May 14 2026
Stop Treating AI Visibility As One Problem. It’s Actually Three, On Three Different Layers via @sejournal, @DuaneForrester

When a brand stops appearing in ChatGPT, or when its share of voice in Perplexity drops by half over a quarter, the typical response from the marketing org is to write more content. Sometimes a lot more. The thinking goes that if AI systems aren’t surfacing the brand, the fix is to feed them more material to work with. That instinct is a misdiagnosis. It’s a retrieval-layer fix being applied to what is increasingly a different kind of problem entirely, and the cost shows up as wasted budget, missed quarters, and a creeping sense that the work isn’t connecting to the outcomes anymore.

The mistake is treating AI visibility as a single problem when it isn’t. There are three structurally different layers between your brand and the answer a user receives, each with its own failure modes, its own fixes, and increasingly its own organizational owner. Diagnose the wrong layer, and the fix doesn’t land.

Where Most Of The Conversation Has Been Living

The first layer is retrieval. This is where the AI search optimization conversation has spent most of the last two years. The mechanics are familiar in shape if not in detail. When a model needs to answer a question grounded in real-world content, it pulls relevant material from external sources and uses that material to construct the response. The technical name is retrieval-augmented generation, or RAG, and the layer it operates on is the gateway between your content and the model’s output.

This is where crawlability, parseability, and chunk-friendliness do their work. If your content can’t be retrieved cleanly, nothing downstream matters. The visibility tracking platforms most marketing teams have evaluated this year measure outcomes that depend on this layer functioning, which is why they tend to reward the same disciplines that produced good results in classical search: structured content, schema markup, self-contained answers, clean technical implementation.

But retrieval has a structural limit, and Microsoft Research has been unusually direct about it. Plain RAG, in their words, struggles to connect the dots. It retrieves chunks of text that look relevant to the question, but it cannot reason about how those chunks relate to each other. When the answer requires synthesizing information across multiple sources, or when the question is broad enough that the right answer depends on understanding patterns across an entire dataset, retrieval alone breaks down. The model gets the chunks and has to guess at the relationships, and guessing is where hallucinations enter.

The discipline question this layer asks is straightforward. Can the model retrieve our content at all, and is it retrieving the right content for the right query? Most marketing teams have some version of this work in flight already, even if the specific tactics have shifted from classical SEO. But retrieval is only the gateway. Even when a model retrieves your content correctly, what it does with it depends on whether you exist as a recognized thing in the layer above.

The State of AEO/GEO Report Conductor 2026

Where Entity Recognition Does The Real Work

The second layer is the relationship layer, and the dominant structure on it is the knowledge graph. The major search infrastructures all maintain one. Google’s Knowledge Graph, Microsoft’s Satori, and the open knowledge graph built on Wikidata and schema.org collectively define how your brand is represented as an entity, what category you sit in, and which other entities you’re connected to.

This is the layer that decides whether AI Overviews and large language model responses treat you as a recognized member of your category, or as one fuzzy candidate string among many. Brands that exist as clean, well-defined entities get cited consistently. Brands that exist as undifferentiated tokens scattered across the open web get pattern-matched against fifty other candidates and lose more often than they win.

Knowledge graphs have been around long enough that the discipline is reasonably mature. Schema markup on owned properties, consistent naming and identifiers across the open web, structured presence on the high-trust nodes like Wikidata entries and review platforms, and the slow accumulation of brand mentions in contexts that the graph treats as authoritative. This is where the unlinked brand mentions conversation lives, because consistent contextual mentions strengthen the entity even without a hyperlink attached. The fix at this layer is structural rather than volume-based. Writing more content does almost nothing if the entity definition underneath it is fuzzy.

The discipline question here is harder than the retrieval-layer question. Are we a clean, defensible entity in our category, or are we still being pattern-matched against fifty other candidate strings? A brand that can’t answer that question affirmatively is going to lose ground in AI search, regardless of how much content it produces, because the second layer is where the model decides what your content is actually about.

The knowledge graph tells the model what your brand is. But increasingly, your brand has to function inside a third layer that most marketing teams haven’t met yet, where the model isn’t just understanding you, it’s being asked to reason about you on behalf of someone making a decision.

The Layer Enterprise Companies Are Quietly Building Right Now

The third layer is the context graph, and this one needs a careful introduction because most of the marketing conversation hasn’t reached it yet.

A context graph has the same structural shape as a knowledge graph, with entities, relationships, and typed connections, but it’s grounded differently. A knowledge graph models the world. It tells you what things are and how they relate in general. A context graph models a specific organization’s data, decisions, policies, and operational reality. The cleanest framing I’ve seen calls a knowledge graph the library and a context graph the operating manual written by the people who actually run the place. The library tells you what exists. The operating manual tells you what’s relevant, what’s authorized, and what to do about it right now. The library is read-only semantic infrastructure. The operating manual is a living operational layer that grows every time a business process executes.

What separates a context graph from anything that came before it is that governance lives inside the graph rather than alongside it. Policies, permissions, validity windows, and authorization rules are nodes the graph itself queries, not external documentation applied at the edges. When an agent retrieves something from a context graph, the result has already been filtered through what’s currently authorized, currently valid, and currently applicable. The graph is also continuously evolving, so what it knows about you this week is not necessarily what it knew last quarter. That’s where the word “governed” comes from when people in this space talk about governed retrieval. It isn’t a frame, but rather the architecture.

That architecture used to be invisible to anyone outside the organization that built it, which is why marketers haven’t had to think about it. That changed at Google Cloud Next ’26, when Google introduced the Knowledge Catalog inside its new Agentic Data Cloud. Google’s own description of the product, written in their own first-party blog content, says the Knowledge Catalog constructs a unified, dynamic context graph of your entire business, enabling you to ground agents in all of your business data and semantics. That sentence is the moment the term left the data-engineering blogs and entered enterprise procurement vocabulary.

The reason this matters for marketing is that context graphs are what’s going to power the next generation of agents inside your enterprise customers. Gartner projects that 40% of enterprise applications will be integrated with task-specific AI agents by the end of 2026, up from less than 5% in 2025. Procurement agents, competitive intelligence agents, content strategy agents, vendor evaluation agents. These agents won’t be reasoning about your brand from the open web. They’ll be reasoning about your brand from inside their company’s context graph, and what that graph says about you depends on what got ingested into it.

That ingestion is where the work for marketing lives. The brand that arrives at the context graph fragmented arrives weak. If your category positioning is inconsistent across owned and earned media, the graph picks up the contradictions and represents you ambiguously. If your entity data is fuzzy on the second layer, it stays fuzzy when it gets pulled into the third. If your third-party signal is thin or contradictory, the graph has nothing solid to anchor to. The work is upstream of the graph, but the consequences land downstream of it, inside an agent’s reasoning process that you’ll never see directly.

I think of this discipline as governed visibility. The practice of making sure your brand arrives at the context graph in a state that holds up under governed retrieval. Clean entity definition, consistent third-party representation, reliable structured data, and a category position that doesn’t fall apart when an agent traverses the relationships around it. Governed visibility isn’t a new tactic stack. It’s the result of doing the second-layer work well enough that the third layer has something solid to ingest.

The discipline question at this layer is the one most marketing teams haven’t started asking yet. When an agent inside our customer’s company is reasoning about us, what does it find, and is the version of us it finds the version we’d want it to act on?

Three layers, three different problems, three different fixes. But also three different responsibility zones, and that’s where most teams are quietly losing ground.

The Reason Most Teams Will Lose This Even Though They’re Working Hard

Each layer maps to a different organizational responsibility, and most marketing teams only own one of the three cleanly.

  • The retrieval layer is shared with web, dev, and sometimes IT. Marketing influences what gets published, but the infrastructure that makes content retrievable sits in someone else’s domain.
  • The knowledge graph layer is genuinely marketing’s territory. Schema discipline, entity definition, third-party signal, brand consistency, the slow structural work that compounds over years.
  • The context graph layer is where IT owns the infrastructure inside the customer’s organization, but marketing has to influence what gets ingested. The work is upstream, and the consequences land downstream, often invisibly.

The teams that win in 2026 are the ones that figured out how to operate across all three responsibility zones rather than perfecting their work on just one. Most teams I see are still optimizing their owned content, which is the retrieval layer, while losing ground on entity definition, which is the knowledge graph layer, and remaining completely absent from the context graph conversation, which is the layer where some enterprise businesses are quietly standing up right now.

The work isn’t writing more content. The work is figuring out which layer the problem actually lives on, and building the disciplines to operate on all three. Governed visibility is the third-layer discipline that marketing is going to have to develop, whether or not the term sticks. The brands that build it now will look prepared in eighteen months. The brands that don’t will be wondering why their content investments stopped producing the visibility they used to.

If any of this lands or contradicts what you’re seeing inside your own teams, I want to hear about it. Drop a comment about which layer your work has been concentrated on, where you’re seeing the gaps, or where the responsibility zones break down inside your organization. The patterns are still forming, and the conversations in the comments tend to be fresher than anything else.

A lot of the measurement frameworks for this kind of work sit in The Machine Layer, which expands the original 12 KPIs for the GenAI era into something teams can actually run against.

The State of AEO/GEO Report Conductor 2026

More Resources:

This was originally published on Duane Forrester Decodes.

Featured Image: Master1305/Shutterstock; Paulo Bobita/Search Engine Journal

Ecommerce MGMT 0 Comments
May 12 2026
Scaling AI Content Is The #1 Enterprise Priority: How Do You Scale Without Penalty? via @sejournal, @theshelleywalsh

Scaling AI content generation is the number one content strategy for enterprise organizations optimizing for AI search visibility. According to Conductor’s 2026 State of AEO/GEO CMO Investment Report, which surveyed over 250 executives and digital leaders across 12 industries, it ranked above structured data, above authoritative long-form guides, and above original research. Across every maturity level surveyed, from organizations venturing into AI visibility to those with enterprise-wide adoption, it was the top answer.

However, this may also be where the problem starts.

The State of AEO/GEO Report Conductor 2026

AI Content Scaling Is Failing

Inside the report, Aleyda Solis acknowledged the strategic intent but raised a concern: “Although it’s possible to leverage AI for content, a personalized editorial and optimization workflow is required to ensure quality, originality, and expertise by integrating unique brand insights and first-party data, which is exactly what AI platforms are likely to cite.”

Eli Schwartz predicted that the current AI content scaling trend “will change in 2026 as Google and other LLMs push back against low-quality content” with what he described as an AI version of Google’s Helpful Content Update. He also flagged that the leaders he speaks with are “somewhat skeptical about the effectiveness of mass amounts of AI content, but are afraid of being left behind if they don’t do this.”

Fear of missing out is not a basis for an effective content strategy.

Lily Ray, who is known for her in-depth analysis, said earlier this year: “Interesting, but not surprising, to see people on LinkedIn sharing their stories of losing all search visibility (sometimes overnight) after an aggressive AI content strategy.” She added: “Just because it’s easy doesn’t mean it’s a good idea.”

I strongly echo that if something is easy, it’s easy for everyone and not competitive.

Pedro Dias documented that in June 2025, Google began issuing manual actions specifically for scaled content abuse, targeting sites that had been mass-publishing AI-generated content. Sites across the UK, US, and EU received Search Console notifications citing “aggressive spam techniques, such as large-scale content abuse.”

Dan Taylor recently wrote about the mechanics of this failure in granular detail, sharing traffic graphs that illustrate what Glenn Gabe calls the “Mt. AI” effect, an initial spike when new content floods the index, followed by a cliff edge as Google’s quality threshold assessment kicks in. What Taylor identifies as the real problem isn’t AI content itself, but the absence of any genuine content strategy underneath it. “The real problem lies in the fact that scaling content production, regardless of the method, often introduces a raft of quality control issues,” he writes. The freshness boost that new URLs receive masks those issues temporarily. Then it doesn’t.

I write, read, and edit a lot of content, and I can clearly see when AI has been used to supplement writing. Some writers can do this well and have input enough of their expertise to get reasonable results. Others not so much, where they are leaning on AI to supplement their lack of knowledge or expertise. For myself, I can get astounding results from Claude when I input quality, unique research, but I do have to invest a huge amount of guidance to get anything worth publishing.

To be clear, I’m not anti-AI usage. Like Google, I’m focused on good quality content and writing.

That gap between what AI produces by default and what’s actually publishable is precisely where the opportunity still lives for writers who know their subject. Exceptional human-guided content isn’t a compromise. Right now, it’s the competitive advantage.

Google Is Consistent About AI Content

Google’s position on the use of AI content and quality content has been consistent.

Danny Sullivan spoke at the Google Search Central event in Toronto in April 2026 about the concept of commodity versus non-commodity content.

Commodity content is everything an AI can produce from publicly available information. Non-commodity content requires you to have actually done something, know something from direct experience, or hold an opinion grounded in genuine expertise. And this is what Google considers your competitive strength going into the AI era.

John Mueller framed AI content abuse in the context of Google’s Quality Rater Guidelines update, which now explicitly groups AI-generated content in a section about content created with little effort or originality. Quality raters are instructed to apply the lowest rating to pages where all or almost all of the content is auto- or AI-generated with little to no effort, originality, or added value, regardless of production method. Google’s guidelines are explicit that AI tools alone don’t determine the rating, effort, originality, and value do.

This all aligns with the foundations of what Google wants to surface – quality content that demonstrates first-hand experience.

We Have Seen This Before

Lily Ray ran a test by asking Perplexity for SEO news and received a confident report about the “September 2025 Perspective Core Algorithm Update,” a Google update that had never happened. The citations Perplexity provided pointed to AI-generated posts on SEO agency blogs. Sites that had run a content pipeline, hallucinated an update, and published it as reporting. Perplexity read this and treated it as source material, and served it back to her as fact.

There’s a historical parallel here that some older SEOs will recognize.

Early digital PR/link building efforts involved seeding stories or content into lower-tier publications because top-tier journalists used them as source material, and it generated implied credibility of multiple citations. Journalists then began to cite what was published by other sites, and published sites cited and referenced them in the same citation cycle.

Another example I saw recently involved several articles [incorrectly] reporting that Jeremy Clarkson and his partner Lisa Hogan (from the top Amazon UK show Clarkson’s Farm) were spending time apart and ending their relationship. What Clarkson had actually said was that they deliberately go their separate ways during the day so they have something interesting to talk about in the evening. This might be a low-stakes example, but it perfectly illustrates how quickly misinformation spirals.

Screenshot from search for [have jeremy clarkson and lisa hogan split up], Google UK, May 2026

Content Scale Is Strategy And Challenge

The highest-maturity organizations in the Conductor report (organizations where AEO/GEO is a core digital priority) have already arrived at the right conclusion, and they are the only group in the study that prioritized original research based on first-party data as a content strategy. They understand that first-party data and genuine research cannot be replicated by running an AI content operation and exclusivity is the point.

The Conductor report’s headline finding is that 94% of enterprise organizations plan to increase AEO/GEO investment in 2026, and that AEO/GEO has become the number one marketing priority, above paid media and paid search. The report also surfaces that generating AI-optimized content at scale is not only the top stated strategy, but also the top stated challenge. Brands know what they want to do, but they don’t know how to get there.

How Enterprise Brands Can Scale And Win

Industries that already operate on programmatic content models (travel, ecommerce, large product catalog sites) have been producing content at scale for years. A hotel comparison site generating location pages, a retailer producing thousands of product descriptions, a marketplace creating structured listings are all legitimate use cases where AI can effectively accelerate something that was already happening.

But, to have real brand differentiation, investing in a unique voice and approach to how they write these listings can set them apart and be a competitive advantage.

Alongside their programmatic content, enterprise brands should also be finding ways they can produce content that is genuinely difficult to replicate. Experience-driven, data-grounded, editorially considered, and specific in ways that only a real subject matter expert would know.

For an enterprise brand to win at scaling content, my recommendation is to wrap AI usage around subject-matter experts and editors. The power of AI is how it can turn experts into super producers and allow them to produce more. Enterprise brands should invest in finding these super producers and then use AI to exponentially scale their ability, not try and replace them.

AI Amplifies What’s Already There

The most useful frame for AI in content production is as an amplifier of whatever you bring to it. If you have genuine subject matter knowledge, proprietary data, and the editorial discipline to maintain quality, AI can meaningfully accelerate your output. It helps you produce more of what you’re already good at, faster.

But if you don’t have those things, AI produces more of what you don’t have, faster. The content output has structure, length, and the right vocabulary, but it contains nothing that an LLM can’t generate from publicly available information. Nothing that differentiates you from every other brand trying to scale with AI in the same way.

As I said earlier, I have produced in-depth content for years, and for me, AI is a creative amplifier and an exciting tool that augments what I know. It doesn’t replace me, and it certainly can’t do what I can by itself. On that basis, I see subject-expert editors as being the new information gatekeepers.

For enterprise brands who want to scale their content they should start with understanding that good content is not about including everything; it’s about knowing what not to include.

The State of AEO/GEO Report Conductor 2026

The full Conductor 2026 State of AEO/GEO CMO Investment Report is available here.

More Resources:

Featured Image: ImageFlow/Shutterstock

Ecommerce MGMT 0 Comments
May 8 2026
5 Content Marketing Ideas for June 2026

June 2026 offers ecommerce marketers a mix of global events, seasonal demand, and cultural moments that can translate into effective content.

From the FIFA World Cup to weddings, the month presents opportunities to connect products to how customers live, celebrate, and spend.

The aim of content marketing is to attract, engage, and retain customers. Here are five topic ideas your business can use in June 2026.

FIFA World Cup

Image of a FIFA World Cup sign and logo

The FIFA World Cup is among the most popular global sporting events.

The Fédération Internationale de Football Association (FIFA) World Cup is a top global sporting event, even beyond the Olympics. An estimated 5 billion fans watched the 2022 World Cup, held in Qatar.

This time, the games will be held in Canada, Mexico, and the United States from mid-June through July across 16 metropolitan areas.

For ecommerce content marketers, the event’s scale offers an unusually good opportunity to connect soccer to products. Here are some examples.

  • A store selling educational products could publish a series on math and science underlying soccer.
  • A grilling supply retailer might create viewing guides that include match-specific party ideas and recipes.
  • Consumer electronics sellers might release the “Best Gear for Streaming the 2026 World Cup.”

Wedding Season

Image of a male and female on their wedding day

June has long been a popular month for weddings.

June is a popular month for weddings in the Northern Hemisphere. August is a close contender, but June is the champ.

What’s more, the average American wedding costs $36,000. Attracting even a share of venue, caterer, jeweler, dressmaker, and florist expenditures is worth the marketing effort.

Wedding-related articles, videos, or podcasts should be useful and participation-based, subtly including products without forcing them. Here are a few example titles.

  • D2C beauty products brand: “Best Makeup for Wedding Photos”
  • Luggage and travel store: “How to Pack for a Wedding Weekend”
  • Power tool retailer: “Guide to DIY Wedding Gazebos”

D-Day and the 250th

Photo of troops wading ashore during D-Day.

A landing craft disembarking Allied troops on the morning of June 6, 1944, on beaches in Normandy, France. Source: Robert F. Sargent via Wikipedia.

June 6, 2026, marks the 82nd anniversary of D-Day, the massive Allied amphibious invasion of Normandy, France, that eventually freed Nazi-occupied Europe. The U.S.-led operation was the beginning of the end of World War II.

The battles at Utah and Omaha beaches would themselves merit articles and videos that spoke to American and Allied history, heritage, and unity. But there is a special opportunity this year: the 4th day of the following month is the U.S.’s 250th birthday.

Content marketers in the U.S. could publish articles featuring patriotic rituals or profiling heroic actions of the country’s founders or D-Day participants. Products become part of a larger story about durability and service.

Outdoor Cooking

Image of a male cooking outdoors on a grill

Outdoor cooking content can go beyond recipes.

Summer offers more opportunities to cook outside — on a patio, at a campsite, or in a full kitchen. June content can help shoppers find practical ways to simplify preparation or expand selections. Here are example titles.

  • “How to Cook a Complete Meal Outdoors”
  • “Pizza Ovens, Smokers, Grills: Which Is Right for You?”
  • “Beginner’s Guide to Cooking Over an Open Fire”

Each article can target a specific cooking act to incorporate products such as cookware, fuel, tools, or equipment. Merchants are not competing on recipes but on usefulness, helping customers cook better outdoors.

Time with Dad

Image of a father and son playing baseball

In 2026, try Father’s Day content that focuses on experiences.

Father’s Day is a top marketing opportunity, but gift guides alone can feel repetitive. Many shoppers look for ways to spend time together.

Content that focuses on shared activities can address that need while connecting to the store’s products.

  • Sporting goods retailers might suggest a day of fishing or a backyard game, including the gear to get started.
  • Kitchen brands could feature a meal to cook together, from preparation through serving.
  • Home improvement stores can publish a weekend project for a father and child.
Ecommerce MGMT 0 Comments
Apr 24 2026
Localized Distribution In The AI Era: The DIRHAM Framework via @sejournal, @gregjarboe

Last year, I taught a module on content marketing around the PESO model (Paid, Earned, Shared, and Owned media). Matt Bailey asked me to include more content about influencers in this year’s module; I joked that it might take me all morning to come up with a new acronym. He shot back, “Can you adapt it to a DIRHAM model instead of PESO?”

That’s when I had an epiphany: Buried beneath our banter was a strategic insight.

Publishing great content used to be enough. Write something valuable, post it, and trust that search engines, social feeds, and your audience will handle the rest. For most of the past decade, that assumption held. It no longer does.

Between your content and your audience now stand three powerful gatekeepers, and none of them are human. AI summarization systems like Google’s AI Overviews surface answers without delivering clicks. Social feed algorithms pre-select what users ever encounter, often before those users have articulated what they want. Private messaging networks carry enormous volumes of content sharing through channels that are invisible to any analytics tool. If your content isn’t built to pass through all three of these filters, quality becomes irrelevant. It simply won’t be found.

In response to this challenge, I created the DIRHAM framework.

Why The Old Frameworks No Longer Work

Content marketers generally have organized their thinking around PESO: Paid, Earned, Shared, and Owned media. The model served its purpose well as a categorization tool, helping teams allocate budgets and map campaigns across channels. The problem is that PESO was built to answer a distribution question that no longer captures the real strategic challenge. It told you where to place content. It said nothing about how to make content visible in a world where algorithms, not humans, decide what gets surfaced.

DIRHAM is a visibility system rather than a categorization scheme. It is behavior-driven and AI-aware, designed around how content is actually discovered today rather than how it traveled through digital channels a decade ago. The distinction matters because discovery itself has fragmented across three systems that operate on entirely different logic. Search has become an AI answer engine that returns summaries instead of links. Social platforms use recommendation algorithms that predict what users want before those users have searched for anything. And messaging apps carry significant content sharing through what marketers call dark social, private exchanges that leave no traceable footprint in your analytics dashboard.

Each of these systems decides relevance differently, which means a single distribution strategy cannot serve all three. That, in turn, exposes the deeper problem with channel-first thinking. Asking “where should we post?” is no longer the right starting point. The more productive question is how this particular audience actually discovers things, and what each system needs to see before it will serve your content to them.

The Six Pillars Of DIRHAM

D: Digital Advertising

The role of paid media has changed in ways that most campaign budgets haven’t caught up with yet. The old model treated paid advertising as a direct delivery mechanism: You bought impressions, people clicked, some of them converted. In the AI era, that logic is incomplete. Paid media’s primary strategic function now is to generate the early engagement signals that algorithms need before you should invest in distributing your content organically. Paid doesn’t deliver to the audience anymore. It earns the algorithmic attention that makes organic delivery possible.

This reframing has real implications for how budgets should be structured and how creative should be evaluated before spend. Rather than committing to a single campaign execution, the more effective approach is a three-stage cycle: Run small tests across multiple creative variations, use AI performance tools to identify which executions are generating genuine signal, then scale selectively into what’s actually working. Small bets, fast reads, concentrated fuel.

Targeting has matured in a parallel direction. Legacy demographic segmentation worked from surface assumptions about who a person was based on age, gender, and location. AI-powered clustering works from behavioral reality, tracking what people actually do, what they read past, what they share, what they ignore. Content that mirrors real behavioral patterns gets amplified. Content that shouts without matching those patterns gets filtered out, regardless of budget. And creative that looks like advertising at a glance will fail to generate the engagement signals that trigger wider distribution in the first place. Native creative, content that looks and feels like organic content in each platform’s environment, is not just aesthetically preferable. It is structurally necessary.

I: Influencer Partnerships

In an environment where AI-generated content floods every platform, human credibility has become the most effective filter against noise. Audiences, consciously or not, are calibrating their attention toward sources that have demonstrated genuine expertise or authentic experience, and away from the polished but anonymous brand voice that could have been written by anyone or anything. This is why influencer strategy in the DIRHAM model is not primarily about reach. It is about borrowed trust.

The distinction matters because it changes who you look for and what you ask them to do. A creator with 200,000 engaged followers who have followed them for three years because they trust their judgment is more valuable in this environment than a creator with 2 million followers and a transactional relationship with branded content. The former has built the authenticity, consistency, and credibility that together produce real trust. The latter has reach without the authority that makes recommendations land.

The operational implication is a move away from one-off campaign sponsorships toward integrated, ongoing relationships. When influencer programs feel bought rather than believed, they fail on two levels. They fail to generate the authentic engagement that algorithms reward, and they fail to produce the kind of trust transfer that makes the partnership valuable in the first place. The most effective influencer programs are built around shared narratives and long-term creative collaboration, which produces compounding community value that a single sponsored post cannot. This also means that creator selection has to account for context. In government and public sector campaigns, credibility and safety are the primary criteria, with success measured through sentiment and public awareness. In commercial campaigns, fit and demonstrated performance matter most, and success gets measured through conversion and sales velocity. Reach alone is never sufficient justification for a partnership.

R: Regional And Local Context

AI systems are not passive distributors. They actively parse content to determine who it is for, and generic content sends signals that are simply too ambiguous for the system to act on confidently. Without specific geographic or cultural markers, content can get deprioritized, not necessarily because it’s of poor quality, but because the algorithm cannot reliably categorize it or identify the right audience to serve it to. The counterintuitive result is that narrowing your focus tends to increase your reach. Anchoring content in regional or local specificity gives the system exactly the classification signal it needs to serve the content to people who will engage with it.

One of the most common mistakes brands make when addressing multilingual markets is treating bilingual content as a translation problem. It is not. Arabic and English audiences in the UAE, for example, engage with content on the same platforms through fundamentally different cultural frames. English-language content in that market tends to perform around adventure, exploration, and discovery. Arabic-language content, produced by creators with genuine cultural proximity, centers on heritage, family, and values that are better expressed in local dialect than in formal translated language. The difference is not vocabulary. It is intent and tone, and no translation process produces it reliably. What local creators bring to content distribution is something that should be understood as shared context: an intuitive grasp of reference, nuance, and community expectation that outside brands cannot replicate and cannot purchase directly. They can only access it by working genuinely with people who hold it.

H: Hybrid Content

Hybrid content is what happens when passive consumption and active involvement are designed into the same piece of content. The reason it matters so much in the current environment is that engagement is not merely a metric for how interesting your content was. It is the distribution mechanism itself. When users comment, complete a challenge, share to their own network, or otherwise participate in content, they are not just expressing interest. They are distributing the content on your behalf. Without that participation, reach is bounded by budget. With it, reach compounds through the network in ways that no paid campaign can replicate in isolation.

This changes the design question for content. Broad content, built for a generic audience and a generic platform, tends to produce passive consumption. People scroll past it, or watch it to completion, and move on. Specific content, anchored in a particular cultural reality or a particular community’s concerns, provokes a response. It invites people to add themselves to the story, to disagree or affirm, to share with someone they know, because it lands with enough specificity to feel personal. Gamification, photography challenges, and community incentives work in this context not as marketing gimmicks but as structural mechanisms for turning audience members into distributors. AI tools can accelerate the production of hybrid content significantly, handling drafting, formatting, and initial translation at volume. But the human editorial layer remains essential. Resonance, cultural accuracy, and the kind of tonal authenticity that makes people want to participate cannot be automated. The goal is not automated publishing; it is automated drafting with rigorous human curation.

A: AI Visibility

Becoming visible to AI answer engines requires a different optimization logic than traditional SEO. The governing rule is that AI systems reward reliability and structural clarity above creativity and cleverness. A headline that works brilliantly for a human reader because it is unexpected or witty may work against you in an LLM context, because the machine cannot confidently categorize content whose purpose is obscured by figurative language. Clear, consistent, authoritative content builds the kind of signal that answer engines recognize and cite over time.

Structure is the mechanism. AI models parse structural elements before they interpret meaning, which means clear headers function as navigation signals, declarative sentences enable clean fact extraction, and credibility markers such as named sources, cited research, and identified authorship communicate authority to the system in ways that stylistic sophistication simply does not. If the architecture of the content is unclear, the quality of what’s inside it goes unread.

There is also a significant measurement gap that most organizations have not addressed. AI and LLM conversations represent the fastest-growing discovery channel in most content categories, but they are almost entirely invisible to conventional SEO tools. Tools like Cairrot have emerged specifically to track brand citations inside AI models, showing where and how organizations appear when users ask ChatGPT, Perplexity, or Gemini a relevant question. The new SEO is not optimizing for a position on a search results page. It is optimizing to become the source an AI system trusts enough to cite.

M: Measuring Outcomes

The final pillar of DIRHAM is still where most organizations’ discipline breaks down, and where the gap between doing DIRHAM and doing it well tends to be widest. The standard that should govern every measurement decision is straightforward: If a metric doesn’t change what you do next, it doesn’t matter. Impressions, follower counts, and raw reach have always been easier to report than to act on, and in an era of infinite AI-generated content production, they have become almost entirely disconnected from influence or impact.

The hierarchy that actually serves strategic decisions looks different. Impressions and vanity metrics get ignored. Engagement signals get observed carefully because they reveal which content is generating the algorithmic response and community participation that the other pillars depend on. Behavioral change and decisions get optimized toward relentlessly, because those are the outcomes the content exists to produce. Every campaign run this way becomes the prototype for the next one. The data from this cycle funds better decisions in the next.

For organizations with “trust” instead of “cash” as a strategic objective, particularly in government and public sector contexts, the Hon and Grunig Trust Scorecard provides a quantifiable measurement approach. It assesses trust through three dimensions: Integrity, measured through whether stakeholders believe the organization treats people fairly and considers them in decisions; Dependability, measured through whether stakeholders believe the organization keeps its commitments; and Competence, measured through whether stakeholders believe the organization can deliver what it promises. Stakeholders rate these dimensions on a Likert scale, producing a quantifiable trust score that can be tracked over time and correlated with content and campaign activity.

DIRHAM In Action: The World’s Coolest Winter Campaign

Abstract frameworks earn their place by explaining real results. The UAE’s World’s Coolest Winter campaign, which concluded on Feb. 2, 2026, is an unusually clean example of the DIRHAM model operating at full scale, because the framework wasn’t applied after the fact. Distribution was the blueprint from the beginning.

The campaign’s paid media strategy used TikTok and Snapchat as the primary channels, with short-form cinematic video built specifically for scrolling behavior rather than for broadcast viewing. Instant-experience formats connected directly to destination booking, collapsing the distance between discovery and action. Critically, paid spend was deployed to generate algorithmic ignition rather than to deliver impressions. The goal was to earn enough early engagement signal that organic sharing would carry the campaign forward, which is exactly what happened. Paid lit the fire. Organic kept it burning.

On the influencer side, the campaign avoided the trap of centralizing its voice. Instead of a single spokesperson, it deployed influencer missions structured around distinct audience segments. Lifestyle creators on TikTok highlighted adventure and entertainment experiences, reaching audiences looking for something unexpected to do. Professional voices on LinkedIn surfaced the UAE as a destination for remote work and family travel, reaching audiences whose priorities are entirely different. The strategic logic was that diversity of influence produces diversity of reach. Trust is built through credible local voices, not through a polished corporate message broadcast at scale.

The regional dimension of the campaign revealed something that straightforward localization would have missed. English-language content was built around adventure, hidden gems, and the kind of active discovery that appeals to visitors approaching the country as travelers. Arabic-language content was built around heritage, privacy, and family, using local dialect and family-centric themes that resonated with residents and regional visitors through a completely different cultural logic. The same destination, communicated through entirely different frames. That specificity did two things simultaneously: It made the content more resonant for human audiences, and it gave AI discovery systems the clear categorical signals they need to serve content to the right people. The regional strategy wasn’t just a localization effort. It was an authority signal.

The hybrid content mechanism at the center of the campaign was a gamified digital passport system that invited visitors to earn stamps by experiencing all seven Emirates, with photography challenges and completion incentives that rewarded actual behavior rather than passive attention. This bridged digital content discovery with physical travel behavior, and it recruited participants as content creators in the process. Every visitor who shared a photograph or completed a challenge was generating authentic user content that no brand team could have produced centrally. The campaign’s AI visibility strategy depended on exactly this kind of volume: thousands of UAE residents posting under shared hashtags simultaneously created what the campaign called a Signal Storm. That mass of authentic, organic, contextually rich content fed AI discovery systems with the consistent high-volume signal that establishes topical authority at scale. Social proof of this kind cannot be manufactured. It must be engineered through genuine participation.

The outcomes validated the model. The campaign generated AED 12.5 billion in hotel revenues, attracted 5 million guests, representing a 5% increase over the prior period, and achieved an 84% nationwide hotel occupancy rate. These are behavioral outcomes, not impression counts. They are the direct result of distribution strategies built around how people actually discover, evaluate, and act on content. When distribution aligns with behavior, visibility compounds.

The Integrated Workflow

Understanding each pillar individually is necessary but insufficient. What makes DIRHAM work as a system is the way the pillars interact, and where the interaction breaks down.

Digital advertising without content relevance generates clicks that produce no signal worth amplifying. Influencer reach without genuine trust is wasted on an audience that has already learned to filter branded content. Regional specificity without hybrid participation anchors the content in place without recruiting the network to carry it further. AI visibility without structural clarity leaves authoritative content invisible to the systems that would otherwise surface it. Measurement that reports on impressions rather than behavioral change tells you what happened last quarter without informing you about what you should do this one. Each element depends on the others. Weakness in one area suppresses results across the whole system.

The workflow that holds this together operates as a continuous loop. It begins with paid signals to earn algorithmic attention, moves through influencer validation to establish human trust, anchors in local context to signal relevance to both algorithms and audiences, amplifies through participation by designing for users to become distributors, optimizes for machine readability, so AI systems can parse and cite the content, and closes with measurement of behavioral impact. That measurement then determines the budget, targeting, and creative decisions that ignite the next cycle. Measurement connects directly back to the D. The loop is continuous rather than linear, and the information flowing from the M back to the D is what makes the system improve over time.

Key Takeaways

After creating a rough draft of my updated online course on content marketing, I sent it to Bailey for his review. He quipped, “Great framework. Is it copyrighted?”

You can adopt the DIRHAM Framework with just as much confidence. Why? Because William Gibson, a speculative fiction writer, was strangely prescient when he observed, “The future has arrived – it’s just not evenly distributed yet.”

The World’s Coolest Winter campaign demonstrated four principles that hold across contexts far beyond UAE tourism.

  • Visibility is engineered. In the AI era, reach is not accidental. It is designed, and the design has to account for the three gatekeepers that now stand between content and audience. Distribution can no longer be treated as the final step in a content process. It must be the architecture around which the content is built.
  • Visibility beats volume. Strategic placement outperforms mass production. A smaller amount of content built for the specific behavioral context of each discovery system and each regional audience will consistently outperform a larger volume of generic content scattered across channels without strategic intent.
  • Trust over polish. Authentic local voices outperform corporate narration, and the gap is widening as AI content floods every platform. Human credibility is the scarcest resource in the current information environment, which means influencer strategy should be evaluated on the depth of trust the creator has built, not the size of the audience they have accumulated.
  • Measurement changes behavior. Metrics that don’t alter the decisions made in the next cycle are not measuring anything useful. The only numbers worth tracking are the ones that tell you what to do differently.

The DIRHAM model is systemic, scalable, and built to adapt as platforms and algorithms evolve, because it is grounded in human discovery behavior rather than in the specific mechanics of any particular platform. Content competes on distribution first. That has always been true to some degree, but it has never been as consequential as it is now.

More Resources:

Featured Image: Tetiana Yurchenko/Shutterstock

Ecommerce MGMT 0 Comments
Apr 8 2026
5 Content Marketing Ideas for May 2026

May 2026 offers ecommerce content marketers a mix of cultural milestones, seasonal buying moments, and fun creative hooks. The challenge is not finding topics, but producing content that stands out in search, earns visibility in feeds, and contributes to revenue.

Content marketing has long been foundational for overall visibility, but it is changing.

Marketers in 2026 should create content that is discoverable in organic search and Google Discover-like feeds, easy for genAI platforms to summarize and cite, and capable of influencing buying decisions.

With this in mind, here are five content marketing ideas to try in May 2026.

First American in Space

Photo of Alan Shepard in his space suit, inside the Mercury capsule.

Alan B. Shepard Jr. in his space suit, inside the Mercury capsule. Source: NASA.

Roughly 65 years ago, on May 5, 1961, NASA’s Mercury-Redstone 3, Freedom 7, flight marked a milestone in American innovation.

The 15 minutes, 28 seconds journey into space orbit, took astronaut Alan Shepard 116.5 miles over the Earth, making him the first American in space. The Mercury rocket topped out at 5,134 miles per hour. It was a meaningful accomplishment.

The Freedom 7 anniversary offers content marketers a chance to connect products with themes of precision, durability, and engineering.

A workwear shop, for example, might publish a timeline showing how fabrics and clothing have evolved since the early days of the space program. The shop might even add a “then vs. now” comparison that highlights how modern products outperform their historical counterparts.

The key is to move beyond a simple historical recap. Use diagrams, product breakdowns, or side-by-side comparisons to make the content useful and easy to understand.

Mother’s Day Gift Guides

Consider niche gift guides, such as pickleball how-to, for Mother’s Day.

Mother’s Day on Sunday, May 10, 2026, is the most important U.S. retail holiday in the month. The occasion typically generates more than $30 billion in retail sales, mostly for flowers, candy, and small gifts.

Ecommerce businesses can go beyond traditional gifts and promote unusual products in niche gift guides. Here are a couple of examples.

  • Pickleball mom. Upwards of 5 million American moms play pickleball each year. Consider guides to paddles, bags, court shoes, and recovery tools.
  • Homesteading mom. Millions of U.S. households are engaged in gardening, food preservation, or small-scale farming. The Mother’s Day home-gardening gift guide, anyone?

Memorial Day How-to

A guide to help shoppers plan a Memorial Day gathering can include products that make the experience easier.

Content that helps a reader or viewer complete a task or learn a skill remains one of the most powerful forms of marketing.

Consider publishing checklists for backyard events, guides to planning a cookout, or packing lists for a weekend trip. Step-by-step formats are easy to follow and easy to share. Don’t be afraid to include specific product recommendations.

National Paper Airplane Day

National Paper Airplane Day is an opportunity to add fun to content marketing.

Celebrated on May 26, 2026, National Paper Airplane Day is a minor occasion to entertain and have some fun.

A simple approach is to publish instructions for building paper airplanes. These could include tutorials, design variations, and performance tips to make the content relatively more valuable.

  • Power tool retailer: Publish plans for a fan-powered backyard paper airplane race course.
  • Stationary shop: Create a checklist for selecting aeronautical paper.
  • A STEM products seller: Provide a guide to the aerodynamics of paper airplanes.

Even a fun topic like paper airplanes can become useful content that engages shoppers and supports product discovery.

Miles Davis at 100

Photo of Miles Davis playing the trumpet

Miles Davis playing a gig in Antibes, France, in July 1963. Photo by Mallory1180.

Born on May 26, 1926, Miles Davis was a renowned American trumpet player who helped define jazz.

Content marketers promoting music, culture, or nostalgia could examine how Davis reinvented his style over time and connect that idea to various product categories. Frame apparel, audio equipment, or lifestyle goods around themes of craftsmanship and expression.

A vinyl record store could publish “The Beginner’s Guide to Miles Davis. Where to Start and What to Buy.” Or a wine and spirit retail might make “5 Cocktails Inspired by Miles Davis Albums.”

Ecommerce MGMT 0 Comments
Apr 4 2026
The 5-Pillar Framework For AI Content That Audiences Actually Trust via @sejournal, @gregjarboe

When I started updating an online course I’m teaching, I kept returning to the same uncomfortable observation: The content marketing profession has gotten remarkably good at producing content nobody wants to read.

That’s not a knock on the people doing the work. It’s a structural problem created by an industry that optimized for volume at precisely the moment audiences were becoming more discerning. AI turbo-charged the volume side of that equation, and now we’re living with the consequences. Production cycles that once took weeks compress into minutes. A single core message can spin out into thousands of personalized variants for specific micro-segments before lunch. We have the technical ability to create more content faster than ever before.

And yet consumer trust keeps falling. The gap between what we can produce and what actually connects with real people is widening, and most digital marketers are standing on the wrong side of it. More output is simply not the answer.

The argument I make in the course and the one I want to make here is this: AI changes how we work, not why audiences engage. The fundamentals of storytelling still apply. The difference is that mistakes now get amplified faster, and audiences have grown sophisticated enough to detect soulless content almost instantly.

Here’s how you can use AI strategically without sacrificing the human authenticity and cultural integrity your audiences actually respond to.

Understanding The Trust Gap Before You Touch Any Tool

Before getting into frameworks and tactics, it’s worth sitting with the problem for a moment, because the instinct in marketing is always to jump to solutions. Three distinct forces are eroding trust right now, and they’re operating simultaneously.

The first is algorithmic gatekeeping. The platforms have built increasingly sophisticated AI-driven filters, and those filters are getting better at detecting and suppressing low-quality, inauthentic content. The very tools that made it easier to produce content at scale are now being used by platform algorithms to identify and downrank that content.

The second force is what I’d call the authenticity crisis. As content volume has exploded since 2022, audience skepticism has risen in direct proportion. Consumers in 2026 can detect generic AI-generated output – what some researchers have started calling “slop.” If your content looks like an ad and reads like a press release, it gets filtered before it’s even consciously processed.

The third is plain audience sophistication. Your readers have now seen tens of thousands of pieces of AI-generated content. They know what it feels like, even if they can’t articulate exactly why. The brain is a prediction machine, and it ignores what it can easily predict.

The Framework: Five Pillars, One Sustainable Ecosystem

The approach I’ve developed in my online course organizes the challenge into five interconnected areas: AI-powered content strategy, visceral storytelling, multimodal optimization, audience psychology and analytics, and ethics and authenticity. Each pillar builds on the previous one. Getting the strategy wrong makes everything else harder. Getting the ethics wrong undermines everything else you’ve built.

Here’s how each one works in practice.

Pillar 1: Strategy First, Automation Second

Most marketers use AI reactively. They open a chat window when they need a first draft, get something plausible-sounding back, clean it up a little, and ship it. That approach treats AI as a shortcut rather than infrastructure, and it produces exactly the kind of generic, undifferentiated content that’s making the trust problem worse.

The shift I’m advocating is moving from random generation to what I call an architectural framework. The idea is that you build the strategy first – deeply, carefully, the way you always should have – and then use AI to execute it at scale. Strategy acts as the guardrail against the amplified mistakes that come with AI-accelerated production.

One analogy that’s changed how I talk about this in the course: Prompting AI is the same as briefing a junior writer. If you wouldn’t hand a new hire a one-line brief and expect a polished deliverable, you shouldn’t do it with AI either. A vague brief produces generic fluff. A structured brief with clear context, defined constraints, and specific tone guidelines produces something you can actually work with.

What belongs in a good AI brief? The specific audience segment and the pain point they’re experiencing right now. The emotional response you’re trying to trigger. The single action you want the reader to take. Brand voice guidelines with concrete examples of what “on-brand” actually sounds like. And critically, explicit guardrails about what the AI should not do – topics to avoid, phrases that feel off, cultural considerations that require human judgment.

The workflow itself matters just as much as the brief. The most effective AI content process isn’t linear; it loops. A human sets the strategy. A hybrid prompting phase generates raw material. Then – and this is the step most teams skip – a human evaluates that output against strategic goals before anything else happens. Editing comes next to inject brand voice and emotional depth. Then publishing, then learning from the data, then feeding those insights back into the next strategy cycle. Evaluation is the most overlooked stage in AI content workflows. Without a dedicated checkpoint to assess output before it moves forward, the whole process becomes a loop of mediocrity.

Pillar 2: Visceral Storytelling And Why Safe Content Is Invisible Content

When production is fully commoditized – when anyone can generate a competent first draft in 30 seconds – storytelling becomes the only real differentiator. The problem is that most organizations have spent years training themselves out of good storytelling.

Corporate content defaults toward safety, and safe content is invisible. There are three failure modes I see constantly. The first is being too rational: leading with features and specs rather than the human experience of using something. The second is being too generic: following best practices so faithfully that the brand blends into the noise of every competitor doing the same thing. The third is being too brand-centric: talking about the company rather than the customer’s identity and aspirations.

One useful model for thinking about attention is how it moves through three phases. The limbic system reacts first, almost instantaneously: “Do I care about this? Is this interesting?” Logic only engages in phase two, after emotion has granted permission. Memory encoding happens in phase three, and only for content that cleared both previous gates. You cannot argue your way into memory. Logic justifies attention that emotion has already seized.

Visceral storytelling is content that’s felt before it’s understood. It bypasses the analytical filter to create an immediate physical or emotional response. Content that achieves this shares four qualities: It’s anchored in feelings rather than facts, it evokes sensory details (sight, sound, texture), it mirrors lived reality rather than corporate ideals, and it delivers the hook immediately rather than building toward it.

Four narrative formats do this reliably. Before-and-after structures work because they visualize transformation with high satisfaction and instant comprehension. There’s a reason the format has been used in advertising for over a century. Behind-the-scenes content demystifies the process in a way that builds genuine trust, particularly with B2B audiences trying to evaluate whether a vendor actually knows what they’re doing. First-person perspective removes the brand-voice filter entirely and creates direct human-to-human connection, which is why founder stories and employee perspectives consistently outperform official announcements. And micro-stories – a complete narrative arc compressed into a short format – work because they respect the audience’s time while still providing the emotional arc that drives engagement.

Here’s a concrete example of the transformation I’m describing. A coffee shop writes this about itself: “Our coffee shop is open 24 hours and uses high-quality beans sourced globally.” That’s accurate, inoffensive, and completely forgettable. Now consider this version: “For the late-night grinders and the early risers: fuel that traveled 4,000 miles to keep you going. We’re awake when you are.” The second version identifies the customer, creates a scene, and speaks to an emotional need. It doesn’t state facts. It describes the reality of someone experiencing those facts.

Pillar 3: Multimodal Optimization And The Repurposing Fallacy

Content needs to be optimized not just for text search anymore, but for voice, visual, and video ingestion by AI agents. That’s a significant expansion of the surface area content teams are responsible for. The instinctive response is to produce more content, but that’s the wrong answer. The right answer is smarter reuse of a single asset.

One of the most common mistakes I see in content marketing is copy-pasting the same asset across channels and calling it a distribution strategy. This fails for several reasons. TikTok’s interest graph operates completely differently from LinkedIn’s social graph, so content engineered for one will typically underperform on the other. A polished corporate video feels alienating in a raw TikTok feed. And audiences have become intuitively good at detecting content that doesn’t belong on the platform they’re using – they scroll past it without really knowing why.

The strategic shift is adapting the story’s core to each platform’s native dialect, rather than syndicating the same asset everywhere. Different platforms carry different emotional intentions for users, and successful content matches the narrative to the mindset. On Instagram, users are curating identity, so content needs to be visually aspiring. On TikTok, users seek raw entertainment, and polish is actively punished while personality is rewarded. On LinkedIn, the mode is professional development – users want peer validation and actionable insight. On YouTube, users have actively chosen to spend time, making it the natural home for long-form narrative depth.

The framework I use in the course assigns every format a distinct role in the conversion funnel. Short-form video and interactive content belong at the top, grabbing attention with high velocity. Audio and long-form text sit in the middle, building the intimacy and context that move people from awareness toward consideration. Deep interactive tools and long-form video belong at the bottom, providing the detailed utility that supports a decision.

A travel campaign called “The Hyperbolist” illustrates this well. Directed by Oscar-winner Tom Hooper, the campaign targets North American long-haul travelers seeking substance over spectacle.

The campaign has a single narrative theme, luxury travel experience, which features a playful husband-and-wife dynamic: the “Hyperbolist” husband describes Dubai in sweeping, mythical terms, while the wife offers a warmer, more grounded emotional perspective. The throughline is a clever tension, acknowledging that the location sounds like an exaggeration, while insisting the reality lives up to it.

However, the campaign expresses itself entirely differently across platforms. TikTok and Reels handle discovery through fast-paced visual content. YouTube delivers planning utility through detailed itinerary guides. Instagram Carousel provides the inspirational aesthetic content that helps potential visitors imagine themselves there. The user encounters the same destination three times without experiencing the repetition fatigue that comes from seeing the same asset recycled.

Pillar 4: Measuring What Actually Matters

The most dangerous thing in content marketing right now is optimizing for the wrong metrics. Likes, impressions, and follower counts feel like success. They’re visible, they’re easy to report, and they create a satisfying sense of momentum. But they rarely guide strategic decisions because they represent visibility rather than intent.

Watch time tells you whether a narrative actually resonated. Did the audience stay for the message, or bail after five seconds? Scroll depth tells you whether the hook was efficient enough to pull people through the full piece. Repeat exposure tells you whether there’s genuine brand affinity being built or whether people are bouncing and never coming back. A user who watches 90% of a video without liking it is more valuable, behaviorally, than a user who taps the heart and scrolls on in two seconds.

SEO has largely shifted from keyword-based search intent to behavior-based retention signals. Engagement velocity (how quickly users interact after posting), completion rates, and saves and shares are the signals that trigger algorithmic amplification. High performance in behavioral metrics unlocks reach.

Translating these signals into language that resonates with leadership and clients matters too. “We got 5,000 likes” is a social media metric. “We validated brand alignment with a core demographic” is a business outcome. “The video had high watch time” is a platform stat. “We retained audience attention on a complex policy message” is a communication result. Content needs to be positioned as a business driver, not a marketing output, and that requires defining outcomes before hitting publish rather than retrofitting meaning to whatever the dashboard shows afterward.

Pillar 5: Ethics, Authenticity, And Why Trust Has Become Competitive Advantage

In an era of infinite AI-generated content, ethical transparency has shifted from a compliance question to a genuine competitive differentiator.

Three hidden costs of over-automation tend to compound each other. The first is misinformation: AI hallucinates confidently, and factual errors that get published undermine authority in ways that take a long time to repair. The second is the uncanny valley effect: Content that’s technically competent but emotionally hollow, generating disengagement because something just feels “off” about it. The third is brand erosion: When efficiency consistently overrides empathy, the brand voice gradually becomes generic and interchangeable. No single moment of damage, just a slow drift toward invisibility.

Hiding the use of AI reads as weakness to increasingly sophisticated audiences. Disclosing it clearly, with non-intrusive labeling like “AI-Assisted” or “Synthetically Generated” where appropriate, reads as strategic competence and respect for the audience’s intelligence. Transparency strengthens credibility rather than weakening it.

The governance principle I come back to most often is what I call the Human-in-the-Loop requirement. Every AI content workflow needs a human filter providing editorial oversight (fact and tone review) and cultural review (norms, values, sensitivity assessment). AI cannot be responsible for content. Only a human can take ownership of a message, and that ownership matters most precisely when something goes wrong.

A Case Study Worth Studying: The $1 Million Film

In January 2026, the 1 Billion Followers Summit Challenge in collaboration with Google, concluded with 3,500 global entries competing for a $1 million prize. Requirements stated submitted films had to be powered by at least 70% generative AI tools from Google. The winner was Zoubeir ElJlassi of Tunisia, with a short film called “Lily.”

The premise is deceptively simple. A lonely archivist discovers a doll at a hit-and-run scene. The doll gradually becomes a silent witness to a haunted conscience, and the weight of it forces a confession. The story is elemental: guilt, isolation, the impossibility of outrunning what you’ve done.

ElJlassi used Google’s Veo to generate the signature gloomy aesthetic and maintain visual consistency across the film. Google’s AI filmmaking tool Flow handled fine-tuning of individual scenes to ensure the characters moved and emoted with genuine nuance. Gemini served as a creative co-pilot for storyboarding and defining the look and feel from the start.

The judges called it a seamless blend of raw emotion and high-tech execution. What I find instructive about this outcome is what it tells us about what the tools actually did. None of them invented the story. None of them understood why a doll at a crime scene becomes unbearable to look at, or why confession is both the worst and the only option. The human brought the emotional core. The AI brought the execution capacity. That division of labor – human meaning, machine scale – is the model worth studying.

What To Do Starting Tomorrow

Four things are worth doing before you get to any of the more sophisticated changes.

Start by auditing your existing workflows to map exactly where AI is currently used and identify where there is no human checkpoint before content goes live. Most teams, when they do this exercise honestly, find gaps they didn’t realize existed.

Then add AI to your process intentionally rather than expansively. Pick the high-impact, low-risk areas first – idea generation, headline testing, first drafts for internal review – rather than deploying it across every content type simultaneously.

Implement a mandatory cultural review step for all external-facing AI content. This means a human with contextual judgment reviewing for tone, accuracy, and sensitivity before anything publishes. For teams operating across multiple markets or cultural contexts, this step is not optional.

Finally, shift your key performance indicators away from volume and reach toward sentiment and trust signals. Watch time, scroll depth, saves, and repeat visits tell a more honest story about whether content is actually working than follower counts and like rates ever did.

The Fundamental Argument

The future belongs to organizations that merge the scale of machines with the judgment of people. Not one or the other. Both, in deliberate proportion.

The technology will keep changing. The core truth won’t: meaning cannot be automated. Stories outperform statements. Specific outperforms generic. Authentic outperforms polished. By placing the human back at the center of the workflow – not as an obstacle to efficiency, but as the source of everything that makes content worth reading – you transform AI from a risk into something genuinely sustainable.

More Resources:

Featured Image: Roman Samborskyi/Shutterstock

Ecommerce MGMT 0 Comments
Mar 27 2026
Framework for Quality AI Content Marketing

AI-generated marketing content is only effective when it attracts organic traffic from search engines, LLMs, and Google Discover.

Content marketing exists to attract, engage, and retain customers. For ecommerce marketers, attraction is often the primary role.

Historically, that meant search. Articles ranked, generated visits, and fed the top of the funnel. Plain and simple.

Retention matters too, but content marketing works best when it acquires prospects.

AI Gives and Takes

The advent of ubiquitous AI is a double-edged sword for content marketers.

On the one hand, AI makes producing content cheap, at least in a utilitarian sense. But AI has also flooded the internet with relatively low-value content and changed the way consumers search.

Moreover, in 2026:

  • AI has increased the percentage of zero-click search results.
  • Many customers begin and end searches with AI chat.
  • AI-generated articles increase competition for organic traffic.
  • Feeds such as Google Discover and Perplexity Discover are traffic generators.

Consider Google’s February 2026 algorithm update, which focused on Google Discover. According to DiscoverSnoop, a Google Discover-focused research firm, several large websites lost significant Discover exposure after the rollout.

  • “The biggest loser appears to be Yahoo, which lost nearly 50% of its content, with its audience plunging by 62%.”
  • “Go.com, which was redirecting to ABCNews.com, totally disappeared from the ranking, dropping to zero, and ABCNews.com was not able to replace it.”
  • “Among mainstream publishers, the Fox franchises (News, Business, Weather) experienced a visibility drop of more than 40%.”

Algorithm updates, zero-click search results, and changes in consumer behavior create a vicious cycle of AI-generated content.

It works something like this.

As organic traffic declines across search, LLMs, and feeds, the relative cost of content rises. To offset that cost, marketers turn to AI. But more AI-generated content increases competition, worsening performance.

Competing articles from the same AI models and prompts are similar in tone and substance. It’s “AI slop” applied to content marketing.

Quality Is the Solution

A year ago, AI offered a speed or cost advantage. That advantage has largely disappeared.

The differentiator now is execution. Marketers must produce AI-assisted content that is structured, validated, and refined. In practical terms, that means improving quality.

Marketers first need to overcome a bias. We must assume AI-generated content can be at least as good as that of humans. To this end, consider a recent quiz from The New York Times comparing human-written text to an AI-generated rewrite. Thus far, roughly half of Times’ readers preferred the AI-generated versions.

Second, we need to believe that AI-assisted content can be optimized and systematized.

12-Step Framework

The way to improve AI-generated content is through better processes, not prompts.

A practical approach is to treat content generation in steps. Each adds structure, reduces risk, and improves quality. Human editors can participate at any stage. But in general, these are steps the AI can take for content automation.

A step-by-step framework can improve the quality of AI-generated content. Click image to enlarge.

  1. Idea. Pick a specific topic and goal for the article.
  1. Sources and brief. Gather strong source material and set the rules for format, tone, and style.
  1. Validate. Check the inputs. Are the sources credible?
  1. Summarize. Pull the useful material from each source. Focus on relevant facts, data, and claims.
  1. Outline. Prompt the AI to provide a clear structure for how the article opens, progresses, and ends.
  1. Draft. Prompt the AI to generate the full article from the outline and summaries.
  1. Edit. Ask the AI to critique the draft against the brief, summary, and outline.
  1. Plagiarism. This is often overlooked. Have the AI compare the draft against sources. Consider a dedicated plagiarism checker, such as Grammarly’s API.
  1. No AI-speak. Ensure the output reads naturally.
  1. Optimize. Prompt the AI to optimize the article for search engines, answer engines, and Google Discover. Consider using the Discover click-through predictor.
  1. Grade. Prompt the AI to grade the article against steps 7-10. Assign the good scores to a human reviewer.
  1. Refresh trigger. Have the AI set a review date for updates.

AI has lowered the cost of producing content. It has not lowered the standard required to compete. In fact, the opposite is true.

The marketers who win in 2026 will generate the best content, not the most.

Ecommerce MGMT 0 Comments
Mar 2 2026
5 Content Marketing Ideas for April 2026

April 2026 offers ecommerce content marketers many potential topics, from nostalgia to beer.

Content marketing is the practice of creating, publishing, and promoting articles, videos, podcasts, and similar media to attract, engage, and retain customers.

Here are five content marketing ideas your business can use in April 2026.

Apple Turns 50

Photo of one of the first Apple computers in a wooden case

An early Apple computer. Photo: Ed Uthman.

On April 1, 1976, Steve Jobs and Steve Wozniak founded Apple Computer in a garage. Fifty years later, Apple’s influence spans personal computing, mobile, and even culture. It is one of the most recognized brands in the world.

Many news outlets will publish Apple retrospectives, Steve Jobs mini-biographies, and similar pieces this April. It’s an opportunity for content marketers, too.

  • Electronics retailers could publish Apple-related listicles, for example, “10 Best Products Apple Produced — and the 5 Worst.”
  • Lifestyle or apparel brands could lean into nostalgia. A post recalling the first Mac, iPod, or iPhone may resonate with shoppers who grew up alongside those devices.
  • B2B merchants could frame Apple’s history as a case study in product innovation, branding, and ecosystem building.

National Burrito Day

Photo of two burritos wrapped in foil

From humble Mexican origins, the burrito is a staple worldwide.

National Burrito Day falls on the first Thursday in April (the 2nd this year). The pseudo-holiday celebrates the familiar and adaptable Mexican dish.

The burrito (“little donkey”) likely originated in Mexico’s Sonora or Chihuahua regions. Wheat flour tortillas made it easy to wrap beans, meat, or potatoes into a portable meal for laborers.

Those same workers carried the compact and meaty wrap with them to the United States in the early 1900s. By 1930, El Cholo Spanish Café in Los Angeles added the burrito to its menu (the first in a restaurant).

In America, the burrito continued to evolve. In 1956, then 19-year-old Duane R. Roberts invented the first frozen burrito. In 1961, another L.A. restaurant, El Faro, invented the massive, foil-wrapped “mission style” burrito. There is also the “California burrito” stuffed with carne asada, French fries, cheese, guacamole, and sour cream.

For content, the adaptable burrito fits a variety of merchants.

  • Online grocer. “Regional Guide to America’s Favorite Burritos.”
  • Meal subscription brand. “Build-Your-Own Burrito for National Burrito Day.”
  • Workwear retailer. “10 Best Job Site Burritos.”
  • Kitchenware merchant. “How to Warm, Fold, and Wrap a Burrito.”

Google Discover Experiment

Photo of a Google Discover page on a smartphone

Google Discover displays content based on user interests rather than explicit queries.

Google Discover may represent a relatively new form of site traffic. Unlike traditional search, which responds to explicit queries, Discover pushes content based on users’ interests and behavior.

It favors fresh content with strong visuals, topical authority, and user engagement. Hence, merchants may focus on being a trusted source for their niche.

In April, consider running a series of Discover optimization tests. Marketers can visit their Search Console account and download the list of Discover-referred pages (if any). Then ask an AI model such as ChatGPT or Gemini to analyze the articles. Was there a common topic? A recognizable pattern?

National Beer Day

Photos of various bugs and bottles containing beer

The popularity of beer spans styles, regions, and traditions.

National Beer Day on April 7 commemorates the Cullen-Harrison Act of 1933, which legalized the sale of beer containing 3.2% alcohol and signaled the end of Prohibition.

Since 1933, beer has become one of America’s most popular beverages.

Beyond volume, beer is an expression of identity and taste. From light lagers to high-alcohol craft IPAs, beer culture spans sports, travel, food, and lifestyle. That breadth makes National Beer Day a versatile content hook for ecommerce content marketers.

Here are some examples.

  • Fitness gear merchant. “Low-Alcohol Beers for Active Lifestyles.”
  • Men’s heritage apparel brand. “Gentleman’s Guide to Classic Beers.”
  • Luggage retailer. “Guide to Germany’s Most Iconic Beers.”
  • Outdoor equipment store. “Craft Beer Pairings for Spring Adventures.”

Each approach connects beer culture to the merchant’s audience and product set.

National Zipper Day

Photo of a zipper on an article of clothing

The zipper transformed apparel, luggage, and outdoor gear.

On April 29, 1913, American engineer Gideon Sundback patented his “Hookless Fastener No. 2” —  the first modern zipper.

The zipper may seem mundane, but it represents a significant innovation. Developed for clothing, the zipper eventually found applications in luggage, boots, tents, and more. Like many great product innovations, it solved a practical problem with elegant engineering.

Content marketers with relevant products can use National Zipper Day as an opportunity to spotlight craftsmanship and materials.

Ecommerce MGMT 0 Comments
Feb 28 2026
How a Pro Writer Uses AI

Kaleigh Moore is a 12-year freelance writer and editor. She’s contributed to Shopify, Forbes, Vogue, Adweek, and various B2B providers, among others. Like all of us, she’s now grappling with the promise and limits of AI.

She calls AI a “fire hose of information” that greatly increases efficiency. She also cautions on what it cannot do, such as interview humans or learn from experience.

She shared those views and more in our recent conversation, including her preferred AI platform, use cases for entrepreneurs, and getting started with AI-driven composition.

Our entire audio is embedded below. The transcript is edited for clarity and length.

Eric Bandholz: Give us a rundown of what you do.

Kaleigh Moore: I’m a freelance writer and editor. I’ve worked with all kinds of B2B and SaaS companies within the ecommerce ecosystem over the past 12 years.

One of the first case studies I wrote for Shopify featured Beardbrand. That was over a decade ago.

Bandholz: Does AI commoditize writing?

Moore: The emerging AI composition tools are incredible; they greatly increase efficiency, especially for the tedious parts of writing, such as ecommerce product descriptions.

But remember that AI tools operate on existing information. They are not creating something new. Human composition provides original perspectives — experiences, thoughts, and feelings.

Do we care about those perspectives? Some people care a lot. I’m a journalist first and foremost. I want to do my own homework, fact-check, and make sure I’m putting out the best of whatever my name is on.

I worry about young people and how they’ll use these tools. I’m 37 years old. I grew up in a largely pre-internet, pre-social media time. I hope we always have human experience and interaction — talk to each other, go to coffee. To me, AI is a nice supplement, but it doesn’t replace person-to-person interaction.

It’s been interesting on the hiring side of things. I occasionally look at full-time in-house writing roles. Over the last 18 months, many of those roles have shifted to require AI operational skills, to hop into an AI tool and craft something. If you are not hands-on with these tools, you won’t even get an interview. So the ability to learn the tools and be curious about them is an important skill now.

Some people say AI is just a bubble, but I don’t think so. It’s too powerful.

Bandholz: How does a writer or entrepreneur learn and apply AI?

Moore: It’s a fire hose of information every day. I approach it as a journalist. A key skill is developing very strong prompts. The more advanced we are at prompting, the better the output.

Beyond that, accept a willingness to learn the new functionalities. It can be intimidating, what with all the new tools.

Anthropic’s Claude is my go-to platform. Claude’s outputs are very good. Anthropic’s entire stance is open-source and transparent. The company prioritizes ethical concerns and data privacy. For me as a writer, Claude is the best. It’s also a great place to start.

Generative AI platforms such as Claude can help entrepreneurs and marketers with promotional emails, social media posts, and LinkedIn articles, among other applications. The platforms will remember a voice and style from existing content.

Bandholz: How do you train AI in that way?

Moore: I’ve been doing it for my own work. I feed Claude good, strong examples of my published articles, case studies, and guides to provide points of reference. It’s akin to informing a new hire, say a junior writer or copywriter.

The aim supplying a lot of very specific guidelines. Lots of dos and don’ts. Use this word; don’t use this one. Input it once, and the AI never forgets, unlike humans. And I can update it over time, which is essential. Although more examples are not always better. AI can get confused by too much information.

A user’s top 10 tweets would be a good, limited data set to start with, plus general instructions on likes and dislikes. Teach AI in the same way you would a human.

Say a merchant wanted to publish a blog post. I would enter a full brief, such as the targeted keyword, the audience, brand names to avoid, and data sources to cite. There’s quite a bit of heavy lifting involved just getting that prompt ready.

Certainly, the merchant could give it a paragraph and request a 500-word blog post on X for this audience. She would get a pretty good output.

But give it the full brief, and she will likely receive a much better result, requiring little editing or tweaking. It’s often a choice of spending time editing the output versus preparing the brief.

For me, editing AI text usually comes down to my writing preferences, though much of it is fact-checking. AI hallucinates; it makes stuff up. It will cite data that doesn’t exist.

Moreover, AI cannot interview or speak with an expert. We have to integrate those afterward.

Bandholz: Where can people follow you and reach out?

Moore: My site is KaleighMoore.com. I’m on X and LinkedIn.

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Feb 13 2026
AI Content Licensing for Merchants

Recent efforts by Microsoft and Amazon to develop content-licensing marketplaces for artificial intelligence models could represent an opportunity for ecommerce marketers.

A leaked Amazon Web Services slide presentation and Microsoft’s February announcement of its Publisher Content Marketplace both aim to solve the AI licensing problem.

REI is an excellent example of ecommerce content marketing.

AI Content

Large language models need content. They train on it and self-evaluate against it.

Yet those AI-driven interfaces increasingly answer questions without sending users to the content source. Google’s AI Overviews makes this obvious to many businesses in the form of dwindling search traffic.

Many publishers are alarmed, having built their businesses on audience reach, page views, and advertising impressions.

When AI systems summarize articles instead of referring readers, the economic model fractures. News organizations, media companies, and independent creators argue that AI platforms derive value from their work but don’t pay.

Some large publishers have made license deals, but the problem remains.

Microsoft’s Publisher Content Marketplace is one path toward a solution. The program allows publishers to license content for AI use through a centralized system that emphasizes usage-based compensation and reporting transparency.

Rather than relying exclusively on separate agreements, publishers can theoretically expose their work to multiple AI buyers while maintaining defined licensing terms.

Amazon’s reported initiative appears conceptually similar. Publishers could sell or license content to AI developers. While unconfirmed, the effort signals a broader industry shift toward formalized access to AI content rather than unstructured scraping.

Economics

These and similar marketplaces could reshape how value flows between content producers and AI builders.

For publishers, a marketplace implies more predictable compensation and greater control. For AI developers, it offers a defensible content supply chain that reduces legal uncertainty. In principle, marketplaces reduce friction by normalizing pricing, usage measurement, and participation mechanics.

Content Marketing

While the licensing debate centers on publishers, ecommerce marketers should closely watch, too.

For years, some retailers have produced publisher-like content to attract, engage, and retain shoppers. Buying guides, tutorials, recipes, and project libraries increasingly sit alongside product catalogs.

Prominent examples include:

Much of ecommerce content marketing operates on the principle of reciprocity. Retailers provide useful information, and consumers reward it with trust, attention, and eventual purchases. The strategy does not depend solely on immediate transactions. It builds long-term preference and brand affinity, similar to that of publishers.

In fact, not too long ago, publishers complained that some forms of content marketing represented direct competition.

Content Traits

The distinction between the types of ecommerce marketing content is worth noting.

The first is promoting products. Content marketers and search engine optimizers work hand in glove to expose products. AI has made this more difficult.

Product feeds are a potential solution. The feeds would originate from ecommerce platforms such as Shopify or marketplaces like Walmart, which have direct relationships with AI businesses.

The second type is publisher-style and reciprocity-driven. These are the articles, videos, and podcasts to attract shoppers. It is distinct from product-focused and has at least three aims.

  • Relationships first. Reciprocity-based content creates value independent of short-term purchases. It’s a back door to ecommerce sales and builds customer relationships. REI’s educational posts and videos help outdoor enthusiasts develop skills, whether or not a transaction occurs immediately.
  • Brand affinity and trust. In the same way publishers seek authority, content marketers instill confidence. For example, Williams Sonoma’s recipe and entertaining collections position the retailer as an authority in cooking and hospitality. Shoppers engage with the brand through expertise, not only merchandise.
  • Audience development, wherein the marketer is akin to a media company, with content that drives search engine rankings, repeat visits, email subscribers, and consumer preferences. Rockler operates as a niche publisher with its learning center that cultivates repeat visits and sustained engagement.

Content Opportunity

When they produce publisher-style content, marketers gain access to publisher-oriented tools, including emerging AI content marketplaces.

Yet the motivation differs. Publishers seek licensing revenue, while merchants seek discovery and visibility. Thus content-license marketplaces are a potential ecommerce opportunity to expose a brand’s products and expertise across AI-driven interfaces.

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