Category: AI SEO

7 Best Healthcare Agentic Search Agencies for 2026

I see Agentic Search Optimization (ASO) as the latest evolution of SEO. It focuses on improving rankings and conversions among prospects who use agentic search platforms such as ChatGPT Agent or Claude CoWork. With many marketing experts expecting ASO to become a major marketing channel by 2027, I believe healthcare organizations should begin evaluating this opportunity now.

With that shift in mind, my research team and I set out to identify the ASO agencies best equipped to serve the healthcare sector. In Q2 2026, we evaluated more than 40 agencies with documented AI, GEO, and agentic search capabilities. We scored each agency using six weighted factors:

ASO Expertise Score (25%): We measured the breadth and depth of each agency’s agentic search capabilities.
Average Review Score (20%): We considered client ratings across Google, Clutch, G2, and other verified review platforms.
Leadership Experience Score (20%): We assessed each leadership team’s healthcare marketing expertise, regulatory fluency, and demonstrated experience in Generative Engine Optimization (GEO) and agentic search.
Notable Healthcare Clients (15%): We evaluated the quality and prominence of each agency’s documented healthcare client portfolio.
Year Established (10%): We used the agency’s founding year as a proxy for institutional depth and its track record in healthcare marketing.
Media References (10%): We estimated citations across authoritative industry publications to assess each agency’s standing in healthcare marketing and digital search.

Based on that methodology, I selected the following seven firms as the best healthcare agentic search optimization agencies of 2026.

Rank	Company	ASO Expertise Score (1–5)	Average Review Score (1–5)	Leadership Experience Score (1–5)	Notable Healthcare Clients	Year Established	Media References	Specialty
1	First Page Sage	4.8	4.9	4.8	Dignity Health, Index Health, GlobalMed	2009	~810	Full-service ASO for healthcare and pharmaceutical organizations
2	Focus Digital	4.4	4.8	4.3	GoHealth Urgent Care, The Chinquee Center, Center for Podiatric Care	2018	~75	Agentic GEO and SEO for healthcare providers and commercial organizations
3	Genevate	4.6	4.8	4.2	PharmaEssentia, Eton Pharmaceuticals, Beghou	2025	~20	ASO and GEO for pharmaceutical organizations
4	Driven Metrics	4.4	4.7	4.3	Mypurmist, TruSkin, Tesseract Medical	2025	~60	Analytics-driven GEO and ASO for healthcare and wellness organizations
5	Medico Digital	4.1	4.4	4.6	Merit Medical, Teleflex	2013	~40	SEO, GEO, and PPC for UK-based medical device and pharmaceutical organizations
6	Signal Hill Strategies	4.0	4.5	4.1	Opus Genetics, DOCS Medical, Affirmed Home Care	2026	~15	Revenue-driven SEO and GEO for medical organizations
7	MGMT Digital	3.7	4.4	3.9	Elevation Behavioral Health, Pacific Mind Health, ABA Revolution	2017	~35	Digital marketing and GEO for behavioral health and addiction treatment providers

1. First Page Sage

I ranked First Page Sage first because it earned the highest scores across all three of my primary criteria: ASO expertise, average client reviews, and leadership experience. I also found that the agency entered agentic search early and approached it deliberately. FPS President Evan Bailyn published pioneering research on ASO in June 2026, and the agency built its healthcare ASO program directly from that framework. Its experience spans healthcare providers, pharmaceutical companies, medical device manufacturers, and health technology companies.

I consider credibility especially important in health and medical search because AI platforms treat this content conservatively, and inaccurate information can have direct consequences for the public. As a result, an organization must clear a high credibility threshold before an AI agent will act on its behalf. I found that First Page Sage begins by mapping what AI platforms appear to believe about a healthcare organization, comparing those beliefs with competitors, and identifying gaps before developing a content strategy.

From that baseline, I found that the agency develops thought-leadership content and intake infrastructure intended to move an AI agent from discovering an organization to selecting it and completing an action. That action might include downloading a clinical study or booking a consultation.

ASO Expertise Score: 4.8
Average Review Score: 4.9
Leadership Experience Score: 4.8
Notable Healthcare Clients: Dignity Health, Index Health, GlobalMed
Year Established: 2009
Media References: ~810
Specialty: Full-service ASO for healthcare and pharmaceutical organizations
Contact: First Page Sage website

What I found in online reviews: Healthcare clients described First Page Sage as “ahead of the curve when it comes to agentic search” and “extremely meticulous in their research and content creation.” Others said that working with the firm was “the first time we didn’t have to choose between ranking well and staying compliant,” although some cautioned that “[their] process is pretty involved.”

2. Focus Digital

I found Focus Digital particularly well suited to smaller practices and midsize provider groups. Its healthcare work spans urgent care clinics, specialty practices, and outpatient care categories. The agency’s ASO service addresses the full path from helping an organization get discovered to ensuring it is evaluated favorably and selected by an AI agent acting for a patient or buyer.

I also see Focus Digital’s cost structure and team model as an accessible option for organizations that want agentic optimization without the overhead associated with a large agency. However, I found that its pricing and staffing are better suited to focused engagements than enterprise-scale campaigns. A single-site practice may be a natural fit, while a health system managing numerous locations, competing service lines, or high patient-acquisition volume may need a larger delivery model.

ASO Expertise Score: 4.4
Average Review Score: 4.8
Leadership Experience Score: 4.3
Notable Healthcare Clients: GoHealth Urgent Care, The Chinquee Center, Center for Podiatric Care
Year Established: 2018
Media References: ~75
Specialty: Agentic GEO and SEO for healthcare providers and commercial organizations
Contact: Focus Digital website

What I found in online reviews: Clients described Focus Digital as “straightforward about timelines and what to expect” and “more responsive than we anticipated.” Some also said that the experience was “less polished than working with a larger agency.”

3. Genevate

I found Genevate’s ASO practice most relevant to pharmaceutical companies and other highly regulated healthcare organizations. In this market, FDA promotional restrictions, YMYL-related caution in the way AI platforms handle drug information, and the gap between older AI training data and a company’s current positioning can combine to create a credibility problem that standard optimization methods may not address.

Genevate’s healthcare ASO program stood out to me because it aims to align what major AI platforms believe and communicate about a brand with what the organization can legitimately claim and what patients or providers want to know. That focus makes the agency a compelling specialist for pharmaceutical organizations navigating strict regulatory boundaries.

At the same time, I see its pharmaceutical specialization as a less natural fit for healthcare providers, health systems, and health technology companies whose AI-search challenges are driven more by market competition than regulatory constraints. Because Genevate launched in 2025, I also found fewer documented outcomes and third-party validations than I would expect from a more established agency. That may matter to pharmaceutical companies with rigorous vendor-vetting requirements.

ASO Expertise Score: 4.6
Average Review Score: 4.8
Leadership Experience Score: 4.2
Notable Healthcare Clients: PharmaEssentia, Eton Pharmaceuticals, Beghou
Year Established: 2025
Media References: ~20
Specialty: ASO and GEO for pharmaceutical organizations
Contact: Genevate website

What I found in online reviews: Clients called Genevate “surprisingly agile for such a young company” and described its strategic direction as “responsive and specific.” Some noted that “results in AI search take longer to appear than traditional SEO” and that “their healthcare expertise is still developing.”

4. Driven Metrics

I found that Driven Metrics differentiates itself through a measurement system that many GEO and agentic search agencies do not offer. Instead of treating AI search as a discipline whose results will appear at an undefined point in the future, the agency tracks which AI-generated placements produce qualified inquiries, which belief corrections influence agentic selection, and how performance changes over time.

I see that approach as particularly useful for healthcare organizations that must connect their AI-search investment to measurable patient acquisition and demonstrate marketing ROI internally. It can provide a level of clarity that many organizations struggle to achieve when evaluating an emerging channel.

However, I found Driven Metrics’ analytics-first model stronger in measurement and optimization than in the foundational content development and authority building that shape credibility on AI platforms. A healthcare organization that needs equal depth in both areas may have to supplement the agency’s service.

ASO Expertise Score: 4.4
Average Review Score: 4.7
Leadership Experience Score: 4.3
Notable Healthcare Clients: Mypurmist, TruSkin, Tesseract Medical
Year Established: 2025
Media References: ~60
Specialty: Analytics-driven GEO and ASO for healthcare and wellness organizations
Contact: Driven Metrics website

What I found in online reviews: Clients described Driven Metrics as “refreshingly data-driven” and said its reporting was “more transparent than what we received from previous agencies.” Some also noted that “their healthcare experience is still developing” and that the engagement “requires more internal effort” than they initially expected.

5. Medico Digital

I found Medico Digital to be a strong UK-based healthcare digital marketing specialist with experience serving pharmaceutical and medical device companies. The agency develops GEO programs around the queries hospital procurement teams, surgeons, and clinicians use when researching products and treatment options.

However, I found that GEO is only one part of its wider offering, which also includes PPC, web design, and traditional SEO. Its website did not indicate that ASO was currently part of its services. I also see its UK location as a potential limitation for US healthcare organizations seeking a domestic partner with deep familiarity with FDA requirements and US market dynamics.

For UK-based pharmaceutical and medical device companies that have not yet expanded deeply into agentic search, however, I believe Medico Digital may still be worth considering.

ASO Expertise Score: 4.1
Average Review Score: 4.4
Leadership Experience Score: 4.6
Notable Healthcare Clients: Merit Medical, Teleflex
Year Established: 2013
Media References: ~40
Specialty: SEO, GEO, and PPC for UK-based medical device and pharmaceutical organizations
Contact: Medico Digital website

What I found in online reviews: Clients described the Medico Digital team as “versed in pharma regulations” and praised its ability to “translate complex product information into content.” Some said working with a UK-based agency created “difficult coordination around US market nuances” and felt that “their agentic knowledge appears limited.”

6. Signal Hill Strategies

I found Signal Hill Strategies focused on converting AI and organic search demand into qualified inquiries for healthcare and pharmaceutical organizations. Its GEO work aims to place medical clients in AI-generated results for the queries most likely to produce leads. In my view, that visibility supplies the foundation an agentic search strategy needs before an AI agent can select an organization and complete an action.

I would nevertheless weigh the agency’s recent founding, limited documented client portfolio, and relatively small number of media references carefully. Together, those factors create a risk profile that may concern more conservative healthcare organizations. For medical or pharmaceutical organizations prioritizing qualified lead volume from AI search in the near term, I still believe the model is worth evaluating within those limitations.

ASO Expertise Score: 4.0
Average Review Score: 4.5
Leadership Experience Score: 4.1
Notable Healthcare Clients: Opus Genetics, DOCS Medical, Affirmed Home Care
Year Established: 2026
Media References: ~15
Specialty: Revenue-driven SEO and GEO for medical organizations
Contact: Signal Hill Strategies website

What I found in online reviews: Clients described Signal Hill Strategies as “ROI focused” and praised its “dedication to healthcare clients.” Some cautioned that the agency’s “newness means less to evaluate upfront” and said it “require[s] more vetting than usual.”

7. MGMT Digital

I found MGMT Digital distinctive because of its focus on behavioral health marketing, including addiction treatment and mental health. Its GEO service reflects how behavioral health patients actually search. In this field, queries are often indirect and hesitant, and I see AI platforms increasingly becoming the first touchpoint for people who are not yet ready to search explicitly for treatment.

I also see that specialization as the agency’s primary limitation. Health systems, pharmaceutical companies, medical device manufacturers, and other organizations outside behavioral health may find its expertise difficult to translate to their needs. For behavioral health and addiction treatment providers, however, I believe its depth of sector knowledge makes it a strong niche option.

ASO Expertise Score: 3.7
Average Review Score: 4.4
Leadership Experience Score: 3.9
Notable Healthcare Clients: Elevation Behavioral Health, Pacific Mind Health, ABA Revolution
Year Established: 2017
Media References: ~35
Specialty: Digital marketing and GEO for behavioral health and addiction treatment providers
Contact: MGMT Digital website

What I found in online reviews: Clients appreciated MGMT Digital’s “thoughtful approach.” However, some felt that the agency was “too specialized to fit other healthcare markets.”

How I Would Choose a Healthcare ASO Agency

I would begin by matching each agency’s specialty and delivery model to the organization’s specific needs. First Page Sage offers the strongest overall combination of ASO expertise, reviews, leadership experience, and institutional depth in this ranking. Focus Digital appears more accessible for smaller providers, while Genevate stands out for pharmaceutical organizations facing strict regulatory constraints.

I would consider Driven Metrics when measurement and attribution are central priorities, Medico Digital for UK-based pharmaceutical or medical device marketing, Signal Hill Strategies for near-term lead generation, and MGMT Digital for behavioral health or addiction treatment. Before making a final decision, I would also examine the proposed scope, compliance process, reporting standards, team capacity, and evidence that the agency can influence both AI visibility and completed patient or buyer actions.

Source

Inspired by this post on First Page Sage Blog.

July 10, 2026

How Gemini Intelligence Will Reshape Search and Commerce

I see Google’s unveiling of Gemini Intelligence at the May 12 Android Show as a significant step toward an agent-powered future. Announced alongside a new laptop called the Googlebook, Gemini Intelligence is designed as an underlying layer that works across the Android operating system on laptops, phones, watches, and glasses.

The Googlebook makes that vision tangible to me. Built from the ground up around an AI agent, it can understand what is on the screen and act on it. I could point to a date in an email and have the agent schedule a meeting, or select furniture in an app and see how those pieces might look in my living room.

I believe this ability to complete tasks without requiring someone to open a webpage will fundamentally change how people search, discover information, and conduct commerce. Here is how I expect that shift to affect the search industry.

What the shift to an agentic operating system means

Until now, I have viewed search as a familiar sequence: someone has a question or intent, enters it into a search engine, receives a list of links, and chooses one. Earning a prominent position on that list was the prize, and much of the SEO industry was built around winning that click.

Gemini Intelligence starts from a very different assumption. Search intent still exists, but an AI agent can handle the steps between the request and the outcome. It can read pages, complete forms, and increasingly finish the entire task. Instead of visiting a website myself, I may have an agent visit and use it on my behalf.

When I look for an early example, Chrome Auto Browse stands out. Launched in January and built on Gemini 3, it can manage multistep tasks such as researching flights, filling out forms, scheduling appointments, and managing subscriptions. It then pauses for approval before making a purchase.

That efficiency gives me a clear reason to believe ecommerce will continue moving toward agentic AI.

A 2025 preprint supports this view. Researchers evaluated the declared-tools approach across online shopping, authentication, and content management. They found that giving an agent pre-structured interaction data reduced processing requirements by 67.6% and lowered costs by 34% to 63% compared with parsing a complete HTML document. Task success declined only slightly, from 98.8% with the traditional method to 97.9%.

The architecture behind Gemini Intelligence

To me, the architecture is as important as the interface. AI agents naturally favor websites they can interact with cleanly and efficiently, and Gemini Intelligence can only deliver on its promise if those agents can perform tasks reliably.

I see two protocols as central to making that possible. WebMCP turns a website’s actions into callable tools, while the Universal Commerce Protocol (UCP) allows an agent to complete a sale. Together, they enable an agent to finish a task without requiring a person to load and navigate the underlying webpage.

As AI reshapes search, every glowing path to discovery carries commercial value—turning SEO investment into a conversation about pipeline, risk, and customer acquisition costs.

WebMCP

I think of WebMCP as a labeled menu for AI agents. The API allows a website to declare functions as structured tools an agent can call, including searching inventory, beginning checkout, or submitting a support request.

Google co-developed WebMCP with Microsoft. An origin trial is live in Chrome 149, Firefox has committed to the third quarter of 2026, and Safari is expected to follow in the fourth quarter.

Universal Commerce Protocol (UCP)

I see UCP as the transactional counterpart to WebMCP. It gives AI agents a shared language for discovering products, building a cart, completing checkout, and managing orders without requiring someone to visit the merchant’s website.

Google also offers a consumer-facing layer called Universal Cart. It can collect items as I move across Search, Gemini, YouTube, and Gmail, creating a more connected shopping experience across Google’s products.

The range of companies behind UCP shows me how seriously the industry is taking this shift. Google, Shopify, Walmart, Target, Etsy, Wayfair, PayPal, and Stripe co-developed the protocol, which launched in January.

How I would prepare for agentic AI

My main takeaway is that websites are rapidly evolving from destinations into backends—from places people actively visit into systems agents quietly use. As the operating system becomes a search and action layer, I no longer think ranking is the only question that matters. I also need to ask whether an agent can actually use the site.

To prepare, I would begin by auditing the site’s most valuable actions, whether that means submitting a lead form, completing a booking flow, or reaching checkout. I would determine whether an agent could complete each action reliably and check the site’s Lighthouse Agentic Browsing score much as I would review Core Web Vitals. The goal is to understand whether an agent can use the site, not merely read it.

If I ran an ecommerce business, I would confirm whether the checkout process is accessible through UCP or ACP. I would also continue investing in retrieval and visibility because an agent still needs to find and trust the business before it can act on anyone’s behalf.

Dig deeper: Are we ready for the agentic web?

Inspired by this post on Search Engine Land.

July 10, 2026
OpenAI to Retire ChatGPT Atlas Aug. 9: What I’m Watching

I’m watching OpenAI discontinue ChatGPT Atlas, its standalone desktop browser, and move its browser-based AI features into the new ChatGPT desktop app. That app brings together ChatGPT Work, OpenAI’s work-focused agent, and ChatGPT Codex.

The end of Atlas. I’m taking note of an Aug. 9 retirement date after OpenAI’s James Sun confirmed the plan on X.

I’m also noting Sun’s exact wording: “The current targeted date for deprecation is 8/9, and we’ll share more information in the upcoming days both in-app and via email.”

One desktop app. I see the new ChatGPT desktop app becoming OpenAI’s primary desktop product, complete with built-in browser capabilities. Instead of maintaining a separate AI browser, OpenAI is combining browsing, work-agent features, and Codex in one place.

Chrome users can keep Chrome. If I prefer using Chrome, I can access ChatGPT and Codex through OpenAI’s Chrome extension without switching to a dedicated OpenAI browser.

As AI reshapes search, every glowing path to discovery carries commercial value—turning SEO investment into a conversation about pipeline, risk, and customer acquisition costs.

Why I care. I see this as an important shift because OpenAI is moving AI browsing into the main ChatGPT experience, where more people can ask questions, research brands, and complete tasks. In my view, that gives ChatGPT another opportunity to influence discovery beyond traditional search results.

My quick recap. ChatGPT Atlas will be retired as a standalone browser less than a year after its launch.

I first saw ChatGPT Atlas launch on Mac in October. OpenAI later released a dedicated Codex app and added an in-app browser in April. Now, I’m watching those capabilities move into the new unified ChatGPT desktop app.

Inspired by this post on Search Engine Land.

July 10, 2026
How I Help Retailers Win the AI Shelf This Christmas

I see Christmas shopping moving beyond the search bar. More shoppers are now turning to AI answer engines to research products, compare gift options, and decide what to buy long before they land on a retailer’s website.

For retailers, I believe this shift requires a serious reframe. Answer engines can shortlist products, shape preferences, and guide purchase decisions earlier in the journey than traditional search ever did. That compresses the shopping funnel and makes search alone too limited as a customer acquisition strategy.

Instead, I need to think about how retailers can earn recommendations across the entire AI-assisted shopping journey. That means understanding how people use answer engines for Christmas gifting, how brands earn mentions and citations in relevant AI responses, and how ecommerce teams can improve visibility across AI search.

In this report, I give retailers a clearer path to that advantage. I draw on real shopper behavior from Christmas 2025, analyzed through Profound’s AI visibility lens, to show how people are using AI to shop for the holidays.

I also focus on practical takeaways retailers can use now, before the 2026 season ramps up. The goal is simple: optimize ecommerce products early, show up in the AI answers that matter, and win the AI shelf this Christmas.

Inspired by this post on Try Profound Blog.

July 9, 2026
How AI Search Is Reshaping Travel Brand Visibility

I’m seeing travel planning move away from the traditional search bar and into AI answer engines like ChatGPT. For most of the past two decades, a traveler would type a destination-focused keyword into Google, open a dozen tabs, and stitch together a trip one page at a time.

Now, that same traveler can ask a question, keep the conversation going, and let the answer engine synthesize recommendations, compare options, or even help book the trip. The journey from curiosity to decision is becoming faster, more conversational, and far less dependent on traditional search results.

I believe this shift is rewriting how travelers discover brands. Visibility is no longer only about winning top-ranked blue links in Google. Increasingly, it depends on earning mentions, citations, and trust inside AI-generated answers.

For travel brands, that changes the competitive landscape. The companies that show up in AI search are the ones most likely to shape the itinerary, influence the booking decision, and ultimately win the trip.

Inspired by this post on Try Profound Blog.

July 9, 2026
How I Turn Search Console Data Into SEO Wins With AI

I rely on Google Search Console because it is excellent at collecting search data. The challenge is that it still does not make interpretation easy.

When I open almost any property, I usually find thousands of queries, landing pages, impressions, clicks, rankings, and click-through rates. That volume is useful, but it can quickly become overwhelming when I am trying to answer one simple question: what should I do next?

For years, my workflow was familiar: export the data into Excel or Google Sheets, build pivot tables, apply filters, and start digging for patterns. That approach works, but it is slow. More often than not, I am searching for insights without knowing exactly what I am looking for.

That is where AI makes the workflow more useful. I use it to speed up the hardest part of Search Console analysis: finding meaningful patterns hidden across thousands of rows of search data.

I think of Google Search Console as my source of truth and AI, whether ChatGPT or Claude, as the analyst sitting beside me. GSC shows me what happened. AI helps me explore why it happened, uncover opportunities I might miss, and organize messy data into decisions I can act on.

A quick note on regex

Most of the examples I use start in the same place inside Google Search Console: Performance → Queries → + Add Filter → Query → Custom (regex).

From there, I enter a regular expression to filter query data before exporting it for analysis.

The useful part is that I no longer need to memorize regex syntax. I can ask ChatGPT to write it for me. For example, I might prompt: Create a regex for Google Search Console that matches queries beginning with question words.

ChatGPT may return something like (?i)^(who|what|why|how|can|does|will|should)b.

If I need something more specific, I simply describe the pattern I want. I might ask for a regex that matches queries containing five or more words, identifies comparison searches, or finds branded queries that include product names.

The better I describe the pattern, the better the regex usually becomes.

Here are seven practical ways I combine Google Search Console with AI so I can spend less time sifting through data and more time making decisions.

1. I stop looking only at queries and start looking at intent

Most Search Console analysis still happens at the keyword level. The problem is that people do not really search by keyword. They search with intent.

Instead of reviewing thousands of individual queries one by one, I use regex to isolate investigation-focused searches before exporting the data.

One useful regex is (?i)^(best|top|vs|review|reviews|compare|comparison).

After exporting the filtered query data, I ask Claude or ChatGPT to classify intent. My prompt is usually something like: Categorize these queries into informational, navigational, investigation, transactional, and local intent. Return a CSV with classifications and confidence scores.

This helps me spot patterns that are difficult to see keyword by keyword. Informational traffic may be growing while commercial investigation queries are declining. Transactional queries may rank well but earn weak click-through rates. Comparison searches may be driving impressions without having dedicated content to support them.

When I segment by intent, the next steps become much clearer.

2. I discover questions my audience is already asking

Question-based keyword research is not new, but AI helps me identify themes across hundreds of question-oriented searches much faster.

I start with a regex like (?i)^(who|what|where|when|why|how|can|does|should|will)b.

Then I export the results and ask Claude or ChatGPT: Group these questions into common themes and identify unanswered topics.

A Google Search Console query filter highlights how regex can narrow SEO performance data, helping marketers turn thousands of search terms into focused insights.

Instead of manually reviewing hundreds of questions, I can quickly see broader patterns around pricing concerns, product comparisons, implementation challenges, and industry-specific use cases.

This becomes more than a content exercise. I can use these themes to improve FAQs, support resources, sales enablement materials, and AI Overview optimization.

The best opportunities are often not hidden in one query. They are hidden in clusters of related questions.

3. I find queries likely to trigger AI Overviews

Google does not give me a filter for queries likely to trigger AI Overviews, but I can build a useful approximation.

I start by isolating common informational and comparison patterns with a regex like (?i)^(what is|how to|best|vs|difference between|guide to).

Then I export the matching queries and ask Claude or ChatGPT: Review these queries and group them by the content format needed to answer them effectively.

The themes often fall into definitions, tutorials, comparisons, or expert recommendations.

This helps me see where my content may need to shift from simply ranking for keywords to becoming the best available answer. Increasingly, those are not always the same thing.

4. I track emerging trends earlier

Traditional keyword research can be reactive. By the time a trend is obvious in keyword tools, competitors may already be building content around it.

Google Search Console can help me identify shifts earlier, as long as I know how to look for them.

Instead of searching for individual keywords, I use ChatGPT to build regex around broader concepts. For example, I might prompt: Create a Google Search Console regex to identify searches related to AI agents, copilots, assistants, automation, and autonomous workflows.

The output may look like (?i)(ai agent|agentic|copilot|assistant|automation).

This same approach works for new technologies, product categories, competitors, industry buzzwords, and changing customer concerns.

Once I filter and export the data, I let AI look for emerging themes. A prompt I like is: Review these queries and identify emerging themes, new terminology, and shifts in search behavior. Highlight which topics appear to be gaining traction, recommend whether they deserve a new content asset or an update to an existing page, and identify any patterns that could influence our content strategy.

Instead of only confirming that a trend exists, AI helps me decide whether the trend is meaningful enough to act on and what the next move should be.

5. I surface conversion intent inside informational traffic

One of the most overlooked opportunities in Search Console is finding bottom-of-funnel signals inside queries that appear informational at first glance.

I might ask ChatGPT: Create a regex for searches that indicate evaluation, comparison, pricing, alternatives, migration, implementation, or vendor selection intent.

An example output is (?i)(cost|pricing|price|vs|alternative|compare|implementation|migration).

I apply that regex to the query report, export the filtered data, and then ask Claude or ChatGPT to analyze it.

My prompt usually looks like this: Review these Google Search Console queries and identify recurring buying signals. Group them into themes such as pricing, comparisons, implementation, and vendor evaluation. Recommend which existing pages should better address this intent, and identify opportunities to improve content through stronger CTAs, internal links, comparison tables, FAQs, or supporting resources.

A visual metaphor for AI turning messy Google Search Console queries into clear SEO decisions, separating qualified intent from irrelevant traffic signals.

I often find that pages created for top-of-funnel education are already attracting visitors who are evaluating solutions. In that case, the best opportunity may not be creating a new page. It may be improving the page that already earns the visit, so users can take the next step without breaking the informational experience.

Sometimes the biggest content opportunity is recognizing the conversion intent already reaching the pages I have.

6. I find audience-specific opportunities

One of my favorite ways to uncover new content opportunities is filtering queries by industry, audience, or customer segment. It quickly shows me whether my content is resonating with the audiences I intended to reach or revealing opportunities I had not considered.

I start by asking ChatGPT to create a regex based on the audience segments that matter most to the business.

For example, I might prompt: Create a Google Search Console regex that identifies queries related to healthcare, manufacturing, retail, education, financial services, government, and nonprofit organizations.

An example output is (?i)(healthcare|hospital|medical|manufacturing|factory|retail|education|school|financial|bank|government|public sector|nonprofit).

After applying the filter and exporting the results, I ask Claude or ChatGPT: Analyze these queries and group them by audience segment. Identify which industries show the strongest search demand, what recurring questions or pain points each audience has, and recommend opportunities for new content, landing pages, case studies, or internal linking that would better serve those audiences.

The differences can be valuable. Healthcare searches may consistently focus on compliance, while manufacturing queries may revolve around implementation. Retail searches may reveal entirely different use cases than financial services searches.

7. I uncover striking-distance opportunities at scale

Every SEO knows the classic advice: look at keywords ranking in positions 5-15 to identify opportunities within striking distance.

The challenge is doing that at scale. A report with hundreds of queries where a site is close to stronger rankings can become overwhelming fast.

I take the regex patterns above a step further. I apply the filters that match my goals, then narrow the report to positions 5-15 before exporting the queries.

Then I ask my AI analyst: Identify recurring themes across these queries and recommend page-level optimizations rather than keyword-level optimizations.

Instead of getting tiny recommendations for individual keywords, I often uncover larger opportunities. A page may be missing subtopics, comparison details, stronger internal links, or use cases that would make it more complete.

The result is usually fewer optimizations, but more meaningful ones.

Turning Search Console data into decisions

As an SEO, I do not have a data shortage. I have a prioritization problem.

Google Search Console remains one of the richest sources of insight into how people discover a business. The difficult part is turning thousands of rows into something actionable.

That is where AI fits into my workflow. It helps me uncover patterns, organize information, and surface opportunities I might otherwise miss. It is not a replacement for SEO strategy, experience, or critical thinking.

The real advantage is not writing better regex or exporting cleaner spreadsheets. It is spending less time searching for insights and more time acting on them.

Because data does not improve SEO. Better decisions do.

Inspired by this post on Search Engine Land.

July 9, 2026
How I Win the AI Decision Layer in Agentic Commerce

I see the next major battleground for brands being shaped by AI. Every day, AI engines and autonomous agents decide which brands to recommend, compare, cite, and transact with on behalf of consumers. To compete, I have to make my brand the trusted choice AI selects.

This shift is already underway. Adobe data shows that AI-referred traffic to U.S. retail websites grew 4,700% year over year through mid-2025. Salesforce reports that AI and autonomous agents influenced one in five online orders globally during Cyber Week, driving an estimated $67 billion in sales.

As AI becomes the interface between consumers and brands across discovery, evaluation, and purchase, I need to think beyond traditional rankings. A new competitive layer is emerging: the AI decision layer. This is where AI systems evaluate trust, relevance, authority, and transaction readiness before deciding which brands make the shortlist.

If I fail to influence this layer, my brand may be excluded before a customer ever sees it. That makes AI visibility, credibility, and actionability core parts of modern search strategy.

How I take a brand from found to actioned

Agentic commerce readiness follows a clear sequence. I start by making sure AI engines can find my brand, then I move through the remaining stages until AI agents can understand, trust, recommend, and transact with it.

Step 1: I get found by enabling AI discovery and access

Machine accessibility is the foundation of AI visibility. If I want AI systems to discover and access my brand, I have to prioritize technical hygiene and token efficiency.

I start by allowing the right crawlers on my website. Google, OpenAI, Anthropic, and Bing need to reach my content without unintended restrictions.

Then I get the basics right. I set up XML sitemaps and robots.txt, fix crawl errors, add canonical tags, and maintain strong Core Web Vitals. I also make sure my website content is rendered server-side so agents can reliably navigate and reason over my pages.

I also pay close attention to token efficiency. Bloated HTML wastes valuable tokens that AI systems could otherwise use to understand my content, products, and brand.

To make my site more AI-ready, I publish assets that help large language model crawlers process my content more efficiently. An llms.txt file can give LLM crawlers a concise map of my website, while Markdown versions of key content can reduce token consumption and improve machine understanding.

Dig deeper: The enterprise blueprint for winning visibility in AI search

Between consumers and brands, AI agents now act as the decision layer, handling discovery, evaluation, trust signals, and transactions before products reach the shortlist.

Step 2: I become understood by building semantic clarity

To be understood by AI engines, I need to build entity authority. This helps AI interpret who I am, what I offer, and why my brand matters.

Structured data turns my web pages into machine-readable knowledge that AI systems can understand, trust, and use. I strengthen my entity graph with comprehensive schema, trusted citations, and linked references.

I also deliver clean, server-rendered HTML that AI can access without friction. Semantic HTML, structured @graph IDs, and consistent naming help AI engines connect the right context to my brand.

Step 3: I get retrieved by structuring content for AI extraction

Traditional search ranks pages, but AI search retrieves and cites passages. That means I win on relevance, clarity, authority, and freshness rather than length alone. Original expertise, proprietary data, and real-world experience give my content a stronger chance of being selected.

To structure my content for retrieval, I use a clear heading hierarchy with H1, H2, and H3 tags. Under each heading, I create descriptive, self-contained sections that can stand on their own.

I build interconnected topic clusters instead of isolated pages because AI needs enough context to assemble complete answers.

I also front-load every section. I put the core answer and the most important metrics in the opening sentence before a model hits its token limit.

Dig deeper: Chunk, cite, clarify, build: A content framework for AI search

Step 4: I build trust with authority and grounding signals

Just because AI engines retrieve my content does not mean they will recommend my brand. Retrieval is only one step. Trust is what moves a brand closer to selection.

AI systems prioritize sources they can trust, so authority and credibility become decisive. Google’s experience, expertise, authoritativeness, and trustworthiness principles, known as E-E-A-T, remain some of the strongest signals influencing whether a brand is cited, referenced, or selected.

A visual roadmap for becoming the brand AI selects: first be found and understood, then retrieved, trusted, chosen and finally actioned by autonomous assistants.

Trust extends far beyond my website. AI evaluates review sentiment, location accuracy, pricing consistency, product availability, and entity alignment across the web. When those signals conflict, AI confidence decreases.

Credibility is now computational. Grounding, the process of validating responses against trusted evidence, is the bridge between visibility and recommendation.

To earn computational trust, I create original, expert-driven content that shows real experience and unique value. Then I align every external signal so reviews, listings, maps, and directories all tell one consistent story about my brand.

Dig deeper: Integrating SEO into omnichannel marketing for seamless engagement

Step 5: I get chosen by earning machine and human preference

AI agents parse attributes, verify claims, and score confidence in milliseconds. If I cannot make my value clear to AI, my brand becomes invisible at the decision point.

But emotional preference still matters. Consumers may delegate routine purchases, yet they hold tightly to choices tied to identity. The strongest brands optimize for both machine readability and human resonance.

To earn AI recommendations, I measure AI visibility, citation, and recommendation rates through query fan-out testing. I keep brand, product, and location data consistent across every channel. I also work to earn trusted mentions and references that strengthen AI confidence in my brand.

Dig deeper: How to boost your marketing revenue with personalization, connectivity, and data

Step 6: I enable agentic transactions

Recommendation is no longer the finish line for AI search. Discovery, selection, and checkout can now happen inside an AI assistant without the customer ever visiting my site.

An agentic website is designed for AI agents to discover information, retrieve answers, and perform actions on behalf of users. NLWeb helps make website content conversational and machine-readable, improving how AI systems find and understand the site.

A bold Google logo sits atop layered, colorful digital documents, evoking the fast-moving world of search marketing, ad formats, campaign assets, and platform updates.

Web Model Context Protocol, or MCP, extends this capability by giving AI agents a standardized way to interact with website functions. That can include retrieving data, initiating workflows, and submitting forms.

Agentic commerce moves the full transaction inside the assistant. Google’s Universal Commerce Protocol, or UCP, enables chat-based bookings. OpenAI and Stripe’s Agentic Commerce Protocol, or ACP, pushes inventory so AI systems can surface it more easily. Agent Payments Protocol, or AP2, then lets the agent pay.

Underneath these capabilities is MCP, which enables an LLM to read products, content, and live data. This changes my website from a destination into a source of truth. It supplies the inventory, pricing, and signals that drive every agent journey.

Dig deeper: How to select a CMS that powers SEO, personalization, and growth

How I measure performance in the AI decision layer

I still track traditional search metrics like rankings, sessions, and clicks. They remain useful, but they are no longer enough to measure success in AI search and agentic commerce.

For visibility, I track AI presence rate, AI share of voice, citation frequency, and agent recommendation rate.

For commerce, I track AI-influenced revenue, agent conversion rate, autonomous transaction volume, and agentic wallet share.

I also expect traffic patterns to change. Direct visits may decline as agents handle discovery, but AI-influenced transactions through machine-readable layers like WebMCP and schema endpoints can offset that loss and create new revenue paths.

With these changes in place, my website can become the trusted source AI systems rely on for both information and action.

From SEO to decision architecture

SEO remains the foundation for winning search, but a deeper shift became concrete at Google I/O 2026. AI agents now parse raw HTML, distill the browser’s native accessibility tree, and capture visual screenshots through vision models.

Together, these three paths determine whether a site is truly actionable for AI. My page can be technically flawless and still fail if its structure, semantics, or user experience breaks the chain. If I miss any stage, trust and transaction readiness suffer.

When I get these pieces right, my brand becomes discoverable, understandable, trusted, and transactable when AI agents make decisions. The brands that build these capabilities today will be the brands AI surfaces, trusts, and recommends tomorrow.

Inspired by this post on Search Engine Land.

July 9, 2026
Hidden ChatGPT Search Pipelines Can Shake Up Citations

I see these two new analyses as an important reminder that ChatGPT citations are not as fixed or transparent as they may look. The sources shown in an answer can change when ChatGPT routes search traffic through different hidden retrieval pipelines.

Research from Chris Green and Suganthan Mohanadasan adds a new wrinkle to AI visibility tracking: the final answer does not reveal how ChatGPT selected its sources. Both researchers found internal source-selection labels, including Labrador, Bright, Oxylabs, and SERP, but those labels sit behind the answer rather than inside the citation cards users see.

Green tested 1,000 prompts up to 10 times each and captured 9,946 completed search runs. In most cases, prompts stayed on one retrieval source. Labrador accounted for 88.1% of primary search sources in his dataset, followed by Bright at 9.9%, Oxylabs at 1.7%, and SERP at 0.3%.

What stands out to me is that 11.6% of prompts changed their primary search source across repeated runs. When that happened, URL overlap dropped from 0.273 to 0.149, and domain overlap fell from 0.265 to 0.155. Green calculated that as roughly 45% lower URL overlap and 42% lower domain overlap.

Mohanadasan looked at the issue from another angle. He inspected two days of raw ChatGPT network traffic from one logged-in Pro account and logged about 1,240 source records across a few dozen searches. He found a result_source field attached to web results, with four observed values: SERP, Labrador, Bright, and Oxylabs.

He described Labrador as including established publishers and reference sites, Bright as tied to Bright Data, Oxylabs as tied to Oxylabs, and SERP as an open-web baseline that appeared mostly in news-style results. While Green’s repeated-prompt test found Labrador dominating his dataset, Mohanadasan saw Bright play a larger role in his sample, especially for commercial, shopping, finance, weather, and local queries.

I also think the skipped-search finding matters. Mohanadasan found that ChatGPT classified some queries before searching, using a turn_use_case field. Some prompts were filed as text and skipped web search entirely, even when they sounded current. In those cases, no page could be fetched, cited, or used as evidence.

Rows of illuminated data cabinets and paper files stretch into the distance, capturing the pressure on marketers to turn fragmented customer data into a smarter performance engine.

More complex “thinking” queries behaved differently. Mohanadasan found that ChatGPT could branch into many searches, including site: probes, pricing checks, and searches for unnamed competitors. That changes which pages can enter the answer process because ChatGPT may search rewritten queries, direct site probes, or follow-up checks instead of the exact phrase a user typed.

Another useful distinction is that fetched does not always mean cited. Mohanadasan separated three outcomes: fetched, cited, and mentioned. A page can be pulled into ChatGPT’s context without being shown to users, cited as support for a specific sentence, or skipped as a source even when a brand is mentioned in the answer.

In his small commercial-query sample, Reddit and YouTube were both fetched often, but Reddit was cited and YouTube was not. He attributed that gap to text availability: Reddit threads expose text, while YouTube search results often provide metadata rather than full video transcripts. Vendor pages were cited for their own facts, such as prices and specs, while third-party pages were more likely to support broader recommendation claims.

The practical takeaway for me is that there is no single ChatGPT visibility result to measure. A page may never be considered if ChatGPT skips search, uses another retrieval source, or finds a clearer third-party page to support the claim.

Both analyses also point back to readability. ChatGPT’s source selection depends partly on what it can retrieve and understand. Mohanadasan found cases where ChatGPT appeared to prefer official pricing pages, then fell back to third-party sources when prices were hidden behind JavaScript or otherwise hard to parse.

Green’s results showed that source routing can change which URLs and domains enter the answer set. That makes plain HTML, crawlable facts, clear pricing and specs, strong third-party coverage, and text-heavy pages more important when source selection depends on retrieval and readability.

Inspired by this post on Search Engine Land.

July 8, 2026
Why My Original Data Gets Cited Only as Benchmarks

In Part 1, I looked at the third-party citation signals that matter so much for AI visibility. In Part 2, I made the case for publishing original data, because it is the strongest single predictor of page originality, and the barrier to earning visibility and authority through this approach is still surprisingly low.

Now I have more evidence for why proprietary data should be part of content creation.

Publishing a number matters, but the number itself is not always what gets cited. I looked at Gauge’s citation data to understand what AI systems actually reward when brands publish first-party data. The answer is narrower, sharper, and more useful than simply saying, “original data wins.” Original data does win, but only when it is packaged in the right way.

The format AI rewards most is the benchmark that answers a clear commercial question: which option is best?

First-party research is scarce and punches above its weight

I worked from Gauge’s cited-URL set: 301 live pages cited by AI systems across 316 unique prompts and 7 verticals. Together, those pages carried 1,075 citations.

After auditing the URLs, I found that only 8 of the 301 pages qualified as primary research. To count, the page had to include the original source of the data and its methodology, rather than simply writing about someone else’s numbers.

That means primary research made up just 2.7% of the cited set. But those same 8 pages earned 90 of the 1,075 citations, or 8.4% of the total citation volume. In other words, first-party research appeared rarely, but when it appeared, it over-indexed by roughly 3x on citation share.

The cleaner way I see this is citation density.

Primary research averaged 11.3 citations per page. Everything else averaged 3.4 citations per page. A primary-research page was 3.3x as citation-dense as a non-primary page.

Primary research is rare, but this Gauge analysis shows it punches above its weight: cited pages with original research averaged 3.3x more citations than everything else.

That is the compounding effect of primary research.

This mirrors the information gain finding I discussed in Part 2, but from the AI citation side rather than the classic 10 blue links side.

There, original data correlated with page originality more strongly than any other trait. Here, original data correlates with citation density. Both findings point in the same direction: the number only you can produce is the lever.

Original research wins when the question has a benchmark

This is where the “original data wins” idea needs more precision.

The 90 primary-research citations were not distributed evenly across the 8 pages. They were not distributed evenly across topics either.

Of those 90 citations, 75 came from one cluster: cloud data warehouse benchmarks. Fivetran’s warehouse benchmark alone earned 44 citations, which was just under half of every primary-research citation in the set.

Once I strip out the benchmark cluster, first-party research barely registers in the citation set. The win is not simply, “I published original data.”

The real win is, “I published a benchmark that answers a buying comparison,” and almost nobody builds those well. By benchmark, I mean a page that measures a set of named things against each other on a specific yardstick and publishes the results as numbers.

A striking citation split: cloud data warehouse benchmarks dominated AI-cited primary research, with Fivetran’s benchmark alone pulling 44 citations from the 90-citation set.

Original research is most powerful when it directly answers commercial comparison queries.

This is also what Google is pushing toward with non-commodity content: new, helpful information that is hard to get elsewhere.

The primary-research citations clustered where prompts asked AI to compare options on measurable specs such as speed, cost, latency, yield, or performance.

That explains the warehouse benchmark spike. The “HR Tech / Compensation” label was noisy, but the citations inside that bucket mostly came from cloud data warehouse benchmark prompts. Fivetran, Estuary, and ClickHouse had numbers AI could use.

Crypto / Solana showed the same pattern at a smaller scale. Marinade and Helius earned citations because staking and MEV questions need firsthand ecosystem data, not generic explainers.

The pattern disappeared in topics without a clear benchmark. B2B SaaS / CRM, Education / TEFL, and Product Analytics returned listicles, product pages, explainers, and case studies. After cleaning the data, I found no cited primary-research page in those topics.

A closer look at the content that held 44 citations

Fivetran’s warehouse benchmark took 44 citations from this dataset on its own. Fivetran’s 2 benchmark pages together took 58 of the 90 primary-research citations. So I wanted to understand why.

The page was published in 2022, but when I examine it, it is easy to see why LLMs still prefer it.

Primary-source visibility is highly concentrated: benchmark-driven topics like HR tech and crypto attract far more AI citations than explainers or listicles.

It answers a measurable comparison head-on. The page names BigQuery, Redshift, Snowflake, and Databricks, then ranks them on speed and cost. It is entity-rich and willing to name the major players directly.

It runs on real first-party data. Fivetran tested against actual customer usage rather than relying on synthetic assumptions, and the page calls that choice out clearly.

It shows the method step by step. The page walks through what data was queried, which queries were used, and how each warehouse was configured and tuned. A reader, or a model, can see how the numbers were produced.

The structure is easy to lift. Descriptive headings such as “Results,” “How much did performance improve?,” and “Why are our results different from previous benchmarks?” help AI map a question to the exact passage that answers it.

It links to raw data and sources. The page footnotes references, including the C-Store paper, and points to the underlying data. That makes the claims verifiable. Few brands put that much work into a data-backed content piece, and even fewer share the full dataset for transparency.

It shows its limits. Dated correction notes from December 2022, named qualitative limitations, and an honest “performance floor” caveat make the claims more credible, not less. The corrections also show care.

The URL never moved. A page from 2022 is still earning citations in 2026 because it stayed live at one canonical address.

The data behind a page like this is easier to pull and analyze than it has ever been. The hard part is everything around the data: the clean method, linked sources, corrections, navigable structure, and willingness to say what the numbers do not prove. That is the craft, and that is the moat.

Fivetran's 2022 benchmark page shows why clear, comparison-led research can become a lasting citation source for AI and search visibility.

This kind of first-party data content is not a thin press release with a few loosely pulled numbers. It requires real work, and it can hold authority for years. My takeaway is simple: AI does not reward “original data” by default. It rewards first-party research when the page gives a clear answer to a measurable comparison and signals depth, expertise, and trust.

The opportunity is to publish a retrievable dataset for a buyer question where AI does not yet have a clean benchmark source. That connects directly to the unanswered-questions finding from Part 2. The opening exists, but in many verticals, nobody has walked through it with a real dataset.

Original data needs a citation-ready package

Original data gives a page something AI cannot get from another explainer. But AI still has to retrieve it, parse it, and map it to the user’s question.

That is where many brands lose the citation. They publish proprietary numbers, but bury them in narrative, gate them behind forms, move the URL, or skip the methodology. The data exists, but the citation never happens.

The pages that won in this dataset had both ingredients: original numbers and a clean citation shape. They had stable URLs, clear methods, named comparisons, and results that answered buyer questions directly.

Who wins: brands with proprietary product, usage, or pricing data that package it into a comparison a buyer can act on, especially one that can inform LLM-generated recommendations.

Who loses: brands that publish original numbers inside dense narratives, on slow or unstable pages, with no clear comparison frame for AI to retrieve and reuse.

When I think about a citation-ready research page, I look for four parts.

Rows of illuminated data cabinets and paper files stretch into the distance, capturing the pressure on marketers to turn fragmented customer data into a smarter performance engine.

Lead with the comparison result. The headline finding, such as “X is fastest” or “Y is cheapest at scale,” should appear in the first 30% of the page. Lead with the result, then explain the method and nuance.

Box the methodology. Show the sample, time window, what was measured, and how the measurement happened. Attribution confidence is part of what makes a number citable, so the method needs to be clear on the page.

Frame the comparison explicitly. AI reaches for benchmarks when prompts ask “which is best.” A table comparing named options on named specs is the format most likely to be lifted.

Keep the URL stable. Use one canonical page and keep it live. Do not migrate it or rename it every redesign. The citation earned this quarter only compounds if the page is still there next quarter. In this dataset, 64 of 365 cited URLs were dead, redirected, or otherwise broken, taking 203 citations down with them.

This is the work behind a citable benchmark, and it is more involved than it looks.

HockeyStack documented its own version in a playbook on launching research reports. The company published 18 original reports built entirely on anonymized first-party customer data, the kind of data no competitor could replicate.

Its process includes the same steps the Fivetran page demonstrates: list the data points needed, have a teammate pull them with SQL, define and document the method so the numbers can withstand scrutiny, and structure the report around a real ICP question. HockeyStack calls methodology non-negotiable because without it, someone will always dispute the data.

With AI analysis, pulling the data is often the easier part now. Building the content into something citable, trustworthy, and durable enough to keep earning visibility for commercial queries years later is where the harder work sits.

What sites are already trusted for your topic? When a benchmark you did not publish is earning the citations in your category, the Citation Source Mapper can map that trusted set into a ranked, pitchable target list. It is available in the premium library.

This post first appeared on the author’s website and is republished here with permission.

Inspired by this post on Search Engine Land.

July 8, 2026
5 Critical Questions I Ask Before Buying Any AI Tool

AI now shows up in nearly every corner of marketing, and for every useful initiative I see, it feels like 10 vendors appear with a tool that claims to solve it.

When this wave first started, I took more vendor calls and answered more outreach than I do now. Over time, I noticed I was asking the same core questions again and again to decide whether an AI tool was actually worth deploying.

If I feel overwhelmed by AI vendor pitches, these are the five questions I use to separate useful solutions from noise. They help me understand what the tool does, whether it solves a real business problem, and whether the vendor is the kind of partner I would trust with my budget, data, and team’s time.

1. What problem does your tool solve?

I start here because I want to understand the purpose of the tool and, more importantly, whether the value it creates connects to real business outcomes.

If a vendor cannot clearly explain the challenges or use cases the tool addresses, I assume it was not purpose-built for a real problem my team faces. That applies whether I am evaluating it from an in-house perspective or on behalf of an agency. I am cautious when vendors lead with feature-heavy language but cannot explain the business benefits those features are supposed to deliver.

If a vendor can identify at least one existing team problem and explain how the tool improves business outcomes, I keep the conversation going. My next question is usually for a case study that shows how the tool was used and what results it delivered for an organization similar to mine in size, market, or vertical.

I look for benefits such as increasing output or identifying tracking gaps that speed up troubleshooting. I do not rush to buy a tool simply because it promises to save time, even if that promise is true. I need to know how I will use that extra time before I can decide whether the savings are meaningful.

2. What expertise do you have in the space where this tool solves a problem?

This answer tells me whether the vendor built the tool for advertisers or merely at advertisers.

Technical skill matters, but so does understanding how a media buyer actually spends the day. If the vendor does not have direct experience in media buying, I want to hear how the team researched the market and how those insights shaped the product.

A shallow understanding of the problem is a red flag for me. I do not expect every sales rep to have deep domain expertise, but someone on the team should. If I am seriously considering the tool, I want access to that person early in the process.

When a vendor has a credible story about identifying a problem I recognize firsthand and building a solution around it, I find that compelling. A founding mission tied to my actual challenges gives me more confidence that the tool can make a real difference in performance.

3. What case studies, real use cases, and results can you share?

In a fast-moving AI market, I treat case studies as essential. I want to know whether the vendor has a strong track record with customers like me or whether I would be one of the first teams testing the product in my space.

If I would be an early adopter, I weigh the tradeoffs carefully. I might gain an advantage by finding a growth accelerator before competitors do. I might also spend time working through bugs, giving detailed feedback, or discovering that the tool does not deliver what was promised.

If I cannot trust the tool, or if I will need to provide a lot of feedback just to make it useful, I have to decide whether the potential payoff is big enough to justify the time and money. In most cases, that bar should be high.

Rows of illuminated data cabinets and paper files stretch into the distance, capturing the pressure on marketers to turn fragmented customer data into a smarter performance engine.

If I am clearly going to be an early adopter and the vendor will not offer flexible contract terms that reduce my risk, I consider that a nonstarter. Established tools may be less flexible on pricing because they can already prove consistent value. Newer tools that take a hard line on price and contract terms are much less likely to become strong long-term partners.

For established vendors, I want specific and relevant case studies with real numbers from advertisers in a similar space, at a similar size, or with a similar use case.

For early-stage companies, the best answer is honesty. If a vendor says, “You’d be one of our first clients in this vertical. Here’s what we’ve seen elsewhere, and here’s what that partnership would look like,” I see that transparency as a positive sign.

4. Who owns my data, and how is it being used to train models?

I am still surprised by how quickly people share data with AI tools in the rush to find a competitive edge. Before I sign anything, I take data ownership and model training terms seriously.

I watch for any answer suggesting that my data could be used to train shared or third-party models without my explicit consent. I also treat vague answers, deflections, or terms of service that conflict with the salesperson’s verbal explanation as major warning signs.

I own my data, full stop.

The vendor should be able to clearly explain where my data is stored, how long it is retained, whether it is used for model training, and what happens to it if I stop using the tool. If model training is involved, I want that training limited to refining my own instance. Most importantly, I want those commitments in the contract, not just in a conversation. If the language is missing, I insist that it be added before I sign.

5. What does implementation actually look like, and what does success require from our team?

Before I commit budget, I need to understand the real cost of adopting the tool. That cost is not just the subscription price. It includes the time, internal lift, integration work, training, QA, and possible disruption to the existing martech stack.

If the tool requires resources my team does not have, or if I cannot realistically dedicate the time needed to use it well, I do not consider it a smart investment yet. A lot of wasted martech spend could be avoided by asking this question and taking the answer seriously.

I do not expect every tool to fit every organization, but I do expect implementation to be clear and the product to be intuitive enough for the team to adopt. If people cannot understand it, trust it, or fit it into their workflow, it will not create the value the vendor promised.

I do not let AI hype rush my decision

I know firsthand that many AI tools sound too good to be true, and often they are. I still want to stay curious and ambitious, but I balance that with caution.

I also remind myself that AI adoption is still early. If a tool feels too expensive, too difficult to onboard, or too rigid in its contract terms compared with its track record, I am willing to wait. A better option may appear in the next few months.

When I am unsure, I ask for a free trial. If integrating the tool will not create too much work for the team, a trial can be the best way to decide whether I have found a real competitive advantage or just another AI pitch dressed up as one.

Inspired by this post on Search Engine Land.

July 8, 2026