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The DIRHAM framework redefines distribution strategy, showing how paid signals, influencers, and AI visibility work together to drive real outcomes.
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.
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 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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Featured Image: Tetiana Yurchenko/Shutterstock
Greg Jarboe is president of SEO-PR, which he co-founded with Jamie O’Donnell in 2003. Their digital marketing agency has won …
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