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.
I can now use Google Trends to quickly add previous time period data to a chart, making it easier to see how search interest compares with the same length of time immediately before it.
Google announced the update on LinkedIn, saying that I can now compare how a trend has changed against preceding periods directly inside Google Trends.
What it looks like. Google shared a GIF showing the feature in action, with a comparison line added directly to the Trends chart for faster context.
How it works. I can go to Google Trends, enter a search term or topic, and then use the new chips that appear above the timeline. Those chips surface percentage changes across different periods, including month-over-month, week-over-week, and specific year-over-year comparisons.
With one click, I can overlay the historical comparison line onto the graph and immediately see whether interest is rising, falling, or following a familiar seasonal pattern.
Why I care. Google Trends is already a helpful source for spotting topics, keywords, and audience interest patterns. When I am planning content, SEO priorities, or marketing campaigns, being able to compare current demand against a previous period gives me a clearer read on timing and momentum.
This update gives me more historical perspective inside Google Trends, which can make trend analysis faster and more useful for content strategy and marketing planning.
On June 11, 2026, I saw more than 1,000 marketing leaders come together in New York for Zero Click New York, Profound’s largest AI Marketing summit to date.
What stood out to me was the range of leaders and brands shaping the conversation. Speakers from Coca-Cola, LinkedIn, Delta Air Lines, U.S. Bank, and CVS Health shared how they are rethinking marketing strategy, team design, and measurement as AI changes the way audiences discover and trust information.
I also found the research sessions especially important. The summit explored Claude’s citation mechanics, ChatGPT’s emerging ads business, and the data behind the kinds of content AI systems are most likely to trust. Together, these conversations made Zero Click New York 2026 feel like a clear marker for where AI Marketing is heading next.
I believe your brand may already be getting misrepresented in AI search, and the hard part is that you might not even know it is happening.
When I looked at how AI search responses behave, one pattern stood out immediately: nearly half of AI responses include unsolicited comparisons, opinions, and recommendations that the user never directly asked for.
That creates a second dimension marketers cannot afford to ignore. It is not just whether AI systems mention your brand. It is how they frame your brand, what they compare it against, and which assumptions they repeat back to users.
To understand the scale of the problem, I analyzed 50,000 prompts across seven industries. I wanted to see when AI search stays factual, when it adds its own judgment, and how often brands are pulled into recommendations or comparisons without the user asking for them.
What I found shows why AI visibility is no longer only about being included in the answer. It is also about making sure the answer represents your brand accurately, fairly, and in the right context.
In this article, I break down what I found, why this “parrot problem” matters for marketers, and what you can do to protect your brand as AI search becomes a bigger part of the customer journey.
A year ago, I saw 82% of consumers say AI-powered search was more helpful than traditional search. By 2026, that number had fallen to 54%, a 28-point drop in sentiment in just 12 months.
That does not mean people are abandoning AI search. In fact, 70% of consumers say they are using AI tools for search more than they did last year. The tension is clear: adoption is rising, but trust is slipping.
That is the core issue I believe search marketers need to solve in 2026. It is no longer enough to appear in AI answers. I need my brand, and the brands I work with, to be visible, accurate, credible, and trusted when AI systems surface information.
To understand the shift, Fractl partnered with Search Engine Land to expand our 2025 research. We surveyed 1,008 U.S. consumers and 150 marketers to compare how consumer trust, marketer adoption, and brand strategy are changing in the AI search era. Disclosure: I am the co-founder of Fractl.
Here is what I believe the data means for 2026 search strategy.
Consumers are using AI more, but trusting it less
AI search adoption is no longer the main story. Seventy percent of consumers report increased use of AI tools for search over the past year, while only 3% say their use has decreased. The bigger question is whether people trust what those tools return.
One surprising finding is that baby boomers now find AI more helpful than Gen Z, 63% to 47%. That challenges the assumption that younger users automatically embrace AI while older users lag behind. What I see instead is a more complicated market where trust has to be earned across every generation.
In 2025, only 3% of consumers said AI was less helpful than traditional search. By 2026, that skeptic group had grown to 17%, nearly six times larger than the year before. Even among the 54% who still find AI helpful, enthusiasm is softer: 37% say it is only somewhat more helpful, while 17% say it is much more helpful.
I think hallucinations and low-quality AI content are changing how people evaluate the entire channel. Consumers may use AI because it is convenient, but convenience does not automatically create confidence.
AI content volume has become a brand trust risk
In 2025, 20% of consumers said heavy AI use would reduce their trust in a brand. In 2026, that number rose to 39%. For me, that makes AI content scale a reputational issue, not just an operational decision.
If I publish AI-assisted content at scale without disclosure, strong editorial standards, or obvious quality signals, I am asking my audience to trust a process they are increasingly skeptical of. That is a risk more brands need to take seriously.
Gen Z is especially strict. Fifty-four percent of Gen Z consumers say heavy AI use in a brand’s marketing would decrease their trust, compared with 32% of baby boomers and 33% of Gen X. Women are also more likely than men to penalize brands for heavy AI use, 44% vs. 34%.
That matters because Gen Z is often the audience most likely to engage deeply, share content, shape online conversations, and influence long-term organic visibility. If that audience matters to a brand, AI-generated filler is not a harmless shortcut.
Disclosure is now a consumer expectation
Across every major content format, more than 80% of consumers want AI-generated content labeled. Video leads at 91%, followed by images at 90%, audio at 87%, and written content at 84%. More than half of respondents strongly agree with labeling in every category.
I do not read that as a mild preference. I read it as a near-universal expectation. The brands that treat AI disclosure as optional are creating a gap between how they operate and what their audiences want.
Consumers still believe AI will shape the future of search. Sixty-four percent agree that AI will replace traditional search engines within five years, nearly unchanged from 66% in 2025. The channel is not going away. But being present in AI results and being trusted in AI results are now two different challenges.
Google still leads on trust, especially for buying decisions
When consumers are making purchase decisions, 39% turn to Google first. Reddit follows at 15%, AI tools at 14%, and review sites and friends or family each at 11%. The trust people have built with Google has not automatically transferred to AI tools.
Platform preference also changes by query type. Google dominates five of six major search categories. It is the first stop for local businesses, product research, travel planning, and health questions. YouTube overtakes Google for how-to content, while ChatGPT is now the second-most-used destination for health questions and ranks strongly for product research, travel planning, and how-to content.
That tells me there is no single AI search platform to optimize for. I need to map content strategy to actual user behavior: where people search, what they are trying to decide, and which platforms influence confidence at each stage.
Before making a purchase decision, the average consumer checks 2.4 platforms. Gen Z checks 2.5, millennials 2.4, Gen X 2.3, and baby boomers 2.2. This behavior is consistent enough that I now think of search optimization as a multi-platform visibility strategy, not a rankings-only discipline.
A brand that appears in Google results but nowhere else can lose to a brand that appears in Google, shows up in Reddit discussions, gets cited by ChatGPT, and has strong third-party review content. Visibility now has to travel with the buyer.
AI is changing marketing operations quickly
AI now touches 53% of marketing work on average, up from 38% in 2025. In practical terms, the equivalent of one full workday per week has shifted to AI-assisted workflows in just 12 months. Fifty-nine percent of marketers say AI is involved in at least half their work, while 27% say it is involved in three-quarters or more.
For SEO and content teams, this means competitors are moving faster. But speed alone is becoming commoditized. Accuracy, original insight, expert judgment, and brand credibility are much harder to copy.
Marketers are also feeling pressure to adopt AI. Fifty-five percent of marketing roles report a 7-out-of-10 level of pressure to use it. SEO and analytics teams feel that pressure most, while PR is not far behind. As AI makes generic content easier to produce, the advantage shifts toward what AI cannot automate well: judgment, relationships, trust, and reputation.
The quality tradeoff is real. Only 26% of marketers say AI made their work both faster and better. Nearly half say it made their work faster but more generic, and 7% report an outright quality decline.
That is where I see a major competitive opening. If other teams are scaling generic AI content while I invest in original data, expert quotes, third-party validation, and earned brand mentions, I am building assets that are more visible, credible, and retrievable across search engines, social platforms, and LLMs.
AI governance is still too weak
About three in four organizations conduct human editorial review before publishing AI-generated content. Sixty-two percent check for brand voice, 54% check facts, and 42% conduct legal or compliance review. Only 27% evaluate content for bias.
That means nearly half of AI-generated content may enter the market without fact-checking, legal review, or plagiarism checks. Too many teams are still relying on surface-level review: Does it sound right? Is the tone appropriate? Are there typos?
In a year when consumers are already prepared to distrust generic AI content, I see governance as one of the cheapest gaps to close and one of the most expensive to ignore.
The disclosure gap is just as serious. Heavy, generic AI use is now a brand-trust liability, yet only 20% of organizations always disclose AI use to their audiences. Compare that with the 84% average consumer demand for labeling written content, and the disconnect is obvious.
The takeaway is not to abandon AI. It is to stop treating governance as optional. Every AI workflow needs accuracy checks, transparency standards, bias review, and human accountability before content reaches an audience.
AI hallucinations are already a brand problem
A year ago, about 22% of marketers tracked LLM visibility. In 2026, that figure barely moved to 24%. At the same time, 27% of brands have already been misrepresented in AI-generated responses, and 14% say an AI inaccuracy has affected a customer relationship, sale, or PR situation.
More brands have been misrepresented by AI than have a formal monitoring process. That should concern every search and communications team.
If AI is summarizing my category, comparing my product, or explaining my brand incorrectly, that is not only an SEO issue. It is a reputation risk, a revenue risk, and a PR issue waiting to escalate.
When AI misrepresents a brand, I believe fixing the source matters more than arguing with the output. That can mean reaching out to publishers for updates, correcting owned profiles, improving brand pages, and publishing clear correction content tied to the entity.
Organic traffic is under pressure, not in freefall
Half of the marketers surveyed reported organic traffic declines since the launch of AI Overviews, and 61% blame AI. That is meaningful, but it is not the whole story.
The larger shift is not simply from Google to ChatGPT. It is from search as a destination to search as a behavior. People are asking, comparing, validating, and deciding across platforms, communities, assistants, and review environments.
The same marketers reporting organic losses are often finding visibility elsewhere. Fifty-seven percent report growth from social platforms such as TikTok, Reddit, and YouTube. Forty percent see growth from AI assistants such as ChatGPT, Gemini, and Perplexity. Thirty-one percent see growth in direct or branded traffic, while only 10% report no visibility growth anywhere.
That is why I think 2026 brand visibility depends on brand mentions and entity authority across the web, not just individual page rankings in Google.
Marketers are prioritizing the easiest tactics
Many teams are moving in the right general direction: community building, earned authority, owned audiences, expert content, and traffic diversification. The most prioritized strategies include building brand presence on social platforms at 59%, GEO and AEO optimization at 54%, and creating authoritative expert content at 44%.
Half of surveyed marketers say organic traffic has fallen since AI Overviews arrived, but the data points to pressure rather than collapse, with 30% reporting no change.
But the least prioritized strategy is original research and data, at only 15%. I see that as a strategic inversion.
Original, proprietary research is one of the hardest content assets for AI to replicate or commoditize. It earns citations, attracts links, builds topical authority, and gives journalists, communities, search engines, and AI systems something distinctive to reference.
In GEO, the same pattern appears. Many marketers are using content-led tactics that AI can easily replicate. Long-tail FAQs can help with AI Overviews, and schema can support structure, but neither one builds credibility by itself.
As organic search pressure grows, marketers are finding brand visibility gains across social platforms, AI assistants, direct traffic and Google AI features, according to Fractl and Search Engine Land.
The stronger moat is entity authority: proprietary data, expert perspectives, topical depth, and third-party validation. These are the assets that make a brand worth citing.
GEO measurement is lagging behind execution
Only a little more than half of marketers are confident in their GEO strategy, and only 12% have measurable results. That is understandable for a newer channel, but GEO is becoming too important to manage casually.
Marketers are leaning into practical GEO tactics, with FAQ optimization leading the pack, while entity authority, original research and citations trail behind.
I believe visibility tracking, citation monitoring, branded search lift, and AI-assisted conversion analysis all need more attention. Teams that can prove GEO ROI will be able to defend and grow investment while others are still guessing.
The main barrier to deeper AI integration is not leadership buy-in. Only 2% cite that as the obstacle. The top barrier is team training and skill gaps at 26%, followed by tool fragmentation at 20%, budget constraints at 19%, unclear ROI at 12%, and legal or compliance concerns at 12%.
For search teams, that means AI literacy, prompt strategy, content quality control, and GEO measurement skills may be more valuable right now than adding another tool to the stack.
Most marketers see early signs their GEO strategy is working, but only 12% report measurable results, highlighting a major gap in AI search measurement.
What I would do for a 2026 search strategy
First, I would audit the brand’s AI footprint. I would query the brand name across ChatGPT, Gemini, Perplexity, and Google AI Overviews, then document what is accurate, what is missing, and what is wrong. Waiting until an AI error becomes a PR issue is too late.
Second, I would invest in entity authority and original research. AI cannot invent legitimate proprietary survey data, named expert perspectives, verified brand facts, or original market analysis. Those assets become more valuable as AI systems get better at rewarding genuine authority.
Third, I would distribute visibility across multiple platforms. Google organic remains necessary, but it is no longer sufficient. A brand needs a consistent presence in Reddit discussions, YouTube content, AI assistant responses, review platforms, and earned media.
Fourth, I would build AI content governance, not just AI content workflows. Consumer demand for AI disclosure ranges from 84% to 91% across formats, while only 20% of brands always disclose. That gap is a reputational liability and may become a legal and regulatory one.
Fifth, I would close the GEO measurement gap. If I can connect AI search mentions to traffic, lead quality, and revenue, I can prove ROI at a time when most teams cannot. That creates a budget and strategy advantage that compounds.
Finally, I would double down on what AI cannot easily replicate: proprietary data, named experts, human-verified claims, transparent sourcing, and a consistent high-quality brand voice. In 2026, the brands that treat quality as a strategic differentiator are the ones most likely to be surfaced, cited, and trusted.
Methodology
Fractl and Search Engine Land surveyed 1,008 U.S. consumers and 150 marketers in Q2 2026. The consumer sample was nationally representative across age, gender, and region. The marketer sample included companies ranging from fewer than 10 employees to more than 5,000 and covered roles in SEO, content, social, analytics, paid media, PR, and marketing leadership.
Where noted, findings are compared year over year against the same questions asked in Fractl’s 2025 consumer study conducted with Search Engine Land.
I think one of the biggest mistakes in AI marketing is positioning a product as a replacement for people. That message can win attention in the short term, but I believe it quietly drains trust over time.
This is a little different from what I usually write about, but it matters. The way we talk about AI shapes how customers, employees, executives, and markets respond to it.
In this memo, I want to focus on three things: why “substitution positioning” feels powerful at first but weakens a brand later, what the data says about whether AI is actually replacing people, and how I think companies should position AI instead.
The cardinal sin of positioning in the AI era is replacement. I call it substitution positioning. It is tempting because it sounds bold, efficient, and disruptive. But over time, it creates anxiety, skepticism, and credibility problems.
We have seen this pattern already. Anthropic CEO Dario Amodei predicted that software engineering jobs could disappear within 6 to 12 months as models began doing most or all of what software engineers do end to end. Yet demand for software engineers has continued to look strong.
OpenAI CEO Sam Altman also predicted that many customer support jobs would go away because AI could handle that work better. Soon after, customer service hiring began outpacing the broader job market.
I understand why fear works as a marketing tool. The fear of being replaced gets attention fast. It got me, too. When powerful AI models gained traction, I worried about my own future. But when I still see AI companies hiring copywriters, SEOs, engineers, and support teams, I sleep better.
Fear sells because it taps into fight-or-flight. Layoffs make that story even louder. They let companies frame cost-cutting as innovation and make the replacement narrative feel more real than it may actually be.
But I do not think the facts support the clean replacement story. In New York, companies can indicate when mass layoffs are caused by technological innovation or automation. In one reported period, more than 160 companies filed mass layoffs affecting roughly 28,300 workers, and not one chose AI as the reason. That list included companies such as Amazon and Goldman Sachs.
Researchers at Yale also studied employment data from the Current Population Survey over 33 months and found no evidence of job displacement from AI. To me, the pattern looks less like instant replacement and more like the earlier waves of computers and the internet changing how work gets done.
That is why I keep coming back to this point: stop trying to make replacement happen. It is not happening in the simple, dramatic way many AI narratives suggest.
AI is powerful, but it is also inconsistent. In its current form, it can do some tasks better than humans and fail badly at others. That paradox is often called the Jagged Frontier.
The Jagged Frontier idea matters because it explains why some people see AI as transformative while others remain lukewarm. A BCG and Harvard study of 758 knowledge workers found that people get the most value from AI when they understand what it is good at and where it breaks down.
Microsoft reached a similar conclusion in its 2026 Work Trend Index Annual Report. The company found that a small group of advanced AI users, described as Frontier Professionals, were not simply using AI more often. They also knew which mode of AI use fit each task.
That distinction is important. The best AI users are not handing everything over blindly. They are applying judgment. They know when to use AI as a helper, when to use it as a collaborator, when to use agents for multi-step workflows, and when to keep a human firmly in control.
I still do not trust most AI workflows enough to leave them running with no maintenance, review, or quality assurance. The question I ask is simple: would I bet my brand, customer experience, or revenue on a fully automated workflow with no human oversight?
Klarna is a useful warning here. The company publicly promoted the idea that AI was doing the work of hundreds of agents and helping reduce headcount. Later, it reversed course and rehired humans after leadership acknowledged that aggressive cost-cutting had lowered quality and that customers still wanted a human option.
That is the tradeoff I see with substitution positioning. It creates immediate attention, but it can damage long-term credibility. The words often do not match the operational reality.
Replacement positioning could work if customers truly wanted full replacement and if the technology were consistently ready for it. I do not think either condition is true.
Cost reduction is a strong AI argument because it shows up quickly on the P&L. Productivity gains usually take longer. They build inside companies over time and often take even longer to appear across the broader economy.
But when replacement positioning goes beyond cost-cutting and becomes people-cutting, I believe it starts to antagonize the very people companies need to win over.
We have already seen backlash. Duolingo’s AI-first memo drew heavy criticism before the company reframed AI as a tool to accelerate work rather than replace contractors. Surveys have found that some workers refuse to use AI tools because they fear job loss. Pew has reported that many U.S. adults are more concerned than excited about AI in daily life. Reuters/Ipsos polling has shown widespread fear that AI will permanently displace workers.
There is also a quality problem. When employees believe the purpose of AI is to replace them, they may disengage or produce lower-quality work. In my view, that is not just an adoption issue. It is a positioning failure.
Executives often feel more excited about AI than the employees asked to use it every day. That gap matters. If leadership talks about AI as a replacement engine, employees hear a threat. If leadership talks about AI as leverage, employees have a reason to learn.
Token economics also complicate the replacement story. Some companies have bragged about massive AI usage, but token costs are still a real business variable. As those costs normalize, the math may make junior employees look interesting again, especially when human judgment, context, and accountability are part of the output.
So what should replace replacement? I think the answer is enhancement. Instead of positioning AI as a way to remove people, I would position it as a way to make capable people more effective.
AI can be used in two broad ways. A company can try to reduce the number of people, or it can grow output with the same number of people. The data I have seen suggests that productivity gains often create the stronger return.
A National Bureau of Economic Research paper surveyed 750 executives about AI’s impact on productivity and labor markets. Larger firms showed more interest in replacing labor costs, but the highest ROI came from productivity growth.
That is the lesson I take from the research: doing more with the talent you already have is often stronger than trying to remove the talent that knows what good work looks like.
Building products has become easier, but distribution has not. When supply explodes, the scarce thing is not output. The scarce thing is being the product, brand, or service that actually gets chosen.
That is why positioning matters more than ever. Product quality still matters, but the way I frame AI use can determine whether people see it as empowering or threatening.
My takeaway is simple: I would stop selling AI as a people replacement. I would sell it as judgment leverage, workflow acceleration, and creative expansion. Fear can get attention, but empowerment is a better long-term strategy.
This post first appeared on the author’s website and is republished here with permission.
Every so often, I see a product launch turn into a marketing lesson bigger than the product itself. Selena Gomez’s Rare Beauty did that with a new fragrance, but it was not only the scent that drew attention. The bottle became the story. Its accessible, easy-to-use packaging sparked conversation, earned praise from accessibility advocates, and reminded me how powerful inclusive design can be when it is built into the product from the start.
For me, the lesson is clear: accessibility is not a side note. It can become the campaign. One thoughtful design choice created cultural impact that would be hard to buy with media spend alone. It also showed why accessibility can build loyalty, strengthen brand reputation, support compliance, and drive measurable growth.
Accessibility as a campaign strategy
I do not see Rare Beauty’s accessibility work as a one-off moment. From packaging to pricing to its ongoing mental health advocacy, the brand has consistently made inclusivity part of its identity. That matters because consumers can usually tell when a brand is chasing attention versus when it is acting from a real strategy. They reward brands that lead with values and follow through.
Rare Beauty is not alone. I see leading brands across industries using accessibility as a differentiator, not a footnote. Apple often frames accessibility features as part of product innovation. Microsoft has brought inclusive design into mainstream campaigns, including adaptive gaming products that positioned accessibility as a source of creativity and connection. In fashion and retail, brands like Tommy Hilfiger and Unilever have put adaptive design into product launches and brand identity instead of treating it as a niche offering.
Studies from Edelman and McKinsey show why this shift matters. According to those studies, 73% of Gen Z choose to buy from brands they believe in, and 70% say they try to purchase products from companies they consider ethical. I do not see those as fringe preferences. I see them as mainstream expectations that should change how marketers build trust and growth.
The $18 trillion market marketers overlook
More than 1.3 billion people globally live with a disability. Together with their friends and family, they control more than $18 trillion in spending power, according to the Return on Disability Group. I believe marketers should view this as more than a compliance issue. It is a growth opportunity, a reputation opportunity, and a trust-building opportunity with one of the world’s largest and most passionate consumer groups.
That passion often turns into advocacy. In discussions with AudioEye’s A11iance Team, a group of individuals with disabilities who regularly share feedback on real-world accessibility experiences, one member said, “If I find a website that works and works very well for me, I will always recommend it to friends and family because I want people to have the same experience that I have.”
Another A11iance Team member, Maxwell Ivey, put it this way: “The cheapest form of advertising is word of mouth, and people with disabilities can have some of the loudest voices when we find people willing to make the effort. Because it’s that sincere effort over time that really counts with us.”
When accessibility becomes part of the customer experience, I see it create something media budgets cannot easily buy: trust and loyalty that scale through advocacy. But the reverse is also true. In a survey of assistive technology users, 54% said they do not feel eCommerce companies care about earning their business.
That should get every marketer’s attention. Too many brands are still fighting for the same crowded audience segments while overlooking a major opportunity in plain sight. When they do, they leave loyalty, advocacy, and revenue on the table.
Here is where I see many brands stumble: accessibility often stops at the shelf. Marketers invest heavily in packaging, store displays, and product design, while digital experiences lag behind. Yet those digital experiences are often the first and most important touchpoints customers have with a brand.
As accessibility-led design earns more attention, loyalty, and earned media, the gap between physical product innovation and digital experience becomes harder to ignore.
AudioEye’s 2025 Digital Accessibility Index found an average of 297 accessibility issues per web page detectable by automation alone. Each issue can create friction in the customer journey, cost a conversion, or introduce compliance risk under frameworks such as the Americans with Disabilities Act (ADA) and the European Accessibility Act (EAA).
I would not launch a campaign without a brand review or a legal check. In the same way, I do not think any digital touchpoint should go live without an accessibility review.
Four moves marketing leaders can make
Too often, I see accessibility treated as a risk to manage instead of an advantage to use. The marketers who gain ground will be the ones who change that mindset. I would start with four practical moves.
1. Make accessibility your campaign hook
I would not hide accessibility in the fine print. I would lead with it. Brands like Rare Beauty have shown that inclusive design is the story. Build campaigns where accessibility is not an afterthought, but the differentiator that earns attention and loyalty.
2. Bake it into your brand system
Accessibility should not sit off to the side. I would make Web Content Accessibility Guidelines (WCAG) alignment part of the brand system, right alongside typography, logos, and tone of voice. When accessibility is documented and expected, it becomes easier to apply across every campaign.
3. Use data as your proof point
Marketers are storytellers, but numbers strengthen the story. I would track accessibility improvements such as fewer user-reported barriers, higher accessibility scores, stronger alt text, better color contrast, and more usable forms. Then I would connect those metrics to business outcomes like conversion, reach, and sentiment to show how accessibility drives ROI, not just compliance.
4. Protect accessibility like brand safety
I would treat accessibility with the same seriousness as brand safety. Every update, seasonal campaign, and product drop should be monitored for accessibility. Trust and reputation are too valuable to leave exposed.
The competitive advantage
Rare Beauty’s fragrance launch proved something important to me: when a brand leads with accessibility, the story can write itself. Loyalty builds more authentically, and momentum feels more natural because the value is real.
The larger opportunity is that many brands still do not see it. They continue to treat accessibility as a compliance checkbox when it can be a growth strategy.
For marketers, that is the wake-up call. Accessibility builds loyalty. It strengthens brand reputation. It supports compliance. And it can drive measurable growth across marketing efforts.
Rare Beauty showed how accessibility can capture attention at the shelf. Now I see the next opportunity clearly: making sure that same accessibility carries through online. When every touchpoint welcomes everyone, every campaign has a better chance to deliver its full impact.
Shopify has just launched Campaign Autopilot, an innovative tool powered by AI designed to streamline marketing efforts. By taking the reins of campaign creation, management, and optimization across various channels, it’s set to significantly ease my workload as a merchant.
Imagine having the power of Campaign Autopilot directly within the Shopify admin. This feature is in its early access stage but is already offering tremendous support in marketing automation.
What’s happening? With AI technology, Campaign Autopilot orchestrates marketing campaigns on my behalf across channels like Meta, Shop Campaigns, and email, enhancing my marketing strategy effortlessly.
Additional support is in the pipeline for platforms such as ChatGPT Ads, Microsoft Advertising, and Snapchat—making it a versatile tool for future needs.
What makes this system stand out is its ability to autonomously handle campaign setup, financial planning, and constant adjustments based on real-time performance, leaving me time to focus on other aspects of my business.
Why I care. By simplifying the complex world of multi-channel marketing, Campaign Autopilot provides me with a user-friendly platform that traditionally relied on the expertise of agencies or specialized teams. Now, I can set my budget and objectives while Shopify’s AI takes care of the intricate details.
How it works. I decide on a monthly budget, select channels to collaborate with, and set guidelines. From there, Campaign Autopilot executes:
Creating and launching campaigns.
Distributing my budget across channels.
Adjusting expenditures based on feedback.
Suggesting automated email initiatives.
Evaluating and refining campaign effectiveness on an ongoing basis.
I have full control—approving or tweaking campaigns, modifying budgets, or halting actions whenever necessary.
How it stands out. Campaign Autopilot redefines contemporary campaign management by sidestepping traditional, more labor-intensive methods.
Its unique approach taps into performance insights gleaned from millions of Shopify stores, offering data-driven enhancements and budget allocations.
Moreover, it functions separately from existing Meta or Shop ads, ensuring previously planned campaigns remain unaffected.
The bigger picture. Shopify is not just about ecommerce anymore. It’s now moving into the realm of growth and customer acquisition by embedding AI deeper within its merchants’ operations.
Industry trends show a shift towards autonomous marketing systems, which can run campaigns with minimal human intervention, constantly optimizing performance along the way.
What to keep an eye on. Shopify will be expanding its channel support further, potentially integrating with platforms like ChatGPT Ads, Microsoft Advertising, and Snapchat.
There’s also the AI assistant, Sidekick, which I can use for reviewing recommendations, triggering actions, and keeping a close watch on campaign outcomes.
First spotted. This update came to my attention courtesy of Digital Marketing Consultant Susan Richards-Benson via a LinkedIn post, where she recommended it as a game-changer for smaller eCommerce brands.
Welcome to my introduction of The Marketing Engineer Podcast—your essential listen if you’re a marketer who loves to build. I dive into episodes featuring trailblazing practitioners and leaders who share how they’ve revolutionized their team’s workflows.
In every episode, I promise you’ll hear directly from those who’ve mastered the art of scaling marketing initiatives without compromising on quality. They’ve invented novel capabilities, unlocking potential that many haven’t dared to imagine.
I’ve noticed that not every organic visit deserves the same consideration these days. It’s become evident that I need to hone in on high-intent pages to truly measure SEO success and its impact on my business.
Recently, HubSpot rebranded its flagship conference from INBOUND to UNBOUND. This change wasn’t merely cosmetic; it represented a strategic pivot away from old-school SEO strategies that emphasized top-of-funnel traffic.
Modern search dynamics are nudging us closer to a zero-click environment. Trust me, the click-through rate curve is rapidly evolving. Studies show that around 60% of searches now conclude without a single click leading to the open web.
I’ve also observed that the discovery layer of search has shifted significantly. Nowadays, buyers are researching vendors within platforms like ChatGPT and Perplexity before they even consider clicking a traditional blue link.
Attribution has become increasingly complex. The modern buyer journey is fragmented, often starting with AI-assisted search and only finalizing on my website when the prospect is ready to make a decision.
This shifting landscape distorts my SEO reports if I focus solely on traffic as a success indicator. I’ve decided it’s time to pivot and redefine how I present traffic data to marketing leadership, ensuring that my reports align more closely with business impact.
A lively discussion on LinkedIn, led by Peter Rota, debated whether to completely retire organic traffic as an SEO metric. The consensus, I’ve found, is to use traffic with caution, always considering intent and the actual revenue it drives.
While organic traffic isn’t inherently bad, relying on it solely as a KPI lacks context and could be misleading. Adam Heitzman pointed out that it’s essential for traffic metrics to come with intent-based context for more accurate reflections of performance.
In a situation where low-intent traffic is reduced and focus is shifted towards high-intent pages, I’ve noticed that although overall visits might drop, conversions and revenue can actually increase due to better-quality traffic.
This understanding has led me to differentiate between top-of-funnel visits and more meaningful page interactions, thereby filtering out the data noise and focusing on what really matters in my dashboards.
Rand Fishkin beautifully summarized that top-of-funnel marketing feels like ‘rented land’—and he’s right. Buyers are now finding most basic information elsewhere, opting for instant answers on platforms like Reddit, TikTok, and within LLMs.
As of now, generic informational traffic is dwindling. Ironically, many SEO efforts are still devoted to content types most vulnerable to AI-driven change, such as FAQs and long-form articles.
Given this shift, it’s crucial for me to track pages based on their transactional value—those that AI can’t easily replace. I’ve narrowed my focus to four main areas: homepage, pricing pages, products and solutions pages, and money content pages.
Focusing my reporting on these key pages allows me to cut through the noise and concentrate on the traffic truly affecting my business’s bottom line.
For example, when a prospective B2B buyer starts searching for a modern CX platform, they’ll go through AI search, Google verification, and eventually land in the dark funnel for conversion.
Understanding these layers helps me recognize which organic traffic is significant enough to report, enhancing my insights into customer journeys and how they interact with my website content.
I know I must move away from outdated traffic analysis techniques to embrace more effective, modern reporting standards that focus on directional trends and macro shifts indicative of real business impact.
By focusing on page health instead of unreliable keyword-level reporting and monitoring branded search volume as an AI visibility proxy, I can capture a more accurate view of my current impact.
