Pipeline is harder to build than it was two years ago. Most senior marketing leaders are reporting inbound pipeline down 10% or more compared to prior cycles - even as intent signals and engagement remain strong.

The familiar plays aren't working. Broad intent activation, increased ad spend, content syndication at volume - none of it is producing pipeline at the pace the business needs. That's not a failure of execution. It's a signal that the underlying model needs to change.

The issue isn't a lack of activity. It's that most programs are converting fewer of the right accounts than they should. And the reason is simple: intent data answers one question: is this account researching your category?

It doesn't answer the question that actually determines conversion: does this account have the structural conditions to buy this cycle?

That gap - between accounts that are active and accounts that are actually positioned to convert - is what this eBook is about.

DemandScience's 2026 research across 750 senior marketing leaders puts numbers on the problem: 87% of organizations are chasing intent signals that don't convert, only 26% of intent signals ever reach qualified pipeline, and 25% of marketing budget is being wasted on non-converting spend.

Inside this eBook, you'll find out why intent data alone is no longer enough, the four signals that separate active accounts from truly winnable ones, why many organizations are over-invested in low-conversion activity, and how leading teams are balancing broad demand programs with high-conversion, in-market motions.

You'll also see why some organizations are generating 3-4x more pipeline from the same investment, and what modern pipeline efficiency actually looks like in practice.

Download the eBook to get the full framework, the scenario model, and the Fortune 500 case study showing $475M in pipeline generated in six months.

Liza Adams, AI Advisor and Go-to-Market Strategist, has worked with dozens of organizations on AI transformation. Her assessment of where the difficulty lives is direct: 

"The transformation was less about AI, and this was actually more about the human beings. AI is the easiest part of the transformation. The hardest part of transformation is actually us, human beings."

Most CMOs spend the majority of their AI planning time on selecting and implementing tools. Which platforms to evaluate? Which workflows to automate? Which use cases to pilot first? That's necessary. It's also not where transformations fail.

They fail because:

• Teams are oversubscribed and have no capacity to experiment. 
• The job security conversation was never had honestly.
• "Learn AI" became the 16th priority on already-overwhelmed lists. 
• Psychological safety for failure was never established.
• The champions who were quietly experimenting were never identified and amplified.

The technology is solvable. Organizations that have invested seriously in the organizational side of AI adoption are consistently ahead of those that treated it as a software implementation problem.

The job security conversation CMOs are avoiding

52.4% of marketing leaders are still in the testing and pilot phase of AI adoption, and 44.1% believe AI will lead to job losses. That near-majority concern is present in every marketing department right now, regardless of whether it's being spoken aloud.

CMOs who avoid the conversation don't neutralize the anxiety. Instead, they allow it to calcify into passive resistance, underinvestment in learning, and quiet sabotage of the AI initiatives they're trying to build.

Christy Marble, CMO at Siteimprove, describes handling this honestly with a team member: "Just a few months ago, one of our brand strategists came to me and she said, Christy, am I going to be doing less editing now? And she seemed a little worried. And my answer, I think, was kinda shocking to her. My answer was yes, and that's the point."

The answer was yes. And then she reframed it with specificity.

"Our content demands had grown so much with this content tsunami, with everyone producing content, and what we were getting was really bad. She was no longer doing brand strategy at all. She was now our brand police and editor. And the work was routine. It was frustrating. It was unrewarding work for her." 

AI taking over the tedious editing work was a path back to the strategic work that had made the role worth having in the first place.

"Her value wasn't in watching all of our content messaging for brand consistency. Her value is not in checking for typos. It was in the human creativity, the strategic thinking, the emotional connection, and the tone that differentiated our brand."

This is an honest conversation that acknowledges that the role will change. It makes a specific case for why the change is better for this person, grounded in what they were doing versus what they were good at.

CMOs who want their teams to engage seriously with AI need to have these conversations. Not a town hall about the exciting future of AI. One-on-ones about what specifically will change for specific people, and what the path forward looks like.

Building AI literacy as an organizational capability, not a specialist function

The instinct in many organizations is to build an AI Centre of Excellence: a specialist team that handles AI, reduces the burden on the broader marketing function, and produces outputs for others to use.

That model tends to create a dependency rather than a capability. When the AI specialists are overloaded, the rest of the team is blocked. When they leave, the capability walks out with them. And it keeps the majority of the marketing organization AI-illiterate at exactly the moment when AI literacy is becoming a basic professional expectation.

Monica Kumar, CMO at Extreme Networks, is explicit about the direction: "We now need AI literacy across the entire organization. This notion of AI specialists is gone. It's more about AI literacy and fluency in the organization."

Chris Hood, AI strategist and former Google marketing leader, identifies a practical blocker: "One of the biggest challenges we see across all industries right now is that there is a mix of understanding of the language of AI." 

His solution is unglamorous and highly effective: "Come up with a shared dictionary glossary of terms for your organization to help you all get on the same page in terms of when we say agent, we mean this." 

Without shared vocabulary, teams talk past each other. Marketing means one thing by "AI-powered personalization" while IT means another. The shared glossary is a first step that takes less than a week and removes a friction point that compounds indefinitely if ignored.

Naveen Blazey, CMO at Wipro Americas, describes how a 250,000-person organization approached universal training: "Everybody has that base layer. Everybody's on common ground zero. And depending on your role, your interest areas, you are trained with certain programs and courses." Universal baseline, then role-specific depth. The baseline removes the common vocabulary problem. The role-specific layer produces people who can act.

Deepak Kumar, CMO at Techies Infotech, has connected learning directly to accountability: "We embedded learning into performance expectations. Upskilling is now directly tied to team KPIs. Every marketing team member is expected to demonstrate improvement in how they use AI-assisted workflows." When AI fluency is a performance expectation rather than a voluntary development activity, the adoption curve accelerates.

Creating space to actually experiment

Here's the structural problem in most marketing organizations: AI adoption is treated as an addition to already-full workloads rather than a redesign of how work gets done.

Liza Adams puts the number on it: "Many of us are 120 percent to 150 percent oversubscribed, and so are our teams." When teams are operating at that capacity, "learn AI" becomes the 16th priority. It gets deferred indefinitely. And CMOs wonder why adoption is stalling despite genuine enthusiasm for the technology.

The solution is to reprioritize.

Thiago Monteiro, Founder of Toco Marketing, describes the cultural conditions that actually work:

"My approach to upskilling is centred on removing fear and making experimentation normal. AI can feel intimidating at first, so I focus on creating a safe environment where people are encouraged to try tools, make mistakes, and learn through doing. To keep momentum, I make AI a standing agenda item. In every weekly meeting, we have a dedicated section to discuss what we have been doing with AI, what has worked, and what has not."

The standing agenda item does several things at once. It:

• Normalizes the conversation, 
• Creates peer accountability without individual pressure, 
• Surfaces what's working across the team faster than top-down reporting would, and 
• Signals to the team that experimentation is expected, not optional.

Liza Adams adds: "You might have a Slack channel where people share what worked, what didn't work, and what their experiments look like. When we succeed, we win, and when we fail, we learn."

The critical reframe in that last phrase: failure is a contribution to organizational knowledge, not a career risk. Organizations that can genuinely establish that norm move faster through the learning curve than those where failure carries stigma.

Identifying and amplifying champions

Francesco Federico, Global CMO at S&P Global, offers one of the most practical pieces of advice for building adoption momentum: "I'm sure you already have your teams filled with AI enthusiasts that are trying AI in their spare time. They can be transformed from enthusiasts to evangelists and really become those agents for change."

Those people exist in almost every marketing organization. They are the individuals who proactively develop custom AI workflows, utilize emerging tools to drive efficiency, and experiment out of genuine curiosity. Rather than requiring formal training, these innovators need an organizational platform to amplify their impact.

Identifying them takes a brief survey or a few conversations. Amplifying them means giving them time to share what they've learned in team meetings, asking them to run internal workshops, and featuring their results in the organization's AI reporting. The peer-to-peer learning that follows is more credible and more adopted than any mandate from leadership.

Francesco's recommendation extends to how wins get communicated upward:

"Celebrate successes. Celebrate successful pilots, celebrate a successful conversational chatbot, or an increase in conversion rates. These are all things that make a big difference in maintaining enthusiasm throughout the implementation process."

Internal communication about AI progress should be as deliberate as external communication about any other strategic initiative.

What full transformation actually looks like

A case with Dice.com provides the clearest benchmark for what high-velocity AI transformation produces when the organizational conditions are right. Liza Adams worked with the team for over 6 months to build a 45-member organization of 25 humans and 20 AI teammates, growing to 63 AI teammates by April and closing in on 100 by summer.

The results were measurable: 75% faster content creation, 98% lead qualification accuracy, 35% improved campaign performance. But Liza is clear about where the actual work was: "The transformation was less about AI. The team spent more time on change management than on GPT configuration." One-on-ones to understand individual fears. Dedicated experimentation spaces. A deliberate decision to reprioritize workloads rather than stack AI learning on top of existing demands.

The marketing team eventually became the internal template for other departments. They started training sales teams, then customer success, then other functions across the organization. The capability they built, knowing how to design, train, and manage AI teammates, became institutional knowledge that competitors couldn't easily access by purchasing the same tools.

That's the output of getting the human transformation right. The efficiency gains were real and significant. The competitive advantage came from the organizational capability itself.

The CMO's actual job in this transformation

Katherine Lehman, Founder and CMO, describes what's changed at the structural level:

"AI has fundamentally changed the speed-to-execution equation. What used to take weeks now takes days. The real shift is not just efficiency gains, it is the collapse of the traditional marketing org chart. Companies no longer need a content writer, a data analyst, and a reporting specialist as three separate hires. One smart marketer with the right AI stack can do what a team of five did two years ago."

This situation represents both a massive opportunity and a significant disruption. Effective CMOs recognize that success relies as much on thoughtful organizational design as it does on choosing the right technology. 

By initiating honest discussions about how roles are evolving, leaders cultivate a culture where experimentation is safe and productive. They further strengthen this by empowering internal champions, treating human transformation as their primary focus, and positioning technology as the essential foundation that supports those efforts. 

Ultimately, the organization’s ability to harness these tools effectively becomes its true competitive advantage.

Marketing in regulated industries isn’t about moving fast and breaking things. It’s about moving deliberately, building strategies that drive growth while holding up under scrutiny from compliance and regulatory stakeholders.

If you’ve worked in finance, healthcare, or legal, you’ll recognize the tension straight away. You’re expected to deliver results, but every campaign operates within a framework of rules and approvals.

Once you're operating inside a regulated environment, one truth becomes hard to ignore:

You’re not just marketing to customers; you’re communicating within systems designed to protect them.

When regulation stops being theory

One of the earliest moments that shaped how I approach regulated marketing came while working in education on UK government-funded campaigns.

At The Skills Network, I managed teams working on initiatives aligned with Department for Education (DfE) guidelines. I particularly remember the Skills for Life campaign. On the surface, it looked like any other campaign:

• Clear audience targeting
• Strong messaging
• Defined outcomes

But once we received the DfE guidance, everything tightened. Messaging and assets had to align precisely with:

• Funding eligibility criteria
• Government-approved terminology
• Policy-backed claims
• Skills-for-life logo placement

Even something like “improve your career prospects” had to be carefully considered. If it implied an outcome that couldn’t be guaranteed under the program, it had to be reworked.

There wasn’t a legal team rewriting the copy. Instead, cross-functional compliance and regulatory stakeholders interpreted the rules and ensured messaging and visuals aligned with them.

At the time, it felt restrictive. However, looking back, it forced a level of discipline that most marketing teams never develop: clarity over creativity and accuracy over assumption.

The people who actually shape your campaigns

One of the biggest misconceptions in regulated marketing is who you’re really working with. If you’re currently in a regulated marketing role, you will know that it’s rarely legal teams directly.

Across education, legal services, and non-profit work, I’ve consistently worked with:

• Compliance managers
• Regulatory or governance stakeholders
• Risk teams
• C-suite stakeholders

These roles sit between legal frameworks and marketing execution. They translate rules into reality:

• What you can say
• What needs evidence
• What crosses the line

Here’s what I’ve learned: if you bring them in late, they will slow you down. If you bring them in early, they will help shape better campaigns.

Legal marketing: Where language carries risk

When I stepped into the role of Head of Marketing at a solicitors firm, it brought a different level of challenge. Because in legal marketing, the risks aren’t abstract; they’re immediate.

You can’t:

• Promise outcomes
• Overstate expertise
• Suggest certainty where none exists

Even small wording choices matter. For example, phrases like “We guarantee results” and “You will win your case” aren’t just risky; they’re non-compliant.

So marketing becomes an exercise in precision.

Instead of bold claims, you have to focus on:

• Demonstrating expertise through content
• Using case-based examples carefully
• Building credibility through consistency

This is where marketers may struggle. They become so cautious that their messaging loses impact and becomes all fluff.

The challenge isn’t to say less. It’s to say things better: more clearly and with more intent.

What non-profit marketing teaches you about trust

Working with several NGOs introduced me to another layer of complexity; this time primarily driven by resource constraints rather than regulation.

With budgets tight, paid media isn’t always viable. So, growth had to come from:

• Organic content
• PR and storytelling
• Strong use of owned channels

There was no room for wasted messaging. Everything had to resonate. Interestingly, this environment reinforced the same principle that regulated industries demand: trust-first marketing.

Because when you can’t rely on aggressive tactics or budget, you rely on credibility. That’s what drives engagement.

When marketing in regulated industries goes wrong

When regulated marketing fails, the consequences go beyond performance metrics. The following real-world examples highlight this clearly.

Financial services and crypto promotions

Regulators like the Financial Conduct Authority (FCA) have taken action against misleading promotions, especially in crypto.

Some campaigns have come under fire for using influencers without proper disclosure, highlighting returns without explaining risk, or creating urgency around high-risk investments. 

The issue wasn’t just messaging. It was that compliance wasn’t fully embedded in the process.

Legal sector missteps

Legal firms have also faced scrutiny for misleading claims about success rates, suggesting guaranteed outcomes, or using testimonials that imply certainty.

Often, these weren’t intentional violations, but in regulated industries, interpretation matters more than intent.

Education sector scrutiny

Education marketing has faced challenges around overstated career outcomes, misleading course benefits, or lack of clarity around funding eligibility. This is something I’ve experienced directly.

Even small inaccuracies can impact trust – and in some cases, access to funding or opportunities.

The pattern behind these failures

Across industries, the same issue appears: marketing moved ahead of compliance, not alongside it.

The processes aren’t aligned enough. That’s where risk enters the ecosystem.

What actually works in regulated marketing

From experience across education, legal, and non-profit sectors, five principles consistently deliver:

1. Bring compliance into the process early: Not at the approval stage, at the planning stage. This turns compliance into a collaborator – not a bottleneck.
2. Focus on clarity over persuasion: Overly persuasive messaging rarely survives review. Apply clear, accurate communication.
3. Build around owned and earned channels: When restrictions limit activity, your owned channels become essential. These include content, email, organic social, and PR. This approach proved especially effective in non-profit work, where constraints forced smarter strategies.
4. Learn how to simplify complex information: Whether it’s legal language, funding criteria, or policy, your role is to make it understandable in layman's terms, without distorting it. This is one of the most valuable skills in regulated marketing.
5. Measure trust, not just conversions: Conversion metrics matter. But in regulated industries, long-term growth depends on credibility, engagement quality, and audience trust.

Why regulated marketers are better positioned for what’s next

As search evolves, particularly with AI-driven summaries, there’s a shift toward:

• Clear answers
• Structured information
• Credible sources

This creates an advantage because these are exactly the qualities regulated marketing already demands.

What once felt like a limitation is now fast becoming a strength.

What this means for CMOs and heads of marketing

Leading marketing in a regulated industry isn’t just about growth; it’s about building systems that support performance, compliance, and trust.

From education campaigns shaped by government frameworks…To legal marketing where every word carries risk…To non-profit environments where trust drives everything…

You don’t win in regulated marketing by pushing boundaries blindly. 

You win by understanding exactly where those boundaries are, and building something credible and sustainable within them.

The brands that succeed are always the ones people trust.

When marketing and sales aren't aligned, leads fall through the cracks, fingers get pointed, and revenue suffers. This framework gives you a clear, structured foundation for getting both teams on the same page, from how leads are qualified and handed off to how performance is tracked and accountability is maintained.

What is a marketing-sales SLA framework?

A marketing-sales SLA (service-level agreement) framework is a formal but practical document that defines the rules of engagement between your marketing and sales teams. It sets out exactly how leads are qualified, how and when they're handed off to sales, what response times are expected, and how both teams feed information back to each other to keep improving.

Think of it as the operating agreement that keeps both teams pulling in the same direction, with shared definitions, clear ownership, and a process for resolving issues when things don't go to plan.

Who is it for?

This framework is for senior marketing leaders (CMOs, VPs of Marketing, and Heads of Demand Generation) who are responsible for the relationship between marketing output and sales performance. It's also highly relevant for revenue operations and marketing operations professionals who own or oversee the lead management process.

You'll find it particularly useful if you're scaling your demand generation function, dealing with friction between your marketing and sales teams, implementing new lead scoring or automation tools, or trying to get a clearer picture of what's happening at the top of your funnel.

How to use the framework

The framework is organized into seven sections, covering everything from lead qualification criteria through to escalation paths. Here's how to get the most out of it:

• Start with the MQL criteria: The lead qualification section is the foundation of everything else. Make sure your definition of a marketing-qualified lead reflects your actual ICP before you fill in anything else.
• Customize it to your org: The framework comes with example criteria and response times, but these are starting points, not prescriptions. Update every section to reflect your team structure, tooling, and sales motion.
• Align with sales before you finalize it: An SLA only works if both sides have bought in. Share a draft with your sales leadership early and build in time to get their input on the handoff process and response expectations.
• Use the reporting section to drive accountability: The metrics and review cadences outlined in section six aren't just for tracking;  they're how you catch problems early and demonstrate marketing's contribution to pipeline.
• Revisit it regularly: Treat this as a living document. As your team scales, your ICP evolves, or your tech stack changes, your SLA should too.

Done well, a marketing-sales SLA builds the kind of trust between teams that makes hitting revenue targets a whole lot more achievable.

Get your marketing-sales SLA framework

Nobody knows exactly what the next decade will bring. That’s one certainty. Anyone who tells you otherwise is either selling something or hasn't spent enough time in the trenches to understand how fast things are moving in the marketing world. 

But here’s what I do know as a CMO: things are moving faster than most boardrooms are comfortable admitting, and the brands that will own their categories in 2036 are making foundational decisions right now. 

Ten years ago, a brand was a logo, a tagline, and a media budget. Five years ago, it became a content strategy. Today, it’s a conversation. By 2036, it will be a living, AI-orchestrated presence that exists simultaneously inside data centers, across social channels, in the personal reputations of your people, and in the cognitive habits of every prospect who has ever touched your company.

What follows is my honest attempt to provide you with an educated guess at what the next decade holds, not as a futurist, but as a practitioner who has to make real decisions in the present using signals already visible today.

The death of the monolithic corporate brand

The idea that a company's brand lives primarily in its visual identity, its advertising, or its official communications channels is already on life support. What’s replacing it is both more human and more technological: a distributed brand ecosystem where the organization itself is only one element, and increasingly not the most trusted one.

By 2036, brand trust will be radically decentralized. Buyers will default to the signal they find hardest to fake: real human beings who genuinely know something and are visibly associated with a company. This is not a prediction; it’s already the direction of travel. 

A recent Refine Labs data study found that personal LinkedIn profile content often generates five times more engagement than company page content. Bain & Company found that founder-led companies outperformed the S&P 500 by more than two times over the past decade. 

The writing is on the wall.

By 2036, your company's most valuable brand asset will not be trademarked. It will be the person on stage, in the podcast, in the LinkedIn feed, in the long-form YouTube video, the human whose reputation has become indistinguishable from your organization's promise.

What changes dramatically over the next decade is the infrastructure available to build and amplify those human brands at scale. The AI systems that will make hyper-personalized brand experiences achievable for companies of any size are already being built.

Leaders as living brand assets: The rise of personal brand capital

People follow people. I know that sounds simple, almost too simple for a piece about AI and the future of branding. But I keep coming back to it because it’s the one truth that all the technological change of the next decade will amplify rather than replace. 

The brands that will win are the ones where real human beings – leaders, founders, subject matter experts – are visibly and consistently connecting with buyers. We already see it happening.

Karen Chalmers, Vice President of Marketing & Partnerships at interVal, a SaaS platform that gives accounting firms, financial institutions, and wealth advisors automated insights for SMB clients, framed the shift succinctly: 

“Humans instinctively trust other humans more than monolithic brands, and now the algorithms reward people more than brands too.” 

She argues the strongest organizations will not treat brand as a centralized corporate function, but as something distributed across employees, executives, and subject matter experts alike.

“People buy from people they trust. Even in B2B, it’s still person to person.”

Marc Benioff: The power of founder-led growth at Salesforce

And in many businesses, the leader is the brand. Take Marc Benioff at Salesforce. To many investors, Salesforce simply is Benioff. As one J.P. Morgan analyst put it: 

"Marc can't live separately from Salesforce. This is his identity." 

How does the industry account for Benioff’s brand equity? His personal brand, built on values-first leadership, stakeholder capitalism, and a willingness to take public positions most CEOs avoid, became the company's brand and a meaningful driver of its ecosystem. That ecosystem of businesses, consultants, and app developers was projected to generate $1.6 trillion in new revenue between 2020 and 2026. 

More recently, Benioff turned a routine quarterly earnings call into an influencer-style livestream from the top of the Salesforce Tower: broadcast-quality microphone, YouTube stream, customer interviews. 

A Fortune cover story noted that 92% of professionals say they are more likely to trust a company whose senior leaders are active on social media. Benioff has understood that longer than most. He’s both the proof of concept and, it should be said, the cautionary tale. When his public persona missteps, the brand feels it immediately. That two-sided risk is precisely what makes a leader brand so powerful and so consequential.

Drew Arciuolo and VKTRY: When personal brand drives acquisition

Then there’s Drew Arciuolo at VKTRY, a sports performance company known for its patented carbon-fiber performance insoles used by athletes at every level. The example that follows is closer to the ground and, for many of us running marketing functions at growing companies, more instructive, more grassroots, and more real, as it’s a true example of how brands break through.

Drew is VP of Marketing at VKTRY. When he joined the family business full time after graduating as a Division I baseball player in 2018, VKTRY was still relatively unknown. The company had a differentiated product and strong technology, but limited visibility. 

Drew built his own visibility alongside the brand. He spent 115 days a year on the road capturing authentic athlete reactions on video, building a content engine that eventually generated millions of organic impressions per day. He also narrowed the company's focus from a broad athletic audience to highly engaged performance niches, helping the brand build a loyal following through direct-to-consumer channels and athlete-driven storytelling.

The strategy worked. In May 2026, VKTRY was acquired by Scholl’s Wellness Company, parent company of Dr. Scholl’s, in a deal that positioned the brand as a high-growth leader in the athletic performance category. The acquiring company cited VKTRY’s “viral e-commerce business,” “hyper-loyal customer base,” and authentic connection with athletes as key drivers behind the acquisition.

Drew himself became a recognized voice in the DTC marketing community, speaking at industry events and appearing on podcasts that drove awareness of both his own brand and VKTRY's. His personal brand and the company brand grew together, each amplifying the other. 

You don’t need to be a billion-dollar CEO for this to work. You need to show up, build genuine trust with a real audience, and let the compounding do the rest.

Why personal brand capital is the next frontier for CMOs

These examples are not outliers. They’re early signals of a structural shift. By 2036, the most strategically important question in a CMO's annual plan will not be "What is our creative platform?" It will be: "Whose personal brand is our growth engine, and how do we build the infrastructure around it?"

We’re entering the age of what I call personal brand capital: the measurable brand equity that individual executives and thought leaders accumulate through their public presence, and its direct correlation to company revenue. This is not a vanity exercise. Within a decade, private equity firms, acquirers, and public market investors will have models that price personal brand capital as a distinct asset class on the balance sheet.

The mechanics of building leadership brands will look very different by 2036. AI will function as a permanent partner in the process. Not replacing the human voice, but dramatically lowering the effort required to maintain consistent public presence. 

A CEO who today publishes two articles a quarter will be able to sustain a weekly cadence without sacrificing quality, because AI handles the structural labor while the human brings the genuine insight and editorial judgment. The human stays in the driver's seat. AI handles the engine.

Beyond content production, AI will handle what I think of as the signal layer: monitoring which content themes are gaining traction with specific audience segments, which speaking formats are converting for different buyer personas, which relationships in a leader's professional network are warming or cooling. Those insights surface in real time so a leader's public presence stays calibrated to market opportunity rather than running on instinct alone.

AI-powered hyper-personalization: When the brand knows you

Let me be direct about this one, because it is the shift I’m most confident in: hyper-personalization will be the norm. Not a differentiator. Not a premium capability. The baseline expectation.

And I want to say something I don’t hear discussed enough in CMO conversations: your CRM stopped being a list of names about five years ago. Most organizations have not fully absorbed that yet, but it’s true. 

With the emergence of AI, your CRM has become the absolute brain of your company, an intelligence engine with near-endless capacity to help you understand every step of the buying process in ways we simply never had available before. 

The prospect journey, the client relationship, the renewal conversation, the upsell opportunity – your CRM now holds a living, learning map of all of it. Companies that still treat it as a contact database are leaving extraordinary competitive advantage on the table.

This reframe matters because it is the foundation for what hyper-personalization actually means in practice. It means a complete dismantling of the assumption-based marketing that has defined our profession for decades. 

We’ve always built personas: semi-fictional composites constructed from surveys, focus groups, and whatever behavioral data we could stitch together. The truth is that most personas are educated guesses dressed up in PowerPoint. By 2036, with AI and data genuinely converging, we’ll no longer guess. We’ll know.

From personas to precision: What the research tells us

McKinsey's landmark Next in Personalization study found that companies growing faster drive 40% more of their revenue from personalization than their slower-growing counterparts, and that personalization consistently produces 10 to 15% revenue lift, reaching as high as 25% for the most capable organizations. 

BCG and Harvard Business School's David Edelman put a precise number on the opportunity in their 2024 book Personalized: Customer Strategy in the Age of AI. A $2 trillion prize is available to companies that get this right. 

A BCG survey of 23,000 global consumers found that more than 80 percent want personalized experiences, yet two-thirds have received personalization that felt inappropriate, inaccurate, or invasive. That gap between expectation and execution is the opportunity.

Gartner took it further in early 2026, predicting that 60% of brands will use agentic AI to deliver streamlined one-to-one interactions by 2028. Their senior researcher put it plainly: 

"This marks the end of channel-based marketing as we know it." 

Writing in Harvard Business Review, HBS professors Julian De Freitas and Elie Ofek made the case that AI's role in brand management goes well beyond automation. It fundamentally changes what brand management can know and do, enabling a level of individual insight that no human team could previously achieve at scale. The implication is a transformation in the quality of a brand's understanding of the people it serves.

"In 2036, your brand will not have one voice. It will have a million, each one calibrated to the individual receiving it, in the channel they prefer, at the moment they are most receptive, with the message most relevant to where they actually are in their journey."

The style guides, tone of voice documents, and message hierarchies that CMOs have spent careers perfecting won’t disappear, but they will function differently. Rather than prescribing a single voice that speaks to everyone, they’ll establish the values envelope within which AI-generated personalized communications must operate. Brand governance becomes about protecting authenticity at the edges, not enforcing uniformity at the center.

Four priorities that separate the CMOs of 2036 from the rest

And here is what makes this personally urgent for me right now. As a CMO, I’m dead focused on figuring out exactly this: how to leverage and scale AI in ways that are operationally grounded and commercially meaningful. 

Four things keep me up at night and get me out of bed in the morning:

1. Understand our clients' pain points like never before: Not the pain points we assume they have. Not the ones they mentioned in last year's discovery call. The ones they are living with right now, that they may not yet have language for. AI gives us the first genuinely powerful tools to get there.
2. Personalize our value to them: Not a personalized subject line. Not a mail merge. Genuine personalization of the value proposition itself, calibrated to what this particular client actually cares about at this particular moment in their journey.
3. Message our differentiating factors better: Most companies, including very good ones, are not communicating what actually makes them different. They are communicating what they think sounds impressive, which is not the same thing. AI helps us get closer to the truth of our differentiation and find language that makes buyers feel it.
4. Do all of it as meaningfully and credibly as possible: Metrics are secondary to me right now. I know that is not what most CFOs want to hear. But if we build the systems, the intelligence infrastructure, the personalization capability, the human brand platforms, the metrics will follow. Building for the next decade is the priority. The numbers will catch up.

That orientation, systems before scorecards, is what I think separates CMOs who will lead their organizations into 2036 from those who will be explaining why last quarter's campaign underperformed.

Why strategic implementation matters now

Forward-thinking CMOs should begin building personal brand programs now – not as a social media initiative, but as a revenue infrastructure investment. The companies that systematize this in 2026 and 2027 will have a three-to-five year compounding advantage over those who wait for the market to force their hand.

Steve Keifer, Chief Marketing Officer of Ordway, a finance automation platform for recurring revenue billing and revenue recognition, believes the deeper issue is not just personalization, but declining institutional trust itself. 

“Buyers are educated on marketing tactics. They know online reviews, search results, and analyst reports can be manipulated or subsidized.” 

As a result, buyers increasingly trust peers more than polished corporate messaging, turning instead to networking events, private Slack communities, Zoom conversations, and customer advocacy networks for recommendations. Keifer argues these authentic, word-of-mouth ecosystems, what many marketers now call dark social, will only grow more influential in the age of AI.

He’s right. The more synthetic and automated the information environment becomes, the more valuable trusted human recommendation becomes. AI may scale distribution, but trust will remain stubbornly human.

The brands that will win the next decade are not those that personalize most aggressively, but those that personalize most trustworthily. As AI capabilities expand, consumer sensitivity to manipulation will sharpen in parallel. The organizations that use data to genuinely serve rather than extract will build durable loyalty. The others will be filtered out.

AI influencers: The question no one wants to ask directly

It would be intellectually dishonest to write a piece about branding in 2036 without addressing AI influencers directly. They already exist. They’re already generating revenue. By 2036, they will be more sophisticated than anything we can currently imagine.

The trajectory is clear. AI-generated personas, some fully synthetic, some digital twins of real executives or creators, will be viable brand ambassadors for certain product categories and certain audience segments. They offer availability, consistency, cost efficiency, and unlimited scalability. A fully AI brand ambassador never has a scandal, never goes off-message, never demands a renegotiated contract.

And yet I would argue that the rise of AI influencers will ultimately increase the premium on demonstrably human, authentic brand voices. As synthetic content floods every channel, the emotional value of genuine human presence and genuine human vulnerability will increase sharply. Authenticity will become scarce. Scarcity creates premium.

The most sophisticated brands of 2036 will use AI influencers and synthetic content strategically – for reach, for efficiency, for 24/7 presence in markets where speed matters more than depth. They’ll simultaneously invest heavily in the human voices that create irreplaceable trust and category authority. The brands that go all-synthetic will discover, in due course, that they have optimized themselves into a commodity.

The disclosure imperative is not optional. By 2036, the regulatory environment around AI-generated brand content will have matured significantly. Brands that try to pass synthetic influencers or AI-generated testimonials as human will face legal exposure and the far more damaging reputational consequence of getting caught. Transparency is both the ethical and the commercially smart choice.

The channel landscape in 2036: Branding in proliferating media

What I tell my team is this: hang in there. I know things are moving fast. That can feel overwhelming when you’re trying to hit quarterly targets and simultaneously figure out what your channel strategy looks like in three years. 

But here’s what I genuinely believe: if we structure ourselves operationally to build our own AI-leveraged ecosystem, customized to our business, our clients, and our specific market, we’ll reap benefits that teams chasing individual channel metrics never will. The companies that will own their categories in 2036 are the ones building that infrastructure now, not the ones optimizing last quarter's numbers.

My message is not to panic about which channels are rising or falling; it’s to stop being so focused on today's metrics and start positioning for the future. Because here is what that future looks like: when you’ve built the right AI-connected systems, the data and intelligence flowing through them will give you more control over those metrics than you have ever had – not less. 

The irony is that the path to better measurement runs directly through the work that feels hardest to justify in a dashboard today.

Stop planning for channels. Start building infrastructure.

The channel landscape itself will keep fragmenting at a pace none of us can fully plan for. Brand touchpoints will include environments that don’t yet have stable names. AI-mediated conversations where an intelligent assistant recommends your company to a prospect who never ran a conventional search. Spatial brand experiences in mixed reality environments. Generative audio and video formats where brand content is assembled in real time rather than distributed as fixed assets. 

Consider what we’ve already lived through: TikTok did not exist in a meaningful form fifteen years ago. Podcasting as a mainstream brand channel is barely a decade old. Now apply that velocity to the next ten years and try to plan for specific channels. You can’t. What you can do is build the kind of AI-connected marketing infrastructure that finds your audience wherever they are, without you having to guess in advance where that will be.

Harvard Business Review's March 2026 piece "Preparing Your Brand for Agentic AI" put a striking number on how fast this is already happening: two-thirds of Gen Z and more than half of Millennials have already started using large language models to research products. Brands that have not thought carefully about what their company looks like inside an AI recommendation are already behind. 

As the authors found when analyzing how leading AI models represented major liquor brands, incomplete or incorrect AI data can lead to serious misrepresentation without a brand even knowing it is happening.

From SEO to GEO: why your brand's AI presence is already falling behind

The most consequential channel shift to plan for is one already underway but not yet fully appreciated by most brand leaders: the rise of AI as the primary discovery and recommendation layer. 

As large language models become the first point of contact for an increasing share of research and purchase journeys, the question "what does our brand look like on Google?" gets supplemented by an equally important one: “what does our brand look like to an AI recommending solutions to our target buyer?” 

Search engine optimization has an AI-native equivalent, call it generative engine optimization, and the brands that understand this in 2026 will have a structural advantage that compounds for years.

The implication is not that brands must be everywhere – that path leads to diffusion and incoherence. It means brand architecture must become modular and principle-based. If your brand can only be expressed through the formats and channels you planned for, it will be left behind by every new surface that emerges. 

The brands that thrive in 2036 will be those whose identity is so clearly defined at the values level that it translates coherently across formats that did not exist when the guidelines were written.

The organizational shift: Brand as infrastructure, not as an intangible

This is the one I’m most personally invested in, because it gets at something that has frustrated me for the better part of a career: the perpetual struggle to prove the value of brand investment in the language that CFOs and boards actually speak.

Marketing departments will become more scientific. Full stop. The ability to custom-build systems, ingest proprietary knowledge, pinpoint data at the individual account level, and connect brand activity to revenue outcomes will fundamentally change what it means to run a marketing organization. 

What has historically been treated as an intangible asset on the balance sheet, something you feel in the room but cannot quite put a number on, will become quantifiably tied to growth metrics in ways that have simply not been technologically possible before now.

The ROI question that marketers have been asked to answer for decades, and have often had to answer with proxies, attribution models full of assumptions, and a certain amount of hand-waving, will get genuinely easier to address. Not because the question becomes simpler, but because the data infrastructure to answer it honestly will finally exist. 

AI systems that can connect the dots between a prospect's first brand touchpoint and their eventual purchase decision, across a complex multi-channel journey that unfolds over months, will give CMOs a level of evidentiary confidence that changes the boardroom conversation entirely.

The CMO of 2036 will not be asking for budget based on brand awareness scores and share of voice. They will be showing a direct line from brand investment to pipeline, revenue, and long-term customer value, with the data to back every claim.

In practical terms, the systems, data pipelines, AI tools, and governance frameworks that produce and maintain brand experience will be as fundamental to the company's operations as its technology stack or its financial reporting systems. 

Every customer-facing team – sales, customer success, product, HR – will have brand tooling embedded in their workflows. The CMO's most important conversations will be with the CTO and the CFO, not about budget allocation but about data architecture and measurement strategy.

The brands that will define their categories in 2036 are being built today. Not in creative reviews, but in data strategy meetings, AI capability roadmaps, and decisions about which humans to invest in and build platforms around. The window is open. It will not stay open indefinitely.

Nobody has the full picture of what 2036 looks like. But from where I sit, the direction of travel is clear, and the decisions being made right now will determine who leads and who follows when we get there.

The foundations are being built today.

Hyper-personalization. Human-led brand presence. Scientific marketing measurement. Channel intelligence that replaces guesswork with precision. These are not distant ambitions. They’re the investments that separate category leaders from the rest. The next decade will reward those who start now.

Taste is the one thing that sets great content, great creative, and great execution apart from everything else right now. 

Thanks to AI, anyone can produce more content, faster and cheaper than ever before. The only real moat left is whether what you put out into the world is actually any good.

In this article, I'll share everything you need to know to use AI without sacrificing quality. Here’s what we’ll cover:

• Why "AI-generated" isn't the real definition of slop (and what actually is)
• How the content marketer's role is shifting
• What a modern AI-integrated content team can realistically achieve
• Why taste is now your most valuable marketing asset
• What CMOs specifically need to do to lead this change

The AI slop problem

By most measurements, we now have as much AI-generated content online as human-written content, and that's after only three years of tools like ChatGPT.

Google and LinkedIn have both started responding. In May, Google rolled out a major core update. Meanwhile, LinkedIn announced its own crackdown on AI slop. You can already see the effect on the LinkedIn feed: fewer of the same recycled templates making it to the front. If you're on the "create and post, create and post" treadmill, this is going to shake things up.

So, what is slop, really? Most people would say slop is AI-generated content. That's the easy answer, and it's mostly wrong.

There's never been a shortage of bad content on the internet. I've seen very good teams produce very bad blogs. They analyze the top five ranking articles on Google, write something nearly identical, add no extra value, and hope to rank. That's also slop. It just happens to be human-made slop.

I'll go further and say something that might rub people the wrong way: AI writes better than most humans. 

It can’t write better than good writers, who spend years honing their craft. However, a lot of people had never seriously picked up a pen before AI showed up, and now they’re churning out content like nobody’s business. The AI writes more cleanly than they ever could. Yes, it uses a lot of em dashes because it was trained on academic writing, books, and formal publications. But it reads well, technically speaking.

Slop isn't created by AI. Slop is created by humans who publish carelessly. They open ChatGPT, type "write me a post about X", paste the output into LinkedIn, and hit post. That's slop – not because AI made it, but because no thought, no strategy, and no taste went into it.

Think of a calculator. If you punch in two plus two and hit equals, you get four. If you punch in two plus divide two and hit equals, you get an error. The calculator works fine, but the human input was bad. Large language models work the same way. Poop in, poop out. Gold in, gold out.

The deeper problem is that most people are outsourcing their thinking to LLMs, but LLMs don't think. They predict the next best word in a sentence based on probability. That's it. The earliest version of this most of us encountered was Gmail's autocomplete, finishing your sentences in ways that were almost always slightly wrong. That's still essentially what's happening, just with vastly more context.

The "bring your own tools" problem

There's another factor making the slop crisis worse. In a LinkedIn poll I ran recently, around 70% of people admitted to using personal AI tools for company work.

This is why slop spreads inside organizations. Leadership keeps saying, "You have AI now, you should be faster," without giving their teams proper tools or guardrails. So, people open a personal ChatGPT, crank out the work, and publish. 

What content teams used to look like

Back in 2023, which is only three years ago but feels like a different era, a typical content department looked something like this:

• An SEO person (could be a freelancer or in-house)
• A content director setting strategy 
• An editor (hopefully!)
• An intern or junior content writer looking after social media (founder-led marketing wasn't really a thing yet)
• A few designers
• Maybe a PR person
• A handful of writers
• A webmaster locked in a never-ending battle between WordPress and their own sanity

A team like that would put out roughly one blog per writer per week. Five writers, five or six blogs a week (sometimes less if you were tackling a long skyscraper piece).

The biggest problem with that team was brand consistency. Getting five to ten human beings to write in the same tone is nearly impossible. You can have the best brand bible in the world, and humans will still drift. 

AI is more consistent than humans. That's part of why we can spot slop so easily, because the patterns repeat: Lists of three, em dashes, "it’s not X – it’s Y". LLMs are pattern machines. If you tell them not to use an em dash, you're essentially asking them to fight their own training.

So, if AI writes more consistently than humans, and arguably better than the median human writer, the real question becomes how you put it to work properly.

What my content department looks like now

If your pipeline is mostly built on form fills, it's probably more fragile than your dashboard is letting on.

I've worked with enough B2B marketing teams to recognize the pattern. On paper, leads are coming in. MQL targets are being hit. Cost per lead is within range. But something feels off, and when you dig into pipeline contribution, conversion rates, or just have an honest conversation with sales, the cracks start to show.

Here's the thing: that's rarely a channel problem. It's a strategy problem.

And at the heart of it is a distinction that's still widely misunderstood. The difference between demand generation and lead generation.

Let's define lead gen and demand gen properly

I'll keep this simple, because the confusion usually starts with people using the terms interchangeably.

Lead generation captures demand that already exists. Buyers are in-market, actively searching and comparing. Your job is conversion: forms, landing pages, high-intent offers.

Demand generation works earlier. It's about helping buyers understand their problem before they're ready to speak to anyone. It's about building genuine trust, shaping how people think, and being useful long before there's a deal to be done.

The analogy I keep coming back to: lead generation is harvesting, demand generation is farming.

Both matter, but most teams are heavily over-optimized for harvesting, and barely invest in actually growing the crop.

How the lead gen vs demand gen imbalance shows up (and yes, I've seen all of these)

Lead volume looks fine. Pipeline quality doesn't.

In my current role leading demand generation and go-to-market at Jisc, a £150M+ revenue environment, we went through this exact reckoning.

We were generating consistent lead volume through gated content and paid campaigns. Everything looked healthy from a reporting standpoint. But when we looked at what was actually converting into qualified opportunities, the picture was messier. Sales were flagging that conversations weren't landing well. Buyers weren't ready.

We hadn't done enough work earlier in the journey to shape understanding before the point of capture. Once we shifted focus toward earlier-stage demand creation, more ungated content, stronger problem framing, content built to educate rather than convert, the lead volume actually dropped. But the quality improved. Conversations got better. Pipeline followed.

Less volume, better outcomes. That's a hard sell to a board used to tracking MQLs, but it's the right call.

Cost per lead keeps climbing with no proportional return.

When you rely heavily on paid acquisition, you're essentially competing with every other vendor for the same narrow pool of in-market buyers. We saw this during planning cycles where increasing spend just made leads more expensive, not more plentiful or more valuable.

The shift came when we started investing more in demand creation alongside capture. Engaging a broader audience earlier meant we weren't so dependent on expensive high-intent channels. You can't harvest your way out of a rising CPL problem. You have to expand the pool, not just chase it harder.

Sales stops trusting marketing.

This one's subtle, but it's a real warning sign. It shows up as slower follow-up on marketing leads, lower prioritization, and sales reps increasingly relying on self-sourced pipeline.

It's rarely about effort. It's about trust, or the lack of it. And it almost always traces back to marketing optimizing for volume while sales cares about quality.

The turning point, in my experience, comes when both teams stop talking about leads and start talking about pipeline. Shared metrics, shared definition of a good opportunity. That's when things start to repair.

Everything valuable sits behind a gate.

Earlier in my career, I leaned hard into gated content. It generates contacts, yes. But it also limits reach and actively reduces the trust-building you need to do upstream.

The buyers I want to reach are self-educating. They want to make their own decisions before they ever speak to sales. If every useful piece of content requires a form, you're not building a relationship with them. You're just annoying them.

Shifting to more open, genuinely useful content made a real difference. More engagement, better-informed leads when they did convert.

What a more balanced demand strategy looks like

I think about building a balanced strategy as a simple four-part system.

Step 1: Create demand

This is where you build awareness and trust with people who aren't in-market yet. 

At Jisc, that's meant investing in sector-specific insights, content that addresses how buyers actually think about their problems rather than how we'd like them to, thought leadership, organic social, and events. The goal isn't immediate conversion. It's to be genuinely useful early, so that when someone enters an active buying phase, the relationship is already there.

Step 2: Capture demand

Once buyers move into consideration mode, this is where your search-driven content, paid search, and high-intent landing pages earn their keep. These tactics work dramatically better when you've done the upstream work. You're not fighting for attention from scratch; they already know who you are.

Step 3: Convert demand

Conversion is less about clever tactics and more about alignment between marketing and sales. Clearer positioning, shared context, faster follow-up. When both teams are working from the same picture, this part tends to improve on its own.

Step 4: Expand demand

Don't sleep on this one. Customer advocacy, referrals, and community often outperform paid channels on both efficiency and trust. Your existing customers are frequently your strongest demand generation asset, and also the most underused.

The implications for CMOs

If you're serious about building a better balance between lead gen and demand gen, three things need to change.

Budget allocation needs honest scrutiny. 

Most B2B marketing budgets are still skewed heavily toward capture. The teams seeing the strongest pipeline performance are rebalancing, investing more in earlier-stage demand creation to build something more durable. You cannot harvest what you haven't planted.

Your team structure probably needs to evolve. 

Demand generation requires different capabilities from campaign execution: content and storytelling, audience development, organic and community-led growth. In practice, it means thinking more like a media company than a demand capture engine. That's a cultural shift as much as a structural one.

Metrics need to reflect revenue, not activity. 

MQLs are easy to track. They're also a poor proxy for marketing's actual impact. Pipeline generated and influenced, conversion rates, sales velocity, and engagement from target accounts tell a far more honest story.

Bringing it together

Demand generation and lead generation aren't competing with each other. You need both.

However, they're not equal in impact, and they're not interchangeable. Lead generation captures existing demand. Demand generation creates future demand, builds trust, shapes buyer preference, and does the slow work that makes everything downstream easier.

Teams that over-index on lead generation end up on a treadmill: chasing short-term results, watching costs rise, delivering inconsistent pipeline.

Teams that invest seriously in demand creation build momentum. They show up earlier in the buyer journey. They convert more effectively. They build pipeline that's actually predictable.

The shift isn't really tactical. It's a mindset change from asking "how do we capture more demand?" to "how do we create it in the first place?"

One final thought

The companies with the strongest pipelines right now aren't just better at generating leads. They've done the work so that when a buyer is finally ready to act, choosing them already feels like the obvious decision.

That doesn't happen by accident. And it definitely doesn't happen from a form fill alone.

As a CMO, it’s up to you to prove exactly how marketing contributes to the bottom line.

That's where things can get a little complicated because buyers don't always follow a straightforward path to purchase. Some might go through social media or search, podcasts, referrals, and private communities, etc., before they ever convert, and a lot of those touchpoints are impossible to track fully.

But try explaining that in a board meeting, and you’ll still get the same question: “So what’s our ROI?”

Key learning points

• Most CMOs still rely on first-touch (36.1%) and last-touch (31.2%) attribution (despite modern journeys being multi-touch and non-linear).
• Data integration (61%) and accurate data collection (53.7%) remain the biggest barriers to proving marketing ROI.
• Paid search (53%) and SEO (42.3%) are still perceived as the highest-ROI channels.
• The strongest measurement approach combines CRM data (used by 66.7% of marketers), attribution tools, sales feedback, and modelling.
• CMOs don’t need enough evidence to connect marketing activity to business outcomes.

The problem with proving marketing’s ROI

Thanks to complicated attribution, tightening privacy rules, and complex buyer journeys, many CMOs are finding it tough to connect the dots between marketing activities and revenue with total confidence.

Our Future of Marketing 2025 Report highlights this perfectly. When we asked marketers about their biggest attribution roadblocks, 61% cited data integration across channels, and 53.7% said accurate data collection was the main issue. 

Rebecca Fowkes, Marketing Director at VenturEd Solutions UK, explains:

"Marketing attribution is getting more complex than ever. With customers more mature in their product understanding and research, we experience many more touchpoints than before.

With so many data points in the customer journey, UTM tracking just won't cut it for measurement and attribution. We need to leverage AI tools that help us understand the attribution and optimize based on that insight."

The key point is this: you don’t need perfect attribution to justify investment. You just need enough evidence to show marketing’s contribution to growth and revenue.

Why traditional attribution isn’t enough

Proving marketing's impact is becoming more difficult, and attribution is a big part of the reason why.

Our research revealed that first-touch attribution remains the clear favorite, with 36.1% of marketers relying on it to measure campaign success. Last-touch attribution follows closely at 31.2%.

These models remain popular because they’re simple. They’re easy to implement, easy to explain, and they give stakeholders a clear answer when they ask where a conversion came from. 

But they only show part of the picture.

A first-touch model credits the very first interaction. A last-touch model credits the final one. Everything in between disappears. The webinar. The case study. The conversations with colleagues. The months of LinkedIn content engagement before sales even gets involved.

When only one touchpoint gets credit, the reality of the buyer journey is lost.

This becomes even more complicated when you look at how performance is evaluated across channels. Marketers still rank paid search (53%) and SEO (42.3%) as their highest ROI channels. On paper, that makes sense. They’re highly trackable, conversion-focused, and easy to tie to revenue.  

However, it creates a subtle problem. The easiest channels to measure tend to get the most credit, while harder-to-track influence is undervalued.

As Flora Wolfer, CMO at Payplug, explains:

"Multi-touch marketing is our new priority in tech B2B marketing. However, that increases pressure for a more efficient attribution workflow, and that is a true challenge today."

The reality is that many buying decisions are influenced by interactions that never appear in attribution reports at all.

Enter dark social. Recommendations shared through WhatsApp, LinkedIn DMs, private communities, and word-of-mouth often shape decisions long before a tracked click ever happens. 

As Chetan Baregar, Senior Director of Marketing at Recykal.com, puts it:

"In B2B, attribution is no longer a clean science. Dark social like WhatsApp forwards, internal Slack convos, and LinkedIn DMs are where product recommendations really happen, but they leave no trace in analytics. Similarly, niche communities like invite-only groups where your TG shares unfiltered opinions are influencing decisions long before they hit your website.

“To get closer to the truth, we’re layering CRM data with last-touch attribution, self-reported sources (‘How did you hear about us?’), and direct intel from sales. It’s not perfect, but it’s a much more honest view of today’s B2B journey."

This is why more advanced attribution models (such as linear and algorithmic attribution) are gaining traction. Even so, our research shows they’re still relatively untapped. Only 14.8% of marketers currently use linear attribution, and just 14.6% use algorithmic models.

We’re not saying you need to overhaul your entire model overnight. However, it’s worth considering that traditional attribution may not be enough on its own.

That’s why leading CMOs aren’t chasing a perfect system. They’re building a broader measurement framework that brings multiple signals together to show marketing’s real impact.

Build a measurement framework finance can trust

If traditional attribution models only tell part of the story, what should you rely on instead? 

The answer is a better measurement framework.

We know visibility is the biggest hurdle right now. In fact, 73.4% of marketers say privacy regulations have already complicated their measurement strategies.

With cookies disappearing and journeys becoming more fragmented, trying to track everything perfectly is no longer realistic. The goal shifts from precision to confidence.

For most CMOs, the CRM sits at the centre. It connects marketing activity directly to revenue and pipeline, which is why 66.7% of marketers use it for attribution.

But on its own, it’s not enough. Strong measurement comes from combining CRM insight with other signals, like:

• Attribution reporting
• Sales feedback and direct intel
• Customer surveys
• Self-reported attribution
• Marketing mix modeling
• Incrementality testing

Rossana R. Rodgers, Chief Marketing Officer at Authena AG, explains how her team approaches this:

"We're integrating API-based tracking, CRM syncing, and unique identifiers (like scan-based tag triggers) to connect offline actions with digital journeys. In B2B, attribution is complex, but solving it is where the real ROI is uncovered."

Don't ignore the non-quantitative stuff, either. Sometimes the most valuable insight comes from a simple "How did you hear about us?" or a quick chat with sales about what prospects are actually saying.

The metrics CFOs actually care about

One of the biggest mistakes marketing leaders make when justifying investment is how they talk about it.

Marketers and finance teams often look at the same results through very different lenses. We’re more focused on metrics like website traffic, click-through rates, impressions, engagement, and lead volume. Those metrics are great for figuring out if a campaign is working, but they don't answer the questions the board is asking.

Your CFO probably isn't losing sleep over engagement rates. They’re more concerned about things like revenue growth, profitability, and customer acquisition costs (CAC).

Our Future of Marketing Report found that leads generated (63.4%) and conversion rates (62.2%) are the go-to metrics for measuring marketing ROI. But if you want to defend your budget, the conversation has to go deeper.

Currently, only 46.3% of marketers track revenue generated, and just 41.5% measure CAC. Yet, these are the exact metrics that speak the boardroom's language.

This creates a serious disconnect. Even top-performing channels like paid search and SEO are often judged using narrow measurement frameworks. The best way forward is to rely less on channel performance and try to pivot the conversation to business outcomes.

Finance wants to know:

• Is marketing generating revenue?
• Are we acquiring customers efficiently?
• Is our investment driving profitable growth?

Our advice is not to rely solely on channel stats, but to lead with business impact.

Instead of saying:

"Organic traffic increased by 35%."

They say:

"Organic traffic increased by 35%, contributing to a 22% increase in qualified leads and a 12% reduction in CAC."

That shift in framing is what turns marketing from a reporting function into a growth driver. Because when stakeholders can clearly see that connection, budget conversations become significantly easier.

Defending brand investment 

If you've ever tried to justify brand spend in a budget meeting, you know it's a completely different beast than pitching performance marketing.

With paid channels, it's easy because you just point to leads, pipeline, and revenue. But brand building? Not so much. Its impact takes time and shapes how people perceive and trust your business, which is difficult to measure on a dashboard.

Because of this, companies naturally gravitate toward what they can track. When budgets get tight, it's tempting to dump all your money into short-term, highly measurable campaigns. However, much of what makes performance marketing effective in the first place is the brand equity you built long before a prospect ever clicked an ad. 

Jelle Boeser, Head of Brand and Digital Marketing at Royal Canin, captures this perfectly:

"It’s easy to measure the 40. That’s why it gets the budget. But the 60 (the long-term brand building) only shows up in modeling and market share. If you don’t protect it, you’ll feel it is a year too late."

Performance metrics can create a dangerous illusion that only short-term tactics drive growth. But if you starve your brand, you’ll eventually face skyrocketing acquisition costs, weaker market positioning, and a complete reliance on paid channels just to keep the lights on.

Instead of hunting for direct attribution, focus on indicators that reflect long-term health and future demand:

• Share of search & branded search volume
• Brand lift
• Direct traffic growth
• Customer retention & repeat purchase rates
• Market share

Individually, no single metric tells the whole story. But layered together, they prove whether your brand spend is actively setting the stage for future growth.

The trick is to stop judging brand and performance by the same yardstick. The most effective CMOs build entirely separate measurement frameworks for short-term wins and long-term brand building, so both get the funding they deserve.

How leading CMOs approach attribution

Today’s best CMOs are practical. They know no single dashboard can perfectly map out a messy, modern customer journey, so they’re layering different approaches to see the bigger picture.

One massive shift is the move toward AI. With buyer journeys getting more fragmented and data piling up, AI helps spot the hidden patterns and connections we could never find manually.

Rebecca Fowkes, Marketing Director at VenturEd Solutions UK, believes this is non-negotiable:

"Marketing attribution is getting more complex than ever. With customers more mature in their product understanding and research, we experience many more touchpoints than before.

With so many data points in the customer journey, UTM tracking just won't cut it for measurement and attribution. We need to leverage AI tools that help us understand the attribution and optimize based on that insight."

At the same time, tightening privacy rules and invisible touchpoints are bringing Marketing Mix Modeling (MMM) back into the spotlight.

As Burak Yedek, Fractional CMO, explains:

"Measurement has become more and more difficult due to channel variety. Marketing mix modeling is becoming more crucial plus more efficient with machine learning."

Marketing teams shouldn’t rely on a single lane but blend attribution software, CRM data, direct customer feedback, and modeling to build a balanced, realistic view of performance.

The industry is finally moving past the impossible goal of assigning 100% of the credit to a single touchpoint. At the end of the day, your stakeholders don't really care if a webinar, a LinkedIn post, or an email sealed the deal. They just need to know that your marketing spend is driving real business growth.

Once you focus on proving that, you have a business case no one can argue with.

A five-step framework for justifying marketing investment

By now, one thing should be clear: securing marketing investment isn't about proving every touchpoint contributed to a conversion. It's about building a credible case for how marketing drives business outcomes.

While every organization is different, the strongest CMOs tend to follow a consistent approach.

1. Start with the business objective

Before you talk about channels or campaigns, get clear on what the business is actually trying to achieve. Is it revenue growth? Market expansion? Retention? Cost efficiency? 

When marketing is tied directly to business priorities, the conversation changes immediately.

2. Focus on commercial outcomes, not marketing activity

It’s easy to lead with impressions, clicks, or traffic. But those don’t carry weight in the boardroom.

Instead, connect marketing to outcomes like:

• Revenue growth
• Pipeline generation
• Customer acquisition cost (CAC)
• Customer retention
• Customer lifetime value (CLTV)
• Market share

This is where marketing starts to look like a growth driver and not a reporting function.

3. Use multiple sources of evidence

As we’ve hammered home by now, traditional attribution simply doesn't tell the whole story anymore. That’s exactly why top-tier CMOs pull their data from a mix of different places to build their case:

• Attribution platforms
• CRM systems
• Sales feedback
• Customer surveys
• Marketing mix modelling
• Brand tracking

When you have multiple, distinct sources all pointing to the same conclusion, your confidence goes up (and so does the board's confidence in you).

4. Measure both short-term and long-term impact

Not every marketing activity should be judged the same way. Performance campaigns can show results quickly, but building a brand takes longer to show up in revenue data. So, it’s better to measure both short-term and long-term impact individually. 

5. Tell a business story

Data is important, but data alone rarely secures a budget. Stakeholders need context. They need to understand what happened, why it happened, and what it means for the business moving forward. Rather than reading off a list of metrics, try to connect those numbers to strategic company goals and explain exactly how marketing is moving the needle. 

By telling a business story (and not a marketing one), you show them how your work actively supports revenue, profitability, and long-term success. 

Stop chasing perfect attribution

Every budget conversation eventually comes back to the same question:

What is marketing actually delivering?

With customer journeys spreading across multiple channels and privacy restrictions tightening, answering that with total precision isn’t realistic.

But that’s no longer the point.

Katherine Lehman, Fractional CMO at ReturnBear & Founder at KT Creativity, puts it perfectly:

"Perfect attribution is a myth. Smart marketers triangulate insights from multiple sources, then use story and signal to drive decisions. The numbers don't always speak for themselves, especially when influence is everywhere."

The best marketing leaders have stopped chasing the ghost of perfect data. Instead, they’re building undeniable confidence by layering multiple sources of insight and tying their team's work directly to business outcomes.

You don’t need to be in the office 40 hours a week to be the most impactful leader. Fractional CMOs deliver results, not just face time.

In fact, fractional CMO roles are becoming increasingly popular, as they allow companies to bring in senior marketing leaders without the cost of hiring a full-time executive.

It’s a win-win: orgs get high-level expertise without the hefty wage, and CMOs get all the flexibility they want. But, despite the benefits, the day-to-day of a fractional CMO is not without its hurdles.

If you’re thinking of becoming a fractional CMO, this is for you.

What is a fractional CMO?

Simply put, a fractional CMO is a chief marketing officer who works with a company on a part-time, contract, or project basis rather than as a full-time employee.

Instead of bringing someone in-house permanently, businesses hire a fractional CMO when they need senior marketing expertise but don't necessarily need it five days a week. This is especially common among start-ups and growing companies that want experienced marketing leadership without the cost of a full-time executive.

For example, a company might hire a fractional CMO to help launch a new product, build a marketing strategy, or support a period of growth. Once those goals have been achieved, they can either continue the relationship or move on.

It's a setup that's becoming increasingly popular. Companies get access to experienced marketing leaders when they need them, and marketers get the flexibility to work across different businesses, industries, and challenges.

How much does a fractional CMO cost?

One of the biggest reasons companies hire a fractional CMO is cost.

Hiring a full-time CMO is a major investment. Depending on the company, industry, and location, a full-time CMO can earn anywhere from around $150,000 to more than $570,000 per year in base salary alone, before bonuses, equity, and benefits are considered.

A fractional CMO, on the other hand, gives businesses access to senior marketing leadership without committing to a full-time executive hire.

While rates vary, most fractional CMOs charge somewhere between $200 and $350 per hour. Others work on a monthly retainer, which can range from around $10,000 to $25,000 per month depending on the scope of work, company size, and level of involvement required.

That's why fractional CMO roles have become so popular with startups and growing businesses. They get experienced marketing leadership when they need it, without taking on the cost of a full-time executive.

Fractional CMO vs. full-time CMO vs. marketing agency

If you're considering a fractional CMO role, it's useful to understand where it sits compared to other marketing leadership options.

A full-time CMO, a fractional CMO, and a marketing agency can all help a business grow, but they solve very different problems. The right choice usually comes down to budget, business stage, and how much strategic leadership the company needs.

A marketing agency focuses on execution, while a full-time CMO provides dedicated leadership.

A fractional CMO sits between the two, offering strategic expertise without the cost of a full-time executive. That's one of the reasons fractional CMO roles have become increasingly popular.

Fractional CMO roles and responsibilities

No two fractional CMO roles look the same. One company might bring you in to build a marketing strategy from scratch, while another may need help scaling an existing team or launching a new product. That said, there are a few responsibilities that come up time and time again.

As a fractional CMO, you might be responsible for:

Strategy

• Building and refining marketing strategies
• Creating go-to-market plans
• Improving brand positioning and messaging
• Identifying new growth opportunities

Leadership

• Mentoring marketing teams
• Supporting hiring and team development
• Aligning marketing with wider business goals
• Working closely with leadership teams and stakeholders

Growth

• Improving lead generation and demand generation efforts
• Finding ways to increase revenue and market share
• Helping businesses scale their marketing function

Performance

• Setting marketing goals and KPIs
• Managing budgets and resources
• Tracking performance and reporting on results

In some cases, you may also be brought in for a specific project, such as a rebrand, product launch, market expansion, or period of rapid growth.

How to hire a fractional CMO

If you're considering bringing in a fractional CMO, the process is usually much quicker and more flexible than hiring a full-time executive.

While every company will approach it differently, the process often looks something like this:

Step 1: Identify your goals

Before you start your search, be clear on what you need help with. For example, are you looking to generate more leads, launch a new product, improve your marketing strategy, or build out your team? Having clear objectives will make it much easier to find the right fit.

Step 2: Find potential candidates

Once you know what you're looking for, start building a shortlist of fractional CMOs. Referrals, professional networks, LinkedIn, and industry communities can all be great places to find experienced marketing leaders.

Step 3: Assess experience and fit

Not every fractional CMO will be right for your business. Take time to review their experience, previous results, industry knowledge, and leadership style. It's also important to make sure they can work effectively with your existing team.

Step 4: Agree on the scope of work

Before getting started, agree on expectations, responsibilities, timelines, and success metrics. Having a clear statement of work helps both sides stay aligned and avoids confusion later on.

Step 5: Onboard and set priorities

Once the engagement begins, give your fractional CMO access to the people, tools, and information they need to succeed. The faster they understand the business, the faster they can start making an impact.

Step 6: Review progress regularly

Fractional CMOs are hired to deliver results, so regular check-ins are important. Review performance against agreed goals, discuss challenges, and make adjustments where needed to keep things moving in the right direction.

Benefits of fractional CMO roles

Fractional CMOs are on the rise. A study by Chief Outsiders found that “attitudes are becoming more positive towards fractional CMOs”, with 73% of people surveyed saying that fractional CMOs are becoming more accepted by the C-suite.

In addition, research also showed that 9% of startup founders and small to medium-sized businesses are currently working with or planning to work with a fractional CMO.

But what are the benefits of being a part-time CMO?

1. Better work-life balance

Because you work on a part-time or contract basis, you have the freedom to design your schedule, from choosing your clients to setting your working hours.

Not only that, but you can even take extended breaks between contracts if you so wish since you’re not tied to a specific company.

2. Higher earning potential

Unlike full-time CMOs who earn a fixed salary, fractional CMOs set their own rates and often work with several companies at the same time. This means you get a more diversified (and higher) income.

3. Exposure to diverse industries 

One of the best things about being a fractional CMO is the variety of work. You’re not locked into a single industry or company but are working across different sectors and helping businesses at different stages of growth.

Many people who thrive in this type of role do so because they enjoy the variety, which helps keep things fresh and avoid stagnation.

4. Career growth

Because fractional CMOs connect and work with multiple executives and founders, they have access to many opportunities, partnerships, and potential future clients. Not to mention that you’re growing your reputation by having a vast network of clients.

This means being a fractional CMO can help accelerate your professional growth.

5. Less internal politics

Some full-time CMOs may get bogged down in company politics and admin, as well as meetings. So, as a fractional CMO, you come in with authority and clarity, allowing you to focus on results and tune out everything else.

6. Focus on what you do best

Following that, it’s worth mentioning that fractional CMOs can play to their strengths, whether that’s launching new products, optimizing growth strategies, or scaling revenue, without being pulled into unrelated tasks.

7. Build your own brand

Fractional CMOs are consultants, thought leaders, and a brand on their own. Building your personal brand is crucial, and working with several businesses can give you more credibility and visibility.

8. Job security 

While being a fractional CMO can come with the challenge of securing new clients (which we talk about below), there’s no denying that having multiple streams of income helps with job security. If you lose one client, you still have others.

You’re also seen as an expert, which makes contract renewals and referrals easier.

9. Potential to scale

You may start this type of work on your own. However, fractional CMOs may also expand into running agencies or advisory firms and since they've worked with many different people, it’s easier to scale their business that way.

Challenges of fractional CMO roles

1. Limited time, high expectations

As a fractional CMO, you’re expected to juggle multiple responsibilities even though you might just work one to three days a week. This means you have to deliver the impact of a full-time CMO in a fraction of the time.

For example, imagine you’re hired by a startup to increase its leads by 30% in six months, and you might be faced with a small team or a not-very-well-optimized tech stack. With only a few days a week to work your magic, you might struggle to balance your strategy with the team’s execution.

2. Difficulty integrating

Being part-time makes it harder to build deep relationships with your team, understand how office politics work, and fully integrate within the culture.

Spending the first few weeks prioritizing relationships can help you create more buy-in.

3. Balancing strategy vs. execution

You may be hired to implement a high-level strategy, but you’re often also expected to see it through. Without a strong team to implement it, you may find yourself being stretched too thin. 

The solution is to have clear role expectations and boundaries so that you can stay focused on strategy while other people handle the execution side of things.

For instance, getting brought in to refine a company’s brand positioning and yet being asked to provide weekly updates on their ad performance (which usually falls within the manager role instead).

4. Proving value quickly

You have to demonstrate impact fast, usually within the first few months of being hired, all the while being part-time. Otherwise, you risk being replaced before you can actually make a difference.

Many fractional CMOs find that delivering a mix of quick wins and long-term strategy helps them prove that value quickly.

5. Managing stakeholder alignment

As a fractional CMO, you may work across multiple companies, which means it can be challenging to align leadership, sales, and product teams. In turn, this can lead to slow decision-making (and impact results).

To prevent this, perhaps you can host biweekly strategy check-ins with all stakeholders to help make sure everyone is on the same page, as well as guarantee buy-in.

6. Adapting to different industries

Every company has its own market, customer base, and growth challenges. Quickly switching gears between orgs (and industries) means you have to be adaptable.

One company might be small and service a very niche market, while another may have thousands of employees and markets in several countries, and you have to be flexible enough to deliver results in both.

7. Limited resources

Usually, however, the fractional CMO role is in companies with lean budgets and teams. This can make it more difficult to implement impactful strategies since you might not have the authority or budget to make large-scale changes.

In the end, this could result in issues with leadership since you may not be able to execute the meaningful strategy you were brought in to do.

Leveraging AI or employing freelancers can be helpful in combating these restrictions.

8. Transitioning out successfully

As a fractional CMO, you don’t have a permanent role at a company, so you have to set them up for success when you leave.

For instance, you’ve been hired for a few months at Company A to build a strong lead gen strategy, but when your contract ends, there’s no one to take over (meaning the progress will stall).

To make sure the handoff is smooth, you may have to train internal teams or hire a replacement to keep things going.

9. Securing clients

Most fractional CMOs work as independent contractors, so you have to constantly market yourself to get new clients.

You may finish a contract and then spend the next couple of months networking to find your next position.

So, a strong presence on platforms like LinkedIn is crucial, as is attending and speaking at events, and creating thought leadership content that can help you get those leads.

In short

There are advantages and downsides to accepting a fractional CMO role. In the end, this type of work is best for those who enjoy working in dynamic environments and solving different marketing challenges.

There’s no denying that being a fractional CMO can be very lucrative, so start building your network today if you’re interested in becoming one.

Get your free copy of our AI for Marketing Leaders playbook for real-world case studies from CMOs who are successfully integrating AI, as well as strategies for building a great AI-enhanced martech stack.

Most marketing teams are still treating visibility and traffic as the same thing. For years, that made sense. If people could find you in search, they'd visit your website. If they visited your website, you had a chance to influence the buying decision. But that relationship is becoming increasingly fragile.

Today, buyers are getting answers before they click. They're comparing vendors before they visit websites. And they're forming opinions based on AI-generated responses that marketing teams often don't track, measure, or influence.

Around 68% of Google searches in the US now end without the user clicking through to any external site. For marketing leaders who built a pipeline on organic traffic, that figure isn't a trend to monitor. It's a structural shift already affecting results. 

AI Overviews now appear in over 60% of searches. Research from Ahrefs suggests that the presence of an AI Overview causes a 35% decrease in click-through rates for organic results. At the same time, ChatGPT alone has crossed a billion active users, processing approximately 2.5 billion prompts per day, quickly closing the gap with Google's 14 billion daily searches. 

The scale of this shift means the question facing CMOs isn't whether to adapt. It's whether the adaptation they're currently planning is deep enough.

The visibility problem has changed

For most of the past two decades, visibility meant ranking. Get to page one. Hold the position. Drive traffic. The underlying logic was a direct line from search query to organic visit to pipeline.

That line no longer holds in the same way. As Bill Hobbib, CMO at DemandScience, puts it: 

"Visibility is no longer defined by ranking. It is defined by inclusion in AI-generated answers. As AI systems increasingly mediate discovery, buyers are forming opinions before they ever visit a vendor's website. This shifts the goal of content from driving clicks to shaping how a brand is interpreted and cited."

The practical implication is that buyers arriving at sales conversations have often already formed a view of your category and your position in it, based on what an AI system told them. That view was shaped by content you may have optimized for entirely different purposes, or content you never controlled at all. 

Monica Kumar, CMO at Extreme Networks, encountered this directly:

 "We were in front of a big prospect. They slapped in front of us this huge competitive analysis that they had put together using AI. And lo and behold, we didn't look that favorable. But they had the wrong information."  

The prospect had done the work. They'd formed opinions. Sales walked into a meeting where the competitive framing was already half-set, based on how AI systems had represented the brand. This is now a common scenario. And most marketing organizations aren't actively managing it.

How AI search actually works

To understand why traditional SEO tactics are becoming less effective, it helps to look behind the scenes.

When someone searches for "best CRM software for small business," Google's AI isn't simply matching those exact words to a page. Instead, it expands the query into dozens of related searches, exploring adjacent concepts, comparison criteria, implied needs, alternative wording, and related entities.

In other words, AI search doesn't just look for answers. It investigates the topic. That's a big shift from traditional search, and it changes what gets surfaced. The first implication is that rankings matter less than relevance.

In the traditional SEO model, securing a top position for a target keyword gave you a relatively predictable path to visibility. In AI search, visibility is much less deterministic. Content is selected based on semantic relevance, contextual fit, and how well it supports the answer being generated. A page that ranks highly for a keyword may never be cited if another source better supports the AI's reasoning. 

The second shift is that pages are no longer the primary unit of value. Passages are.

AI systems increasingly extract individual sections, paragraphs, and explanations rather than evaluating an entire page as a single asset. One well-structured, authoritative paragraph can become part of an AI-generated answer, even if the user never visits the source page itself. 

That's changing how content needs to be written. Every section needs to stand on its own, communicate a clear idea, and provide enough context to be useful when pulled out of its original environment.

The third implication is that content infrastructure is becoming a competitive advantage. As Avi Bhatnagar, VP of Demand Gen Marketing at ServiceChannel, explains:

"Those companies that have built the infrastructure to build out definitions or glossaries or knowledge centers, they're really benefiting from the trends of what's happening in ChatGPT queries." 

The organizations benefiting most from AI search aren't necessarily producing more content. They're building structured knowledge that AI systems can easily understand, reference, and cite. 

In many cases, comprehensive knowledge centres, glossaries, frameworks, and reference content are outperforming high-volume publishing strategies.

From SEO to relevance engineering

Traditional SEO aimed to rank pages. Relevance engineering aims to be cited. The shift requires different practices, different content structures, and different measurements.

The optimization target is no longer a set of keyword clusters, but a comprehensive map of the semantic territory your buyers navigate. This includes the primary question, the adjacent questions, the comparative queries, and even the implicit concerns that buyers have but don't always articulate in search terms. Content needs to address the territory, not just the keyword. 

From page-level to passage-level construction. Every paragraph should be treated as a potentially standalone citation. That means front-loading the key claim or finding rather than building to it. It means clear data attribution. It means a modular structure so that AI systems can extract useful passages without needing surrounding context to make sense of them.

From generic to hyper-targeted. AI Mode uses user-specific signals to tailor responses based on individual search history and context. The same query can produce meaningfully different results for different users.

A single piece of content about CRM implementation is less likely to be cited than separate resources for startup founders evaluating their first CRM, mid-market sales directors migrating from legacy systems, and enterprise IT teams managing complex integrations. The personalization happening at the search layer requires matching specificity in the content layer. 

Chetan Deshmukh, Senior Marketing Manager at InfraCloud Technologies, describes the operational pivot his team made:

"We started structuring our content specifically for LLM citations, not just Google rankings. It's like the early days of SEO when most companies hadn't heard of meta descriptions. We're optimizing for how AI tools reference and cite sources."

Mehak Chowdhary adds the strategic framing:

"We're not trying to 'win every keyword' anymore. With AI Overviews, ChatGPT Search, Perplexity, informational traffic is getting absorbed at the answer layer. So instead of chasing long-tail volume, we're focusing on building contextual authority that LLMs can reliably reference."

What citation-worthy content actually requires

The organizations seeing positive results share three specific content practices: 

1. Original research and first-party data

AI systems can't synthesize what doesn't exist. Proprietary insights, primary research, and original data points are difficult to replicate and frequently cited. Angeley Mullins, with marketing leadership experience across Amazon, QuickBooks, and GoDaddy, makes the case directly:

"Content is still king. The GPTs are still sourcing from content, and they're sourcing from content that is being self-published by an individual or a business."

2. Expert-attributed perspectives 

Christy Marble, CMO at Siteimprove, emphasizes that attribution matters: "You need to have authority, which means you need to have named experts on your content." AI systems increasingly evaluate not just what is claimed, but who is claiming it. Content that floats without an attributed expert perspective is less likely to be treated as authoritative.

3. Comprehensive knowledge architecture

Glossaries, frameworks, definitional content, and structured reference material create citation infrastructure. This isn't glamorous content strategy. It's consistently outperforming high-production editorial content in AI-mediated search environments.

The measurement gap

One of the biggest challenges with AI search is that most marketing teams are still measuring success using metrics designed for a different era.

Keyword rankings. Organic traffic. Click-through rates.

Those metrics aren't irrelevant, but they're increasingly measuring yesterday's game.

As AI-generated answers absorb more of the discovery process, marketers need to understand something different: how often their brand is showing up in the answers themselves.

That requires a new set of questions.

• How often is your content being cited in AI-generated responses?
• When your brand appears in category comparisons, is it positioned prominently or mentioned in passing?
• And perhaps most importantly, what information is AI getting wrong?

That last point matters more than many marketers realise.

AI systems don't just surface information. They interpret it. If they're relying on outdated content, incomplete information, or inaccurate third-party sources, they can shape buyer perceptions before your sales team ever gets involved.

That's why Monica Kumar's team at Extreme Networks built a GEO (Generative Engine Optimization) playbook focused on monitoring AI visibility and accuracy. As she explains, they track "how favorably and positively is our company placed versus the competition when certain types of questions are asked in the LLM" and compare those answers against their actual positioning and product information.

The goal isn't simply visibility.

It's making sure the right story is being told.

And for some organizations, that effort is already paying off. Joris Brabants, a CMO who has been closely tracking AI-driven discovery, reports:

"We already see that 35% of leads get to know us via LLM searches. Organic traffic and conversions did drop, but not significantly."

That's an important distinction.

The organizations navigating this shift most successfully aren't avoiding change. They're adapting to it. They've become trusted sources that AI systems repeatedly reference, cite, and recommend.

Rather than focusing solely on preserving traffic, they're building authority in the places where buyers are increasingly getting their answers.

Where to start

The good news is that you don't need a six-month transformation programme to understand where you stand.

An honest audit of your AI visibility can take less than an hour, and it will likely tell you more than weeks of traditional SEO reporting.

Start by asking ChatGPT, Perplexity, Claude, and Gemini the same questions your buyers ask. Not branded searches. The category questions. The comparison questions. The "what should I look for when evaluating X?" questions.

Pay attention to how your brand appears. Which competitors are being mentioned? What information is being surfaced? Where is the narrative accurate, incomplete, or simply wrong?

The answers can be surprisingly revealing.

From there, focus on closing the gaps. Invest in original research that gives AI systems unique data points to reference. Build expert-led content with clear attribution. Create the glossaries, frameworks, and knowledge resources that help establish authority in your category.

It's also worth keeping an eye on the paid side of AI search.

As Chetan Deshmukh predicts:

"ChatGPT has already announced ads on free plans. Perplexity is doing sponsored answers. By 2027, 'LLM advertising' will be a line item in every serious marketing budget."

Just as organic search created an advertising ecosystem around it, AI search is likely to follow a similar path. Marketing leaders need to be thinking about both at the same time.

But the bigger opportunity is organic.

The brands showing up most consistently in AI-generated answers aren't simply outranking competitors. They're becoming trusted sources. They're publishing original insights, attributing expertise to real people, and building the kind of knowledge infrastructure that AI systems rely on when constructing answers.

Whether you're actively managing it or not, AI systems are already helping shape how buyers understand your brand.

The real question isn't whether you're showing up. It's whether the story being told is accurate, credible, and one you've helped create.