AI tools feed into the same reporting dashboard as your other channels. If your AI agent books an appointment at 9 pm on a Saturday, you should be able to trace that back to the Google Ad or AI search referral that started the journey.

Go deeper: Why The Top Marketers Pair Data With Story

Start Tracking & Optimizing AI-Driven Leads Now

The shift isn’t on the horizon. It’s already here.

It’s time to build AI-aware attribution so you can see what’s actually driving leads, unify your data so you can act on it, and respond fast enough to capture the high-intent leads AI search is already sending your way.

So Your Traffic Tanked: What Smart CMOs Do Next

We’ve all seen it. Brands with healthy websites and excellent content have been watching their organic traffic from Google’s SERP erode for years. In a recent webinar hosted by Search Engine Journal, guest speaker Nikhil Lai, principal analyst of Performance Marketing for Forrester Research, estimated his clients are losing between 10 and 40% of organic and direct traffic year-over-year.

However, a stunning bright spot is this: Lai said referral traffic from answer engines is growing 40% month over month. Visitors arriving from those engines convert at two to four times the rate of traditional search visitors, spend three times as long on site, and arrive with queries averaging 23 words, compared to the three or four words that defined the last decade of search.

Lai asserted that the channel driving this shift deserves a seat at the CMO’s table. Answer engines influence brand perception before purchase intent forms, which makes answer engine optimization (AEO) a brand investment, and puts budget and measurement decisions at the CMO level.

Here is the strategic roadmap Lai laid out at SEJ Live. He highlighted the decisions, org structures, and measurement frameworks that will move AEO from a search team initiative to a C-suite priority.

Answer Engines Build Demand Before Buyers Know What They Want

Classic search captures intent that already exists. A user types “running shoes,” clicks a result, and evaluates options. Answer engines operate earlier and differently: users hold extended conversations with large datasets, rarely click through, and leave those sessions with specific brand associations formed across multiple follow-up questions.

A user who once searched “running shoes” now asks ChatGPT, “What’s the best shoe for overpronation with wide feet in cold weather on pavement?” They exit that conversation with a brand name in mind and search for it directly. Your brand appeared in an AI conversation before the user ever reached your site. Every day, demand generation is created from users’ research sessions.

The Forrester data Lai presented reinforces the quality of that exposure: Sessions on answer engines average 23 minutes, with users asking five to eight follow-up questions per session. Each turn is another brand impression. The click-through rate stays low; the conversion rate on the traffic that does arrive runs two to four times higher than search-sourced traffic, with stronger average order value and lifetime value.

Brand familiarity is built in answer engines before purchase intent crystallizes in the user’s mind.

SEO Is The Foundation Of AEO

The brands pulling back on SEO investment in response to AEO are making a costly mistake. Lai put it directly: 85 to 90% of current SEO best practices remain fully valid for answer engine visibility.

Google’s E-E-A-T framework (experience, expertise, authoritativeness, trustworthiness) still governs how quality is evaluated across every index. Site architecture, mobile load speed, structured data, and indexation hygiene all strengthen performance across every engine. Every alternative index (Bing’s, Brave’s) is benchmarked against Google’s for completeness. Every bot (GPTBot, Claudebot, Perplexitybot) is benchmarked against Googlebot for sophistication.

SEO is the infrastructure on which AEO runs. The shift is an expansion of scope and emphasis, but AEO is not a replacement of SEO fundamentals.

What changes is where additional effort goes: natural-language FAQ optimization, off-site authority building, pre-rendering for less sophisticated bots, and a measurement framework built around share of voice rather than click volume.

Bing Is Now Your Distribution Network For Every Non-Google Engine

Most answer engines outside Google draw primarily from Bing’s index.

Bing evaluates credibility by weighting what others say about your brand more heavily than what your own site claims. This explains why Reddit threads, Quora answers, Wikipedia entries, G2 reviews, YouTube videos, and Trustpilot pages dominate AI-generated answers. The off-site web has become the primary source of record for how AI describes your brand.

The immediate tactical implication: Push every sitemap update directly to Bing via the IndexNow protocol. This triggers Bingbot to crawl fresh content and feeds that content into Perplexity, ChatGPT, and the broader answer engine ecosystem faster than waiting for organic discovery.

Bing’s index remains the fastest route to non-Google answer engine visibility. Perplexity is building its own index (Sonar), and OpenAI has signaled plans to build or acquire one, but Bing is the distribution network that matters today.

AEO Requires Cross-Functional Ownership

AEO arguably spans more functions than SEO, with these three in common with SEO: content, web development, and paid search. AEO also more strongly interfaces with PR, brand marketing, and social media.

PR earns a seat because off-site authority outweighs on-site signals in AEO. Brand mentions in publications, influencer mentions, and third-party reviews all directly shape how answer engines describe your brand.

Social belongs in the room because Reddit threads and Facebook group discussions show up in AI-generated answers. Community management and reputation management, previously handled separately from SEO, are now integral to AEO. When your social listening data reaches content teams before they draft, the content responds to the questions buyers are actually asking. When it doesn’t, you’re optimizing for questions nobody asked.

Lai proposed two organizational models that work to capture the opportunities inherent in AEO:

  1. Center of Excellence: A senior SEO specialist evolves into an AEO evangelist, runs a COE, and publishes cross-functional standards: clear rules like “every piece of content must answer these five questions” or “every page must include author schema.”
  2. AI Orchestrator: A dedicated hire who builds agents to handle repeatable AEO tasks (schema implementation, JavaScript reduction, FAQ content creation) and governs the cross-functional workflow with published guidelines for all stakeholders.

The CMO’s decision is which model fits the organization’s scale, and whether to build it internally or partner with an agency that has already built the infrastructure.

The Content Strategy That Wins In AI Responses

Long-form skyscraper content is an ancient relic. Answer engines reward precise, specific answers to real questions, delivered succinctly and across multiple formats. Lai framed this as Forrester’s question-to-content framework: Every piece of content maps directly to a FAQ being asked on answer engines, including the follow-up questions that emerge within a single session.

Five content moves that produce results:

  1. Build surround-sound FAQ coverage. Create glossaries, FAQ pages, videos, and blog posts that address the same topic cluster from different angles. When Claudebot crawls 38,000 pages for every referred page visit (per Cloudflare data), each page it indexes is an opportunity to signal topical authority. Volume and variety matter.
  2. Publish direct competitor comparisons. Users ask answer engines to compare brands. Brands that create honest, data-backed comparison guides are gaining prominent visibility, because they directly answer the queries being asked that pit a brand against its competitors. This was once a taboo content format; it has become a competitive requirement.
  3. Treat off-site syndication as the new backlinking. Hosting AMAs on Reddit, answering questions on Quora, and contributing to industry publications that rank in AI responses all earn the off-site authority that answer engines weigh most heavily. Give third-party voices data and perspective they couldn’t generate themselves, and they will produce mentions that shape how AI describes your brand.
  4. Pre-render pages for bot access. The bots crawling your site lack the compute budget to render JavaScript-heavy pages. Claudebot’s 38,000:1 crawl-to-referral ratio compared to Googlebot’s 5:1 ratio reflects this sophistication gap. Pre-rendering a JavaScript-free version for bots while serving the full experience to human visitors ensures your content gets indexed across every engine. Over time, limit the amount of JavaScript on site. Have content directly in HTML so bots can understand your content, and index it more often. The more you’re crawled and indexed, the more visible you become.
  5. Create unique content. Lai said, “Being distinctive, differentiated, and unique will help your brand stand out in a sea of sameness. Implicit in all this is that you need a lot more content, greater content velocity and diversity, which means you can use AI to create content. Google won’t automatically penalize AI-created content unless it lacks the watermarks of human authorship. The syntax and diction have to be natural. Use AI to create content, but don’t make it seem AI-generated. Get down into the details. It’s not enough to say your product is great. Explain why in different temperatures, conditions, the thickness, and so on, to satisfy long-tail intent.”

Replace Legacy KPIs With Metrics That Predict Market Share

The internal conversation, Lai said, he hears most from Forrester clients: “The hardest part of this transition from SEO to AEO has been trying to convince management to not focus as much on CTR and traffic. Those were indicators of organic authority. They are no longer reliable indicators.

“The new KPIs to focus on are visibility and share of voice. Share of voice can be measured in many ways. The most common are citation share: how often is my brand cited, how often is my content linked, of the opportunities I have to be cited; and mention share: how often is my brand mentioned of the opportunities I have to be mentioned. I’m also seeing more clients look into citation attempts: how often is ChatGPT trying to cite my content, and are there things I can do on the back end of my site to make that citation attempt score go up? Those are the new indicators of authority,” said Lai.

These metrics connect directly to branded search volume, which Lai called “the single strongest leading indicator of market share growth.” The chain of logic to present to the board: higher citation and mention share drives more branded searches, which converts at higher rates, which compounds into measurable market share gains against competitors.

Lai said he expects Google to add citation metrics to Search Console once AI Max adoption reaches critical mass, and an OpenAI Analytics product before year-end.

For now, Lai suggested, the best course of action is to establish a baseline with your current SEO platform and track the directional trend. Lai contended that, to address concerns of accuracy within today’s popular SEO tools of answer engine mentions, even imperfect measurement reveals which content clusters are earning citations and which need rebuilding.

The Agentic Phase Starts The Clock On B2B Urgency

Answer engines are moving from conversation to action. The current phase, characterized by extended back-and-forth with large datasets, is the warm-up. The agentic phase is defined by engines’ booking, filing, researching, and purchasing on users’ behalf. This will mean fewer clicks, longer sessions, and richer intent signals available to advertisers.

For B2B CMOs, the urgency is immediate. Forrester research shows GenAI has already become the number one source of information for business buyers evaluating purchases of $1 million or more, coming in ahead of customer references, vendor websites, and social media. Your largest deals are being influenced by AI conversations before your sales team enters the picture.

AEO visibility in B2B is a current-pipeline variable that requires immediate attention.

The brands building complete search strategies now, covering answer engines, on-site conversational search, and structured data across every indexed channel, will own discovery and have greater control over brand perception in the next phase of buying behavior.

The window to gain an early-mover competitive advantage is shrinking, before AEO visibility becomes just another standard expectation everyone has to meet.

Key Takeaways For CMOs

  • Reframe the traffic story. Lower overall traffic volume paired with two-to-four-times higher conversion rates is a net performance gain. Build that case proactively before your CEO draws the wrong conclusion from a falling traffic chart.
  • Fund AEO as an upper-funnel brand channel. That means applying the same budget logic, measurement frameworks, and executive ownership you would bring to any major brand awareness investment, where success is measured in visibility, perception, and long-term share of voice rather than clicks and conversions.
  • Move to share-of-voice KPIs. Citation share and mention share drive branded search volume, which drives market share. Make that causal chain visible to your leadership team.
  • Assign cross-functional ownership with clear governance. Choose between a center of excellence or an AI orchestrator model and make that structural decision this quarter.
  • Prioritize off-site authority as a content strategy responsibility. Reddit, Quora, third-party publications, and YouTube shape AI’s perception of your brand. PR and social teams own the channels that matter most for AEO.
  • Push every sitemap update to Bing via IndexNow. Bing’s index feeds most non-Google answer engines. This is a 15-minute technical change with compounding distribution benefits.
  • Use AI to help with content, but always apply human editing for authority. Content that reads as machine-generated loses trust across every engine, including Google.

What Does A Smart CMO Do Next?

Start with a 90-day experiment using some or all of these strategies.

Audit your current citation and mention share in one category using your existing SEO platform. Identify three high-intent FAQ clusters where your brand should be visible and build surround-sound content for each: a dedicated FAQ page, a comparison guide, and one off-site piece in a publication that appears in AI responses. Push fresh sitemaps to Bing. Track citation share and branded search volume at 30, 60, and 90 days.

The data may make the investment case for broader rollout. If not, tweak your approach. The brands moving first will capture the highest-quality traffic at the lowest incremental cost, and set the citation baseline that becomes progressively harder for competitors to close.

The full webinar is available on demand.

More Resources:


Featured Image: Dmitry Demidovich/Shutterstock

How To Identify Which LLM Is Actually Working For You [Webinar] via @sejournal, @hethr_campbell

AI search is dominating the strategy conversation right now, and everyone is hearing the same thing from clients and directors: “What’s our AI search plan?”

The instinct is to optimize everywhere, ChatGPT, Perplexity, Gemini, and move fast. But before you reallocate budget or rewrite your GEO roadmap, there’s a more useful question to ask first:

Which LLM is actually driving conversions in your clients’ specific industry?

Join us for an upcoming expert panel webinar where we’ll dive into exactly that.

What You’ll Learn

In this webinar, Danielle Wood, Content & Creative Manager at CallRail, and Natalie Johnson, SEO & AI Visibility Expert & Founder of SweetGlow Marketing, will break down real conversion data by LLM and show how platform-level performance should shape your GEO strategy.

Specifically, you’ll walk away with:

  • Conversion data by LLM platform, so you know where high-intent traffic is actually coming from in each industry
  • A clear AI prioritization framework to stop spreading GEO effort equally and concentrate it where it converts
  • A reporting model that ties AI search activity to real business outcomes clients can see and trust

Why Attend?

You’ll finally be able to justify AI search investment; this session will give you the data and the framework to make that case and to implement the strongest, most successful AI search strategy possible.

Join us live to get your questions answered directly by the expert panel.

SEO 2.0: How Content Marketing Drives Visibility in AI Search via @sejournal, @hethr_campbell

The next evolution of SEO is unfolding right now: AI is changing how people discover brands & content.

Is your content cited ChatGPT, Gemini, Copilot, & AI Overviews?

How do you become a trusted source for AI citations?

Can you intentionally influence AI search outputs?

Yes, you can.

In this on-demand webinar, you can gain a practical, content-first framework for improving visibility in AI-powered search, plus learn:

How To Build The Content Signals AI Systems Actually Surface & Cite

This on-demand session breaks down how large language models retrieve, evaluate, and reference content, and walks through what that means for your upcoming SEO and content strategy.

You’ll walk away with a practical framework for building citation-worthy, AI-visible content that strengthens both traditional SERP rankings and AI recommendations.

You’ll Learn:

  • How to improve off-site mentions to boost AI mentions and citations.
  • Which content is citation-worthy, so you can build a powerful trust engine.
  • Exact traditional SEO advantages you should still consider.
5 GEO Strategies To Make AI Search Engines Recommend Your Brand In 2026

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

The way people search is changing faster than most marketers realize. ChatGPT alone now has over 900 million weekly active users. Google AI Overviews appear in one out of every four search results.

Each of these contains the potential for AI to cite your brand.

This isn’t a future trend. It’s happening right now. And if your brand isn’t showing up in those AI-generated answers, you’re invisible to a rapidly growing audience, even if you rank #1 on Google.

That’s where Generative Engine Optimization (GEO) comes in: the practice of optimizing your online presence. So, AI engines cite, reference, and recommend your brand when users ask questions in your space.

1. Start By Measuring Your AI Visibility

Before changing a single word on your website, you need to know where you stand. Which AI platforms mention your brand? For which queries? How often are your competitors getting cited instead of you?

You can’t optimize what you don’t measure.

How To Measure AI Visibility

Most marketers skip this step because it feels unfamiliar. But the process is straightforward.

  1. List 10–15 questions your ideal customer would ask an AI engine, things like “best [your category] for [use case]” or “how to solve [problem you address].”
  2. Run each query in ChatGPT, Perplexity, and Gemini.
  3. Note whether your brand is mentioned, which competitors show up instead, and whether sources are cited.

Repeat monthly, because AI-generated answers shift as models update and new content gets indexed. Doing this manually across multiple platforms gets tedious fast, which is why dedicated GEO platforms exist to automate the tracking and monitor changes over time.

The best place to start? Run a free geo rank check on your brand. In under a minute, you’ll see which AI engines mention you, which ones don’t, and where your competitors show up instead.

This baseline is essential. Without it, you’re optimizing blind.

2. Don’t Abandon SEO. It Still Feeds AI

Here’s an important nuance: traditional search rankings still matter for GEO.

AI engines frequently pull from top-ranking Google results when generating their responses. If your page ranks well for a relevant query, there’s a higher chance an AI engine will reference it as a source. Google’s own AI Overviews heavily favor content that already performs well in organic search.

So keep doing what continues to drive SERP rankings:

  • Producing high-quality content
  • Building backlinks
  • Technical SEO.

But think of SEO as the foundation, not the full strategy. The brands that win in AI search are those that layer GEO tactics on top of a solid SEO foundation.

3. Make Sure Your Content Follows GEO Best Practices

This is where most of the work happens. AI engines are selective about what they cite, and the structure and quality of your content play a massive role. Here’s what to focus on:

  • Write for citability, not just readability. AI engines look for content that makes clear, specific claims backed by data or expertise. Vague, fluffy paragraphs get skipped. Concrete statements like definitions, statistics, step-by-step processes, and expert opinions are far more likely to be pulled into a generated response.
  • Structure content around questions. Conversational AI is driven by user questions. Structure your content to directly answer the questions your audience asks. Use clear headers, concise paragraphs, and FAQ When an AI engine scans your page and finds a clean, authoritative answer to a specific question, you become a prime candidate for citation.
  • Leverage schema markup and structured data. Help AI engines understand what your content is about by implementing proper schema FAQ schema, How-To schema, and Organization schema all give AI systems stronger signals about your content’s topic and structure.
  • Build topical authority, not just keyword-specific content. AI engines favor sources that demonstrate deep expertise on a topic. Rather than publishing scattered blog posts across dozens of topics, build comprehensive content clusters that cover a subject thoroughly. This signals to AI engines that your brand is a reliable authority worth citing.

Pro Tip: Leverage a comprehensive GEO platform. Optimizing your content for AI search involves many moving parts: content structure, schema markup, topical authority, and technical SEO. Keeping track of all these signals manually across every page on your site isn’t realistic, especially as AI engines update how they evaluate sources. A dedicated GEO platform lets you regularly scan your entire website, monitor your optimization scores, and catch issues before they cost you citations.

Want to see where you stand right now? Run a free GEO audit and get actionable insights on your site’s AI readiness in under a minute.

4. Show Up In Reddit & UGC Discussions

Here’s a strategy most brands overlook: AI engines love Reddit.

If you’ve noticed Reddit threads showing up in Google results more frequently, that’s not a coincidence. Google and AI platforms increasingly treat user-generated content, especially Reddit, as a trusted and authentic source of information. When someone asks an AI engine for a product recommendation or solution comparison, the response often draws from Reddit discussions.

This means your brand’s presence in relevant threads matters more than ever. But you can’t just show up and start promoting yourself. Here’s how to approach it the right way:

  • Find where your audience is already talking. Search Reddit for your product category, your competitors’ names, and the problems you solve. Identify 5–10 active subreddits where these conversations happen. Look for threads like “what tool do you use for [your category].”  These are the discussions AI engines pull from.
  • Contribute before you promote. Spend at least 2–3 weeks genuinely participating before your brand ever comes up. Reddit users check post history, and if your account is nothing but product mentions, you’ll get flagged as spam.
  • Be honest, not salesy. When a relevant recommendation thread comes up, share your product as one option among others. Mention what it’s good at and where it might not be the best fit. AI engines weigh authentic, nuanced mentions far more heavily than obvious self-promotion.
  • Check what AI engines are citing. Run your core queries in ChatGPT and Perplexity and see which Reddit threads appear. If your brand isn’t in those threads, that’s where to focus.

5. Get Featured In Listicles On Trusted Sites

When users ask AI engines for recommendations like “best project management tools,” the AI doesn’t generate that list from scratch. It synthesizes from existing listicle articles on authoritative websites. A single placement in a well-ranking listicle can get your brand recommended across ChatGPT, Perplexity, and Google AI Overviews simultaneously.

  • Find the listicles AI engines are already citing. Run your target recommendation queries in ChatGPT and Perplexity and note which articles they reference. These are the exact listicles you need to be in.
  • Build a hit list of publishers. Identify publications that come up repeatedly across both AI and traditional search results for “best [your category]” queries. Prioritize sites with strong domain authority.
  • Make inclusion easy. Make sure your product pages have a clear one-liner, obvious differentiators, social proof, and transparent pricing. Then pitch authors with something valuable, such as a free account, a demo, or data they can use.

Listicles get updated regularly and AI engines re-scan them, so a placement you earn today could start driving AI citations within weeks.

The Window Is Open, For Now

Generative Engine Optimization is still in its early stages. Most brands haven’t even started thinking about it, which means the opportunity to establish an early advantage is enormous.

The brands that start measuring their AI visibility, optimizing their content for citability, building community presence, and earning placements in authoritative listicles today will be the ones AI engines default to recommending tomorrow.

The question isn’t whether AI search will matter for your business. It’s whether you’ll be visible when it does.

Start Optimizing For AI Search Today

Every strategy in this article comes down to one thing: making your brand the obvious choice when AI engines look for sources to cite and recommend. You don’t need to tackle everything at once, but you do need to start.

Geoptie brings all five strategies together in one platform, from tracking your AI visibility across ChatGPT, Perplexity, and Google AI to auditing your content and monitoring your optimization scores over time. It’s built specifically for GEO, so you can stop guessing and start seeing exactly where your brand stands in AI search.

The early movers will own this space. Make sure you’re one of them.


Image Credits

Featured Image: Image by Tor App. Used with permission.

How To Track AI Visibility & Prompts The Right Way via @sejournal, @lorenbaker

AI search has changed the rules, but has your tracking? 

How do you measure visibility without rankings?

Which prompts actually reflect real buyer intent?

And how do you avoid AI tracking data that looks useful, but isn’t?

Learn how to set up AI prompt tracking you can trust for smarter decisions.

ChatGPT, Google AI Overviews & Perplexity Are Reshaping Discoverability

In this on-demand webinar, Nick Gallagher, Sr. SEO Strategy Director at Conductor, breaks down how AI prompt tracking really works, why topics matter more than individual prompts, and how to avoid common mistakes that skew insights.

You’ll leave with a clear framework for measuring AI visibility in a way that reflects real user behavior and supports smarter search and content strategies.

You’ll Learn:

  • How AI prompt tracking works, and why setup matters more than volume
  • Best practices for choosing topics, prompts, and answer engines
  • Common mistakes that lead to inaccurate or misleading AI visibility data

Watch on-demand and learn how reputation management is shaping local visibility, trust, and growth in 2026.

View the slides below or check out the full webinar for all the details.