Over the last year, I’ve had a lot more conversations with clients and prospects regarding improving their AI visiblity (AEO/GEO).
Usually the question sounds something like, “What do we need to do differently so we show up first in ChatGPT?”
Or, “Is SEO still worth investing in if people are moving to AI search?”
Those are very reasonable questions, but I think, like many, they start from the wrong assumption. AEO is not replacing SEO. If anything, what we are seeing so far suggests that companies with strong SEO fundamentals may actually have an advantage as more search behavior moves into AI-generated answers.
That does not mean ranking #1 in Google guarantees ChatGPT will recommend or priortize your brand.
But it does mean companies should be careful about abandoning the fundamentals of SEO in pursuit of whatever the latest “AEO hack” happens to be.
For business leaders in the ERP space I think the better strategy is much simpler: get SEO right first, then build on it for improved AI visibility.
Traditional SEO is fairly easy to understand. Someone searches Google for “NetSuite implementation partner,” Google decides which pages are most relevant and useful, and your aim is to earn one of those top 10 (page 1) positions.
AEO changes the experience. The same buyer might now ask ChatGPT, Gemini, Perplexity, or Google AI Mode, “We are a $75 million food manufacturer with three locations. Which ERP systems should we consider, and which implementation partners have experience in our industry?”
That is a much bigger and more complex question. The AI may research the ERP category, compare NetSuite with Acumatica, Dynamics or IFS, look at food manufacturing requirements, consider lot traceability and integrations, and then decide which implementation firms are worth mentioning.
In SEO, the question is often, “Can we get this page to rank?” In AEO, another question gets added: “Does the system understand enough about our company to know when we belong in the answer?”
That difference is important, but it does not mean the two disciplines are disconnected.
Imagine two NetSuite consulting firms competing for the same manufacturing client.
One has invested in detailed service and industry pages, food and beverage manufacturing case studies, location-specific content, implementation resources, independent G2 reviews, and years of established organic visibility. This is the type of foundation we emphasize when discussing SEO and lead generation for NetSuite partners.
The other has a five-page website with generic messaging about digital transformation and little evidence of actual implementation experience.
Now imagine a buyer asking ChatGPT, "Which NetSuite implementation partners have experience working with food and beverage manufacturers in or around New Jersey?"
Which company gives the system more relevant, credible information to work with?
AI search can evaluate several dimensions of a buyer's requirements at once, including ERP platform, industry specialization, service type, geography, and demonstrated experience. It can then look across multiple sources to identify potentially suitable partners.
An ERP partner that has invested in documenting those capabilities across its website, customer case studies, and independent reviews has more evidence available to support those connections.
That's why I don't believe abandoning SEO in favor of AEO makes sense. Much of the work required to establish expertise and organic search visibility also helps position ERP partners to compete for increasingly specific recommendations in AI-powered search.
There is now some interesting research supporting the connection between SEO strength and AI visibility.
Semrush analyzed 10,000 domains in its AI Visibility Index and found a 0.86–0.87 correlation between the number of organic keywords a website ranked for in Google and the number of times it was cited in AI-generated answers.
In simpler terms, websites that perform well in traditional organic search also tend to be referenced more frequently by AI search platforms.
Semrush is careful to point out that correlation does not mean causation. Strong SEO rankings don't automatically cause AI systems to cite your website, but the relationship is hard to ignore. Read the Semrush study
Ahrefs found something similar while analyzing roughly four million Google AI Overview citations. About 38% of URLs cited in AI-generated answers also appeared within the top 10 traditional Google search results for the same query.
Put another way, nearly four out of every ten sources referenced in Google's AI Overviews were already appearing prominently in Google's traditional search results. Read the Ahrefs study
But this is where things get interesting: roughly 62% of those citations came from outside the top 10.
That tells us something important. AI search isn't simply taking the highest-ranking Google results and turning them into a written answer. It can draw from a much broader collection of sources when researching a question.
There are two important takeaways from this research.
First, investing in SEO appears to support AI visibility. The work you do to develop useful website content, demonstrate expertise, and establish credibility in traditional search may also help your company become a source for AI-generated answers.
Second, you don't necessarily have to rank on Google's first page to be included in an AI-generated answer. AI platforms can surface information from websites that aren't among the highest-ranking traditional search results. This creates additional opportunities to optimize content for AI-generated search results.
For ERP partners and enterprise software companies, this reinforces an important point: abandoning SEO to pursue AEO would be a mistake. The better opportunity is to build on your existing SEO foundation while developing the depth of content and evidence that helps AI systems understand when your company is relevant to a buyer's specific needs.
The bottom line: Strong SEO and AI visibility are connected, but they aren't interchangeable. Good SEO gives you a foundation to build on, while AEO introduces additional opportunities to be discovered and recommended.
This is where AEO starts getting genuinely different from traditional SEO. Google has publicly described a process called query fan-out in AI Mode. Instead of searching once and returning ten results, the system can break a question into multiple related searches, investigate those subtopics and then assemble an answer.
Google explains AI Mode and query fan-out here: https://blog.google/products-and-platforms/products/search/ai-mode-search/
Think about a buyer asking, “What is the best ERP for a $100 million food manufacturer?” Behind that question are a lot of smaller questions.
What platforms work well in food manufacturing?
Which ones support lot traceability?
What about recalls?
How does NetSuite compare with Dynamics?
What will implementation cost?
Which systems can handle multiple facilities?
Which implementation partners actually understand food manufacturing?
The AI can explore that wider information landscape before producing the answer. That has a major implication for content strategy: you cannot create one page called “Best ERP for $100 Million Food Manufacturers” and assume you have optimized for the prompt.
You need real depth around the whole problem. The more useful information your company has across those related topics, the more opportunities there are for your content and brand to become part of the research process.
Again, that sounds like good SEO fundamentals, right?
One of the more significant developments with AI-powered search is how these platforms evaluate and recommend companies.
In traditional Google search, ranking near the top generally means your page is considered relevant to the query. The search engine presents results, and the buyer decides which companies to investigate.
AI platforms can take that evaluation further. They can compare providers, summarize strengths and weaknesses, and explicitly advise a prospective customer against selecting a particular company.
Imagine a manufacturer asking ChatGPT: "Which NetSuite implementation partners should we consider for a food and beverage manufacturing project, and are there any NetSuite consulting firms we should avoid?"
The response might recommend three firms based on their industry experience, implementation history, and customer reviews. It could also discourage the buyer from considering another partner because of reported implementation problems, poor customer feedback, or limited experience in food manufacturing.
In fact, there's already research documenting this behavior. In an October 2026 study, Parse analyzed responses from ChatGPT Search and Google AI Mode. It found that 17.1% of responses to questions explicitly asking which brands to avoid included a warning about at least one named company. Even when buyers weren't asking which companies to avoid, 4.5% of responses included a warning. Source: Parse: Brands AI Tells You to Avoid
Although the research wasn't specific to ERP implementation partners, the findings raise an important consideration for firms competing for high-value consulting and implementation projects.
A company could appear several times in an AI-generated response because of negative customer feedback, perceived weaknesses, or implementation concerns. Depending on how an AEO reporting tool measures visibility, those mentions might contribute to a favorable visibility score, even though the response could discourage the buyer from considering that company.
There's also the question of accuracy. AI systems can rely on outdated reviews, misunderstand a company's capabilities, or draw incorrect conclusions from incomplete information.
When evaluating AI visibility for our clients, I think we need to understand three things:
A negative AI recommendation could influence which firms make the buyer's initial shortlist, potentially before the buyer visits a single provider's website.
For that reason, we're paying closer attention to how AI platforms characterize our clients and their competitors, alongside traditional organic rankings, citations, and referral traffic.
Through our work with ERP partners and resellers, we're beginning to see some consistent patterns in the companies performing well in both traditional organic search and AI-powered search.
We're observing this through a combination of Semrush organic keyword tracking, HubSpot's AEO prompt monitoring, manual testing across AI platforms, and analysis of organic and AI referral traffic.
Three characteristics repeatedly stand out.
1. Independent customer reviews and third-party validation
An ERP partner’s website explains its services and experience. Third-party sources help buyers assess whether those claims hold up.
Customer reviews on G2 offer insight into what it is like to work with the firm. ERP vendor partner directories can confirm its relationship with a platform, while coverage in credible industry publications can provide additional context about its expertise.
Together, these sources give buyers and AI systems researching on their behalf—more evidence to consider when comparing implementation partners or evaluating customer satisfaction.
2. Detailed case studies demonstrating relevant industry experience
AI-powered search can investigate very specific combinations of buyer requirements.
For example, a food and beverage manufacturer in New Jersey might want a NetSuite implementation partner with experience in manufacturing, inventory management, lot traceability, and multi-location operations.
A general NetSuite implementation page establishes basic relevance. Detailed customer case studies can provide evidence of experience addressing those specific requirements.
The ERP partners we see performing well tend to document their experience across industries, ERP platforms, implementation services, integrations, and customer outcomes.
3. Comprehensive website content covering the markets they serve
ERP partners often serve multiple industries, offer several service types, and operate across different geographic markets. This creates a particular challenge for ERP software consulting firms investing in digital marketing and lead generation, since their websites need to communicate expertise across several different buyer segments.
The strongest websites clearly document these capabilities through industry pages, service pages, integration resources, location-specific content, and implementation guides.
That creates opportunities to establish relevance across different combinations of buyer requirements.
When AI systems research an ERP partner for a specific industry, location, and service type, a well-developed website can provide information that helps establish whether the company is a suitable match.
We also see these firms connecting related content through internal linking, making it easier for visitors and search engines to navigate their areas of expertise.
Taken together, these characteristics create a more complete picture of an ERP partner's capabilities. Their website documents the services and markets they support, case studies demonstrate relevant experience, and independent sources help validate their claims.
We believe this combination contributes to stronger visibility across traditional and AI-powered search, although the relative influence of each factor can vary by platform and buyer query.
There's another important observation from our client work that tends to get lost in the AEO conversation: AI referral traffic is still a relatively small portion of overall website traffic.
Across the enterprise software and technology consulting companies we work with, we're generally seeing identifiable AI referral traffic account for no more than 5% of total website traffic.
And we're not alone in seeing this disconnect between the excitement surrounding AI search and the actual scale of traditional search.
In her 2026 Affiliate Summit West keynote, The State of Search and AI in 2026, SEO researcher Lily Ray presented Similarweb data comparing traffic to major AI platforms with Google Search.
The data tells an interesting story. ChatGPT has experienced tremendous growth, but Google Search continues to operate at a substantially larger scale in worldwide desktop web visits. Ray also highlighted research indicating that approximately 95% of ChatGPT users continue using Google. That is audience overlap not a measure of the share of searches handled by either platform.
Source: Lily Ray, Affiliate Summit West 2026; Similarweb worldwide desktop visit estimates. Link to original keynote: https://www.slideshare.net/slideshow/affiliate-summit-west-lily-ray-keynote-2026/286005569
That doesn't mean AI traffic isn't valuable. In fact, we want more of it. Someone arriving from ChatGPT, Perplexity, or another AI platform may have already researched their options, compared solutions, and narrowed down potential providers before visiting your website.
There's also an important measurement limitation. Not every visit influenced by an AI-generated answer will show up as AI referral traffic. Some visitors may search for a company by name, return directly, or reach the website through another channel after encountering the brand in an AI response.
The takeaway isn't to ignore AEO. It's to recognize that your buyers are using both traditional search and AI-powered tools and Google still maintains a dominant share of the visits.
If you're running marketing for an ERP partner, I wouldn't throw out your existing SEO reports and replace them with a dashboard showing how often your company appears in ChatGPT.
Keep tracking organic traffic, keyword rankings, leads, and conversions. Those metrics still matter, and for most of the enterprise software and consulting companies we work with, traditional organic search continues to generate substantially more website traffic than AI referrals.
But AI search introduces another layer of visibility that we need to understand. For our clients, we're increasingly looking at six measurements:
| Measurement | What we're trying to understand |
|---|---|
| Organic search traffic | Are we attracting more prospective buyers through traditional search? |
| AI referral traffic | How much measurable website traffic is coming from ChatGPT, Perplexity, Gemini and other AI platforms? |
| AI recommendation frequency | When buyers ask AI systems for ERP partners with specific industry or platform expertise, how often is our client actually recommended? |
| AI citation frequency | How often is our client's website referenced as a source in AI-generated answers? |
| Organic and AI conversions | Are these visitors turning into inquiries, consultations and qualified leads? |
| Qualified sales pipeline | Are our SEO and AEO investments contributing to legitimate sales opportunities and revenue? |
AI referral traffic is measurable, but AI's broader influence on purchasing decisions is harder to attribute. A buyer might discover your company in ChatGPT, then return through Google or direct traffic weeks later.
Ultimately, I don't want clients measuring success by how many times they appear in AI-generated answers. I want to know whether we're reaching the right buyers, generating qualified leads and helping the sales team build pipeline.
That's how SEO and AEO should be evaluated: not as competing marketing channels, but as complementary investments in customer acquisition.
If I were running an ERP partner or enterprise software consulting firm, I’d treat AEO as an extension of my SEO strategy, not a reason to abandon it.
I’d keep investing in clear service pages, useful industry content, detailed customer case studies, independent reviews, and a technically sound website. Those are the foundations that help buyers find the business and evaluate its expertise.
Alongside that work, I’d track how AI platforms describe and recommend the firm. Where does it appear in relevant buyer questions? Where is it missing? And does the information accurately reflect its services, experience, and capabilities?
For the ERP partners we work with, success ultimately comes down to building qualified sales pipeline, not simply increasing rankings or AI mentions.
That’s how I’d make SEO and AEO investment decisions: strengthen what is generating business now, build visibility where buyers are beginning to look, and evaluate both against the sales opportunities they help create.