Account-based marketing, or ABM, has evolved from a niche strategy into a core approach for modern B2B organizations. ABM, in a nutshell, is a highly targeted B2B marketing strategy where marketing and sales teams work together to target and engage specific high-value companies with personalized campaigns designed exclusively for them.  

Instead of marketing to a broad audience and generating many leads, ABM focuses on: 

• Identifying target companies (accounts) 
• Understanding key decision-makers within those companies 
• Delivering tailored messaging and content 
• Aligning marketing and sales efforts to win that account 

As buying behaviors change, traditional lead-based models are becoming less effective, especially in complex B2B environments with long sales cycles and multiple decision-makers.  

To get a ground‑level view of ABM’s evolution, we spoke with Vadim Koenen, a Senior Marketing Operations and Automation Manager at Mod Op, about how ABM has developed, why it matters today, and how organizations can successfully operationalize it using integrated platforms. Here’s what he had to say, drawing on more than a decade of experience in marketing automation and operations. 

Why Is the Traditional Lead Model Breaking Down? 

For many years, B2B marketing relied on a linear, lead-driven funnel. Prospects moved from inquiry to marketing-qualified lead, then to sales-qualified lead, opportunity, and eventually customer. While this approach is still common, it is increasingly inefficient in today’s B2B landscape. 

There are three primary reasons for this shift. First, buying committees are growing larger. Decisions are no longer made by a single individual but by groups across departments, making it more effective to target entire accounts rather than isolated leads. Second, sales cycles continue to lengthen, particularly for high-value and enterprise solutions. Third, buyers are filling out fewer forms. Many now research vendors anonymously, making it harder for marketers to identify and influence decision-makers through traditional lead capture. 

ABM addresses these challenges by shifting the focus from individual leads to the account and the buying committee as a whole. 

What Are the Core Elements of an Effective ABM Strategy? 

Regardless of which ABM platform an organization chooses, successful programs tend to follow the same foundational principles. Modern ABM platforms such as 6sense and Demandbase are built to support these pillars through deep data integration and predictive insights. These core elements include: 

Account Selection and Ideal Customer Profiles. The first step in ABM is identifying the right accounts by defining an ideal customer profile (ICP). Advanced ABM platforms help uncover ICPs by analyzing first-party data from CRM and marketing automation systems alongside third-party and intent data. Over time, these platforms identify patterns that correlate with successful opportunities and closed-won customers. 

Unified Data and Platform Integration. ABM platforms integrate with CRM systems such as Salesforce and marketing automation tools like Marketo, HubSpot, Eloqua, or Pardot to enable bi-directional data syncing, so marketing and sales teams work from a shared, up-to-date data foundation. ABM platforms also incorporate website activity through IP-based identification, third-party intent data from syndicated content networks, and insights from review sites like G2 and TrustRadius to uncover what is often referred to as the dark funnel. 

Understanding Buying Stages and Intent. One of the most powerful aspects of ABM platforms is their ability to predict where an account is in the buying journey. By analyzing behavioral signals and intent data, platforms can determine whether an account is in an early research phase or closer to making a purchase decision. This allows marketing teams to align content and messaging to each stage of the journey. For example, early-stage accounts may receive educational content, while later-stage accounts get solution-specific messaging and sales engagement. 

How Do Marketing and Sales Align in an ABM Strategy? 

ABM is not just a marketing initiative. It requires close collaboration with sales. Sales teams should be engaged at the right moment in the buying journey and equipped with relevant, actionable insights. When sales enters the conversation with context about account intent, interests, and behavior, their outreach becomes more meaningful and effective. 

ABM platforms support this collaboration by surfacing account-level intelligence and enabling service-level agreements, tasks, and alerts within CRM systems. This ensures follow-up actions are visible, measurable, and accountable. 

Can You Share a Real-World Example of ABM in Action? 

One example of ABM in action is a competitor displacement campaign. By tracking keyword searches related to competitors, marketers can identify accounts that may be considering alternative solutions. These insights can trigger automated workflows that notify sales teams, enroll contacts into targeted nurture programs, or initiate outreach through tools like Slack and Salesforce tasks. 

Another use case involves named or tier-one accounts that sales teams actively pursue. Even if an account is in a purchase stage, it may be evaluating competitors. ABM platforms help identify this intent and route accounts into personalized, one-to-one engagement through sales enablement tools such as SalesLoft. This approach complements marketing’s one-to-many programs with highly tailored sales outreach. 

How Soon Can a Business See Measurable Results from an ABM Campaign? 

When ABM platforms, marketing automation systems, CRM tools, and sales enablement platforms are fully integrated, organizations achieve what can be described as platform synergy. Segmentation, orchestration, content delivery, sales engagement, and reporting all operate within an automated and evergreen framework. 

Campaigns run continuously, insights flow automatically to the right teams, and performance is tracked across the full buying journey. While ABM is not a short-term play, organizations that invest in building this ecosystem can see significant returns over time. In my experience, well-executed ABM programs have generated millions of dollars in pipeline and closed revenue within a year. 

What Are Marketers Often Missing About ABM? 

ABM is not about quick wins. It reflects the reality of modern B2B buying behavior, where decisions take time and involve multiple stakeholders. When executed thoughtfully and supported by integrated platforms, ABM enables marketers and sales teams to engage the right accounts, at the right time, with the right message. 

For organizations willing to commit to the strategy, ABM offers a scalable and data-driven path to sustainable growth in an increasingly complex B2B environment, and our team helps design, implement, and optimize ABM programs that drive measurable pipeline and long-term revenue impact. 

Organizations across every industry are investing heavily in data to drive decisions, improve efficiency, and unlock new value. In the world of AI, data has become more important than ever to feed models, train models, and unlock data as a competitive advantage. Yet many leaders overestimate the strength of their data foundation. This is rarely due to a lack of skill or effort. More often, teams work so closely with their systems that gaps, inconsistencies, and workarounds become normalized over time. 

When was the last time your organization stress-tested its data foundation? Common patterns include: 

• Systems seem integrated, but data flows inconsistently behind the scenes. 
• Reporting looks comprehensive, but teams rely on manual work–arounds to fix quality gaps. 
• Business units use the same terminology, but with subtly different definitions. 
• Structural issues are overlooked because teams have learned to work around them. 

Real Case Example: Two Revenue Centers, One Company, No Unified Sales View 

Data foundation challenges like, inconsistent data flows or manual workarounds, rarely surface without structured, objective questioning. As a result, organizations underestimate the complexity and overestimate the readiness of their data.  

In fact, one of our recent engagements identified this scenario impacting a client and limiting their ability to make strategic decisions about their business. Our client operated two revenue centers (product and service) under a single entity. Sales efforts were combined, but there were no unique identifiers or opportunity IDs linking the full journey of a sale across both centers. Neither group was able to use data about their customers and offerings definitively to drive decision-making.  

Further challenging this organization was the fact that leadership thought they knew what their customers and potential customers wanted from a value and pricing perspective, yet no market or customer research had been performed in the past six years to determine if their customer hypotheses were valid. 

On the surface, performance reporting looked solid. But once assessed, it became clear that: 

• Opportunities could not be tracked across teams. 
• Leaders could not see where pipeline leakage occurred. 
• Performance metrics were incomplete. 
• Strategic gaps and growth opportunities could not be clearly identified.  

Despite active pipelines and strong sales teams, the business could not answer basic questions about the full revenue lifecycle. Leaders had believed they had “good data”, but the assessment revealed structural barriers that had gone unseen for years.  

The Mod Op strategy team worked with the organization to design and implement a unified opportunity structure and consistent data capture approach. As a result, the organization gained visibility that directly supported better forecasting, strategy, and resource allocation. 

The Value of a Structured Data Assessment and Roadmap 

An effective assessment does more than highlight issues. It defines the path to value.  

A strong assessment evaluates data quality and completeness, system integration and architecture, governance maturity, and process consistency and controls. The resulting roadmap provides: 

• A clear view of immediate risks 
• Actionable recommendations 
• Quick wins that build momentum 
• A realistic sequencing of investments 
• Alignment across technical and business leaders 

This structure helps organizations focus on their resources where they will generate the greatest impact. The most effective assessments come from people who have actually run data organizations such as former operators who have built and owned these environments themselves. There’s a big difference between checking boxes to complete a standardized assessment and knowing which questions reveal underlying issues. Experienced operators recognize when surface level answers mask deeper, systemic problems. Having lived through these challenges firsthand creates an instinct for where the real issues lie, following problems down to their root causes and tracing opportunities to their full potential. 

Objective Insight Clarifies the Path to Value 

At the end of the day, every organization aspires to be data-driven, but self-assessment often clouds the real picture. An objective third-party view – using internal and external data – provides the clarity, structure, and guidance needed to turn data into a true strategic advantage. Leaders who challenge their assumptions and embrace external insight position themselves to make better informed strategic decisions and smarter investments that uncover hidden value, and build a scalable, data foundation that can drive a competitive advantage. 

For leaders ready to move from aspiration to execution, inviting an independent view is often the most impactful place to start. Let’s talk!

Last year, Mod Op Co-Chief Creative Officer Steve O’Connell predicted that “Brands need to learn the hard lesson that people don’t like AI-created ads.” 

So far … he seems to be on track. People don’t love AI ads — at least when they can tell they’re AI-made ads — but brands keep creating them.  

A Reality Check on AI‑Generated Advertising

The disconnect between consumer sentiment and brands’ strategy isn’t accidental – it’s wishful thinking. 

“Companies will keep hoping we reach the point where consumers accept generative AI-produced spots,” Steve shared back in December. “But while the novelty of AI-generated content still exists, the amount of slop is going to grow and the pushback against it will harden.” 

To better understand this gap, we reached out to Steve to get his thoughts on AI-created ads and what separates success from failure.  

What specific qualities make AI-generated ads less appealing to consumers compared to human-created content? 

It’s the AI-generated people. I think audiences, in general, don’t love knowing something is AI-generated, but it’s seeing artificial actors and the uncanny valley that really triggers the ‘ick’ response. There’s a lack of genuine emotional depth that audiences immediately detect – the eyes are just a bit too lifeless. 

Have you seen any brands successfully leverage AI in a way that resonates with audiences, or are all attempts falling short? 

I’d venture to say brands are having success and we don’t even realize it. AI crushes it when it comes to generating inanimate objects – it’s really hard to spot. Same with food photography, which is so retouched anyway. So, I’m sure some brands are using generative AI behind the scenes, staying quiet about it, and having a lot of success. 

The biggest win I’ve seen with a client doing pure generative AI and shouting about it was Kalshi’s “World Gone Mad” spot from last year. Since the concept was about things getting out of control, it made sense to leverage AI. And that seemed to give them more of a pass from the critics. 

What do you think brands could do to make AI-generated ads more authentic or engaging? 

Anytime you’re creating something that would already be done with computers, like visual FX, generative AI is fair game. Audiences tend to accept, or ignore, AI when it’s applied to technical processes rather than creative ones. 

For creative applications, the other path brands have tried is sharing the story behind the spot, focusing on the human effort that went into the production. Although that attempt fell flat for McDonald’s with their holiday spot— it may have softened the blow  

I think brands just need to be selective and not overuse generative AI. More importantly, don’t replace human actors with technology.  

How do you see the consumer pushback against AI-created ads evolving over the next few years? 

This is so hard to know. Logic would say that AI generation will get better and better. If AI-generated video ever becomes indistinguishable from the real thing, consumers simply won’t realize it. And most probably won’t put in the effort to do the research. So, it’s very possible we’ll see a slow progression toward total acceptance, since the economics of generative AI – speed and cost savings – make it increasingly attractive to brands.  

That said, if we see a giant movement against AI usage overall, which is likely given how fast we seem to be moving without guardrails, it’s possible companies will be forced to make a pledge about their AI usage and their willingness to refrain. This isn’t likely to happen but predicting the future beyond a year or so has almost become comical at this point.  

What advice would you give companies eager to use generative AI for their advertising while avoiding consumer backlash? 

Silly company.  

Kidding. I would say start small. We’ve used AI in ways that supplement productions we’ve filmed traditionally. When we wanted to add a little something extra we thought about in the editing room. Or when we wanted to fix something we couldn’t get exactly right on set. Those use cases seem totally fine, since AI is being used to enhance something humans made. 

When it comes to generating a whole spot entirely from AI, my first suggestion would be not to make technology the story when it doesn’t have to be. Normally brands get credit for using new technology and being on the leading edge, but the reception to AI advertising has been mixed at best. Consumers care more about the message and creative, than the tools used to make it — so be transparent about your use of AI, but let the work speak for itself. 

That said, be careful if you’re going to generate actors with AI. That’s the hardest to get right and the first thing audiences will notice. 

Do you think there’s a future where consumers won’t be able to distinguish between AI and human-created ads — or will the difference always matter? 

It’s hard to imagine we won’t end up with AI-generated spots as good as the real thing. I’m not saying an AI actor will ever be as good as Daniel Day-Lewis or Viola Davis. But in most cases, generative AI does mediocrity pretty well. And it will deliver the same results for advertising.  

A lot of brands out there accept mediocrity because it’s good enough. But I’m a creative, so I live by the old saying that good is the enemy of great. I think there will always be a difference between AI-generated and human-made ads. Most brands won’t think that difference matters. Some will. And it’s almost always the brands embracing creativity, authenticity and genuine storytelling that tend to come out on top. 

Moving Forward with AI in Advertising 

The tension between AI capabilities and consumer sentiment isn’t going away anytime soon. Brands have a choice: chase efficiency at the risk of alienating audiences, or find the balance between technological innovation and human creativity. Steve’s advice points to a clear path: use AI where it enhances production, be transparent when it matters, and never compromise on the human elements that make advertising memorable. The brands that figure this out early will have a significant advantage as the landscape continues to evolve. 

The rise of a new crop of powerful gen AI tools that deliver realistic imagery and video (think Sora, Grok, Higgsfield, and Nano Banana) has fundamentally changed advertising and marketing. Brands and agencies now have technology that can streamline and accelerate creative—from stress-testing ideas to producing campaign-ready assets. 

At the same time, this shift has introduced new challenges, including the rise of “AI slop,” which threatens the credibility and value of AI-driven creative overall. Consumers across both the open web and walled gardens are increasingly inundated with low-quality, uncanny-valley AI content. Today, brand risk is largely focused on preventing ads from appearing alongside this material. 

But an equally serious and related issue is emerging: brand-centered AI videos. 

Gen AI tools make it easy to quickly create high-quality content that uses a brand’s logos, symbols, and IP in ways that can erode brand equity and damage reputation. This is a fast-growing problem, and Mod Op is already seeing a rise in customer inquiries related to it. 

Reputation protection in the AI era 

To address this new wave of AI-driven reputational risk, we’re launching our AI Risk Intelligence offering. This new capability helps brands identify AI-enabled content threats and mitigate them both proactively and reactively. 

AI Risk Intelligence sits within Mod Op’s strategic communications practice because this is, at its core, a reputation issue—even though the impact can extend far beyond communications. The offering combines human-led auditing and forensic online research with AI tools to conduct in-depth analysis and manage takedown requests on behalf of clients.

Examining AI Platform Risk Levels 

In conjunction with the launch, our team conducted research across two platforms, Grok and Sora, to demonstrate how easily damaging AI-generated content featuring major global brands can be created.

Grok Findings 

Grok has emerged as one of the more permissive gen AI platforms when it comes to content creation, as seen in its ongoing issues with nonconsensual imagery. Those looser guardrails also extend to brand-related content. 

To better understand the implications, earlier this month Mod Op conducted an observational analysis of brands appearing in Grok-generated posts. After reviewing hundreds of prompt outputs, McDonald’s was by far the most frequently referenced brand. Mod Op’s AI Risk Intelligence team identified dozens of Grok-generated images—many sexual in nature—where users prompted the model to create bikini images featuring McDonald’s branding or to insert Ronald McDonald into existing scenes. 

See cropped examples (SFW) here: 

Sora Findings 

Recent Sora research published by Copyleaks documented cases in which the Burger King crown appeared in AI-generated videos of deepfaked public figures shouting racial slurs. The findings highlight how easily brand assets can be pulled into harmful narratives on the platform. 

Building on that work, Mod Op set out to better understand how permissive Sora is when it comes to brand usage in both prompts and outputs. We issued more than 15 basic prompts featuring OpenAI CEO Sam Altman in reputational risk scenarios—such as unboxing an iPhone that catches fire or falsely claiming an OTC drug causes autism. (Sora’s ability to place real and notable people into fabricated video content increases brand risk by lending credibility to AI-generated videos for unknowing audiences.) 

Sora complied with virtually all test prompts, including scenarios that would likely damage brands. See examples here (to avoid creating or spreading misleading content, all videos were generated in draft mode and shared only as screen recordings, not published): 

Sora rejected some prompts involving physical harm and certain public figures, but was more permissive with brand-related content, suggesting brand protections are underdeveloped. 

Takeaways 

Taken together, this research points to a clear gap in how generative AI platforms handle brand-related prompts and outputs. In most cases, brands are treated as neutral creative inputs, even though misuse can carry real reputational consequences. 

This creates a new category of exposure, where logos, products, and brand symbols can be placed into misleading, off-brand, or damaging scenarios at scale. Because these tools are fast and widely accessible, harmful content can be generated and spread across platforms long before brands are aware it exists. 

In response, Mod Op has launched the first iteration of AI Risk Intelligence. Over time, we are working to mechanize the offering with automated detection, monitoring, and analysis that can scale with the pace of generative AI itself. The goal is to move from one-off assessments to continuous intelligence, enabling brands to identify emerging risks, track patterns of misuse, and respond in near real time. 

Ultimately, AI risk must be treated as a reputational issue first. Platform safeguards alone are not enough. Brands need active oversight, ongoing monitoring, and a clear response framework. AI Risk Intelligence was built to provide exactly that—helping companies understand where their brands appear, how they may be exposed, and how to protect their reputation in the age of generative AI. 

Does this sound familiar?  

You share a brief.
You identify a target.
You provide relevant data.
And in response, you receive a creative idea emotionally compelling enough to shift perceptions and behavior. 

Along the way, you wonder, “is it credible that the target would engage with our idea?” You check that the idea aligns with as much of the provided data as possible — including what’s performed successfully in the past. Everything short of dropping it on the audience points toward success. 

And then, it’s a big, flat flop. 

The good news? There’s a new AI-powered tool to help prevent these outcomes: digital twins.  

What I Mean by Digital Twins — And Why This Definition Matters 

I know, at first “digital twins” sounds a lot like Skynet — a cold and creepy AI threatening to crush your obsolete human talents under relentless advance. But when I talk about digital twins, I’m not referring to fully synthetic audiences generated out of thin, digit al air. I’m talking about digital replicas of real people trained to think, respond, reason, and associate meaning the way actual humans do. 

Some research platforms and consultancies are already showing that synthetic samples, when modeled carefully on permissioned human datasets, can approximate much larger populations with surprising accuracy. So, there will absolutely come a day when creative teams trust purely synthetic audience simulations. But in the near term, even if creatives had that kind of trust in the accuracy of synthetic data – there would still be skepticism.  

The first question any CMO or CCO will ask when presented with AI-powered audience simulations is: “How do you know these synthetic people actually behave like our real buyers or fans?” 

The answer is this: “Each digital twin is a 1:1 replica of a real human respondent.” The process starts with actual people who are surveyed at scale to capture not just demographics and psychographics, but also the linguistic and cognitive fingerprints that make their thinking unique: 

• the words they choose 
• the associations they make 
• how they interpret metaphors 
• how they describe frustrations and joy 
• how they justify decisions 
• how emotions show up in their narratives 

With this dataset, AI models can be trained to behave like that specific human, responding to new questions the way that person would. 

The result: A digital twin that responds like the human it mirrors — offering a trusted, stable, and expressive audience to test ideas with. And because that twin is grounded in real human responses, its feedback is far more credible than a generic “synthetic consumer.” 

How Digital Twins Are Built Today (Without the Science-fiction) 

Here’s the practical version of how this works — 100% science-fact. 

Recruit real human survey respondents at scale. Newer research platforms are making this dramatically easier. For example, Panoplai (formerly Glimpse) uses a mix of first-party data and rapid survey deployment to recruit thousands of qualified respondents worldwide in minutes, not weeks. They emphasize open-ended questions and build “responsive digital twins” that teams can then chat with to test concepts, creative, and messaging.  

Ask both closed-ended and open-ended questions. Closed-ended questions provide structure. Open-ended questions give you human texture. A growing ecosystem of tools and research proves that personality and emotion can be inferred from text alone — not just what people say, but how they say it. Academic work out of the University of Barcelona has shown that AI models can detect personality traits from written text with accuracy comparable to human assessors. On the commercial side, personality-analysis platforms (for example, those used for AI-powered feedback and communication coaching) are already analyzing language patterns to infer traits, preferences, and emotional tendencies from natural language. That same underlying capability is what lets digital twins become expressive, not just statistical. 

Train a digital twin for each human. Each respondent’s linguistic, emotional, and associative patterns become the training data for a corresponding AI twin. That twin can then answer new questions in the respondent’s style, respond to new creative stimuli with similar tastes and biases and, “talk” through its reactions in language like the original person. This means going beyond bar charts to interact with a chorus of digital voices conversationally. 

Validate accuracy by “going back” to the humans as needed. If you want to go further, you can re-survey the original humans later and compare their answers to their twins’ predicted answers. Some digital-twin platforms and UX research groups already use this kind of hold-out testing — removing a portion of the survey data and measuring how closely the twin predicts those missing responses or completely new questions. 

As needed, scale your digital twin audience outward from its human core. Once the twins are validated, you can responsibly extrapolate. For example, from 1,000 humans, you might generate 10,000–20,000 synthetic variants that interpolate between those real patterns. Large research firms are already using synthetic respondents modeled on fraud-free, permissioned human samples to mimic larger populations while keeping the underlying distributions and relationships intact. The key is that every synthetic expansion has a traceable lineage back to real human behavior. 

What Creative Can Do with Digital Twins That They Couldn’t Before 

Digital twins open an entirely new creative R&D lab.  

A low-risk playground for bolder ideas. Want to explore a provocative concept, test a different emotional temperature, or push a visual metaphor further than leadership might initially allow? Test it with your twin audience first. See how they respond. Refine accordingly. 

Faster iteration cycles. Creatives normally get feedback at two points: early, when the work is fragile, and late, when it’s expensive to change. Digital twins fill the massive gap in the middle. They give you real-time reactions so you can shape work in the same moment you’re developing it. 

Better alignment between strategy and creative. Misalignment is expensive. Digital twins help ensure everyone—strategists, creatives, and brand stakeholders—is working from the same understanding of how the audience will respond. This allows strategic hypotheses to be tested with the audience before the creative team invests in full concepts—and again as the work takes form. 

More nuanced insight than traditional panels. Digital twins understand small tonal shifts, subtle wording differences, metaphor comprehension, visual interpretation, sequencing and story logic, and framing effects. Traditional testing panels rarely allow that level of depth without blowing up your timeline and budget. 

Things to do (now) to Leverage Digital Twins 

1. Ask your creative partners to provide you with a digital twin platform. 
2. Submit copy options to a digital twin audience focused on tone & voice experiments – how do different segments react differently to humor levels, communication style, tone, emotional range and pacing or rhythm 
3. Query a single digital individual on storyboard/script reactions. Walk twins through early storylines to pinpoint where engagement rises or falls, i.e., Which beats confuse them? Where does emotional intensity peak? Does the payoff land? 
4. Establish visual style resonance by feeding mood boards, comps, or early frames to the twins and analyze responses across different demographics and psychographic clusters. 
5. Use digital twins to anticipate cultural interpretation and sensitivity including, unintended meanings, cultural misalignments, emotional misfires, language that may alienate certain groups. 

How Can You Get Leadership Buy In? 

Well … the truth is that most leadership folks are hearing constantly – “How are we using AI to get ahead?” You can provide them with the answer. Digital twins as discussed in this blog post are a measurable, scalable and economical way to leverage AI to support human progress. If you start now, you’ll be ahead of the next wave in creative development. 

Marketing in 2026 isn’t short on ambition — it’s short on patience.  

Boards want proof. CFOs want clarity. CEOs want enduring growth. And CMOs? They’re expected to deliver all three while navigating AI disruption, fragmented media, evolving consumer trust, and tighter budgets. The result is a clear shift in how marketing leaders are setting priorities.   

Drawing from leading research across Deloitte, McKinsey, BCG, Bain, Forrester, and others, one thing is obvious: the modern CMO is no longer measured by creativity alone, but by credibility.  

The New CMO Mandate 

Here’s what’s defining CMO agendas in 2026 and what it means for brands that want marketing to be a true growth engine.  

1. Profitable Growth Is the North Star: CMOs are prioritizing profitable, sustainable growth over pure top-line expansion — a notable shift from just a year ago. Investor pressure, economic volatility, and sharper financial oversight have forced marketing leaders to show how spend drives margin, not just momentum. In practice, this means reallocating budgets toward high-ROI channels, rebalancing brand and performance investments, and making harder calls about what no longer earns its keep.

2. Marketing ROI Moves Beyond the Marketing Department: Proving ROI has been a long-standing challenge. In 2026, it becomes a non-negotiable. CMOs are moving beyond siloed KPIs, like awareness or engagement, and adopting enterprise-level measurement frameworks that tie marketing activity directly to revenue, profit, and EBITDA. This shift reflects a broader push for shared accountability and stronger alignment across marketing, finance, and executive leadership.
   
   
3. AI Becomes Embedded Across the Marketing Ecosystem: AI in marketing is no longer experimental. It’s foundational. By 2026, CMOs expect AI usage to be more than double, with generative AI embedded across personalization, creative development, analytics, and media optimization. The most forward-thinking leaders aren’t just automating tasks. They’re reengineering their operating models.

4. Personalization Gets Real: Customers don’t move in straight lines anymore — they stream, scroll, search, shop, and switch channels constantly. CMOs are doubling down on data-driven personalization that reflects these nonlinear journeys, shifting budgets toward moments that genuinely influence decision-making.

5. Talent Becomes a Strategic Investment: Technology doesn’t create advantage — people do. As AI and analytics reshape marketing, CMOs are prioritizing internal capability building, especially in data literacy and advanced analytics.

6. Data, Privacy, and the Single View of Performance: With third-party data fading and privacy regulations tightening, CMOs are investing heavily in first-party data strategies and unified analytics platforms.

7. The CMO’s Role Expands at the Executive Table: In 2026, marketing leaders are forging tighter alignment with CEOs and CFOs, often taking greater ownership of the customer P&L.

8. Brand Purpose Requires Greater Care and Clarity: Brand purpose remains an important component of long-term brand equity, but in 2026 CMOs are approaching it with greater discipline. Rather than broad statements or reactive positioning, leaders are focusing on clarity, consistency, and alignment with core business values.

9. Channel Expansion and Media Innovation Accelerate: Retail media, social commerce, podcasts, and emerging platforms are drawing increased investment as CMOs chase attention wherever it actually lives.

10. Agility Becomes the Ultimate Advantage: Rigid annual plans can’t keep up with real-time change. CMOs are embracing continuous experimentation, flexible budgets, and faster decision cycles.  

The Bottom Line  

The CMO agenda for 2026 is clear: marketing is expected to deliver measurable business impact and sustained growth.  

Success belongs to leaders who can connect creativity with data, innovation with accountability, and brand with business impact. At Mod Op, we see this shift not as a challenge, but rather an opportunity to help marketing leaders turn ambition into measurable growth. 

The pace of technological change in marketing is accelerating faster than ever. New platforms emerge constantly, data systems grow more complex, and the tools your team relies on today may be obsolete tomorrow. As Jack Welch famously noted, “If the pace of change outside outpaces change internally, then the end is near.” For marketing leaders preparing their teams for 2026 and beyond, this reality demands a strategic approach to building resilience and agility. 

Here are five considerations as you look to future-proof your marketing team in 2026.  

Start with Your “Why.” Not Your Tools. 

The first mistake most teams make is jumping straight to technology adoption. They see a shiny new platform and assume it will solve their problems. It won’t. Instead, ground your strategy in a clear business purpose. Before implementing any new tool or process, ask: What specific business problem are we solving? This prevents the “bright shiny object syndrome” that leads teams down expensive, ineffective paths. 

When you start with clarity on your business objectives, every technology decision becomes purposeful. Your team can evaluate tools against actual needs rather than marketing hype, and they’ll understand the “why” behind every change, which is essential for gaining buy-in during implementation. 

Invest in Adaptability Over Specialization 

The marketing landscape has fundamentally shifted in how it demands talent. Five or ten years ago, specialization was valued—someone who was an email expert, another who managed the website, another who owned the CRM. Today, the most valuable team members are those who understand foundational concepts and can apply them across multiple platforms. 

Look for people who understand if/then logic, problem-solving fundamentals, and conceptual thinking about data and automation. They may not be experts in every tool, but they can learn where the buttons are because they understand the underlying logic. This flexibility allows your team to adapt as tools evolve, without starting from scratch every time. 

Equally important? When hiring, looking for candidates with curiosity and the willingness to experiment. Your team needs people who can sit with new technologies, play with them, and translate abstract possibilities into practical business applications. 

Make Change Management Non-Negotiable 

Here’s what keeps many marketing leaders up at night: resistance to change. Getting people out of their comfort zones—especially those who’ve executed the same processes for decades—is genuinely difficult. Yet it’s essential for future success. 

Address this head-on through structured change management. This means more than just training sessions. It requires hands-on experimentation where team members can explore new tools in low-risk environments. It means translating academic knowledge into practical applications. It means leaders staying involved, understanding where teams struggle, and helping bridge the gap between training and execution. 

Create space for your team to learn by doing, not just by listening. 

Build for Connected Data, Not Siloed Expertise 

The old marketing model had marketers navigating multiple disconnected systems —marketing automation here, CRM there, website somewhere else. This fragmentation is becoming a competitive disadvantage. Modern successful teams integrate data across these platforms to respond in real-time to customer behavior. 

When your data systems are connected, you can see when a prospect engages with your content and act immediately. You can build relationships across the entire buying committee, not just chase individual leads. You can measure impact across the full funnel, not just at the bottom. 

This requires your team to think systemically. They need to understand how pieces connect and how to orchestrate experiences across channels—a fundamentally different mindset than traditional silo-based structures. 

Look Beyond Tools to Embrace AI for Efficiency 

As you plan for 2026, view AI as a force multiplier for efficiency, both in customer experiences and internal processes. The question isn’t whether to use AI, but how to leverage it to free your team from routine tasks so they can focus on strategy and creativity. 

The teams that will thrive in 2026 are those that embrace continuous learning, maintain clarity on business outcomes, and build cultures where change is normal rather than exceptional. Start today. 

As December rolls around, it’s natural to pause and reflect on the year that’s come and gone. But this season isn’t just for retrospection. It’s also the perfect time to look forward and consider how the events and lessons of the past twelve months will shape what’s ahead.  

While you’re weighing your marketing plans and budgets for 2026, it’s crucial to look at the bigger picture: Which trends are rising to the forefront, and which might fade away? What new hurdles could challenge us as marketers, and where do the greatest opportunities lie? 

To help you prepare, we reached out to a few of Mod Op’s 470+ experts across every corner of marketing services — from website development and creative, to paid channel marketing and strategic communications — to gather their insights on what’s in store for the year ahead. Here are some of the key marketing trends they shared for 2026. 

Customer Obsession Replaces AI-First as the Real Differentiator  

2023–2025 were the years of AI demos, hype, and efficiency claims. 2026 is the “so what?” year.  

Marketing leaders will no longer win by declaring they are “AI-first.” They will win by showing customers how AI makes their experience better.  

Experiences will need to adapt to emotion and deliver consistent, personalized value across channels in a way that feels human. Instead of AI at the forefront, it will need to become invisible while not violating trust and transparency.  

Tessa Burg, CTO 

Brands Will Continue to Learn that People Don’t Like AI-Created Ads.  

Companies will keep hoping we’ve reached the point where consumers will accept generative AI-produced spots. But while the novelty of AI-generated content still exists, the amount of slop is going to grow and the pushback against it will harden. We’ll likely reach that tipping point where it’ll just be the way most content is made, but it won’t be next year.  

Steve O’Connell, Co-Chief Creative Officer  

AI Search Will Drive Investment in Influencer as a Strategy for B2B 

Next year, B2B enterprise marketing folks will be spending more on influencer marketing and relations, as well as PR.   

As buyer groups continue self-educating and self-servicing (and increasingly using AI to do so), external voices become even more important: POVs from analysts, subject-matter experts, operators with opinions—these are becoming the primary, non-technical reasons that deals move forward.   

On top of that, both humans and LLMs feed off social proof and proof of success, meaning those formats offer a stacked benefit on top of the visibility these tactics traditionally have offered.  

To me, this makes influencer investment in B2B more important as AI search continues to be a tool in finding and cross-checking outside perspectives.  

Tomas Madrilejos
Director, B2B Digital Marketing Strategy 

The Need for LLM Visibility Means All Eyes Will Be On GEO 

Generative Engine Optimization (GEO) is becoming as vital as SEO for brand visibility and in 2026, we will see a surge of brands prioritizing GEO as part of their PR and marketing strategies. As AI-driven engines like ChatGPT and Gemini become gateways to information, brands must ensure their content is not just searchable – but citable. GEO focuses on structuring thought leadership, PR content, and earned media so generative models recognize and surface them as authoritative sources. SEO still builds the foundation through technical precision and discoverability, but GEO adds the layer of credibility and contextual relevance that fuels AI-era awareness.   

For PR leaders, the future lies in integrating both; creating AI-ready narratives, structured data and measurable ‘AI citation visibility.’ In the next phase of PR and communication, visibility means being part of the conversation, not just the search results.  

Sasha Dookhoo
VP, PR 

Enterprise Content Platforms Will See Accelerated Demand  

During 2026, organizations will increase investment in enterprise content platforms that support global scale, unify content and commerce, and integrate native personalization. Marketers will gravitate toward ecosystems that centralize automation, workflows, and orchestration. This will drive vendors to evolve toward modular, API-first architectures with embedded AI capabilities.  

Fabio Fiss
VP, Technology 

Respondent Experience is the Key Ingredient for Better Market Research   

In 2026, market research success hinges on a simple truth: respondent experience matters. When researchers prioritize how participants interact with surveys, participation rates climb and data quality improves.  

This secret isn’t complicated. Participants expect more than ever from survey experiences including an engaging look and feel, mobile-friendly design, and gamification where appropriate. Respect respondents’ time. Ask only what’s necessary and use jargon-free language. Explain why their feedback matters and how it will drive real action.   

Remember also that every survey is a brand experience. Organizations that make participation in market research accessible, meaningful and even enjoyable will collect reliable insights, make smarter decisions, and build lasting relationships with their audiences.  

Lauren Schmidt 
VP, Market Intelligence  

Speed-to-Market Wins — Packaging Becomes the Accelerant  

In 2026, AI will expand its role as an always-on partner in packaging — accelerating the labor-intensive middle stages (adaptation, mechanicals, multilingual versions, hundreds of SKUs) so agency teams can focus more creative energy on strategy and concepting.   

Packaging now plays a central role in a brand’s speed-to-market strategy, and speed is increasingly the competitive edge. Brands supercharging their creative teams with tech in their adaptation workflows — under human creative supervision — will reach the shelf faster, respond faster, and win the sale. 

Philip Congello
EVP, Client Success  

Leaders Will See Marketing as Part of the Business – Not a Separate Function 

Leaders who will accelerate through 2026 are rejecting the idea that marketing is a separate function. They are building organizations where marketing, sales, product, and operations act as a single team with one mission: growth that is scalable, repeatable, and rooted in audience value.  

When marketing operates as one with the business, audiences feel the difference. They trust more. They choose faster. They stay longer. That is the winning path.  

Jenelle Maddox
VP of Client Success 

As for me, I’m going to be watching who’s investing in research and thought leadership. In 2026, the primary competitive advantage in marketing will shift from AI implementation (aka who uses the best tools) to data ownership.  

As the “data wall”—the point where high-quality, public internet data is exhausted—becomes a reality, the AI arms race will pivot. The winners will be those who can feed their AI models with new research and “dark data” (the undigitized, offline, and institutional knowledge that currently lives in file cabinets, archived recordings, and the minds of senior experts). 

What trends are you watching as we head into 2026? Meet us over on LinkedIn to share your thoughts.  

In the Mad Men era, advertising was refreshingly simple. A single piece of artwork, a catchy headline, and perhaps a television commercial—that’s what sold products. Today’s marketing landscape is exponentially more complex. Audiences are everywhere, attention spans are shorter, and competition is fiercer than ever. Yet in this chaos lies an unprecedented opportunity: the convergence of human creativity, data, and artificial intelligence. 

The challenge isn’t just reaching your audience anymore—it’s reaching them authentically and at the right moment with a message that resonates emotionally. Here’s how forward-thinking brands are mastering this trifecta. 

Align Audience and Story Through Data 

You likely have access to more audience data than ever before. The question isn’t whether you have it or not, it’s whether you’re using it to make fundamentally different creative decisions. 

Most brands treat data as a targeting mechanism: finding the right people, at the right time, on the right channel. That’s table stakes now. What separates winners is using data to determine which story to tell which audience. You can tell the same story 500 different ways. The work is identifying which narrative variations will resonate with which segments. 

This means moving beyond demographic segmentation to identifying clusters of people who share not just characteristics, but motivations. What drives them? What communities do they identify with? What emotional outcome are they actually seeking? 

For marketing leaders, this shifts the work fundamentally: instead of asking “How do we reach everyone with one message?” you’re asking “Which narrative variations will resonate with which segments?” Data becomes your creative strategy tool, not just your media targeting tool. It’s the difference between spray-and-pray and precision storytelling. 

AI as a Creative Enabler 

Here’s where many brands misunderstand AI’s role. AI shouldn’t be used to replace human creativity; its power is supercharging it. 

Consider the traditional creative pitch: a script on paper, a paragraph describing an idea. The challenge is imagining the vision, while balancing the risk of the worst-case execution. With AI tools, creatives can now generate proof-of-concept visuals in hours, not weeks. They can show stakeholders the feeling of an idea through music, imagery, and motion—not just describe it. This bridges the risk gap and enables bolder creative choices. 

For creative professionals, generative AI unlocks creative liberation. Specialists can now augment their gaps—the solo musician who can’t also choreograph the dance, the writer who can’t produce the video. AI doesn’t diminish craft, instead it democratizes the full realization of creative vision. 

Instinct Plus Intelligence 

In today’s marketing landscape, instinct alone isn’t enough anymore. The most transformative campaigns emerge when human instinct and data intelligence work in concert. Data identifies where and how to reach audiences. Human creativity determines what resonates emotionally. AI accelerates ideation and execution. 

Rather than choosing between gut feeling and data-driven decisions, today’s most successful brands do both. They trust their instincts—honed through experience—while validating them with data. They use AI to explore possibilities faster, not to make the final creative decisions. 

The brands that will dominate the next decade won’t be those who fear AI or obsess over data metrics. They’ll be the ones who combine all three elements strategically:  

• Using data to understand audiences deeply 
• Leveraging human creativity to forge emotional connections 
• And, employing AI to accelerate the journey from insight to impact. 

The recipe hasn’t changed since Don Draper’s era: know your audience, tell them something that matters, and make them feel something true. We’ve simply gained more sophisticated tools to accomplish it. 

What if your team could build a custom, competitor price-tracking dashboard in two hours. Or create a tool that automatically formats your weekly reports exactly the way the CMO or CRO likes them, without sending a single Jira ticket? 

These aren’t hypothetical scenarios. They’re real business challenges that many teams can start solving today with micro tools: small purpose-built utilities created with AI.  The biggest advantage? Anyone on your team can build these tools, coded by AI, with little to no technical chops required. 

The Economics of Software Have Changed 

For decades, software development has operated on a simple principle: economies of scale. Companies concentrate talent and capital to build tools that benefit the broadest possible market. They need thousands, if not millions, of users to justify the investment in systems design, coding, infrastructure, and security. 

This model created a clear hierarchy of problems worth solving. Enterprise-wide challenges? Worth it. Department-level inefficiencies? Maybe. Individual workflow friction? Not economically viable. 

But AI coding agents – “vibe coding” tools, as they’ve been called – are changing the math. Tools like Anthropic’s Claude, Google’s AI Studio and OpenAI’s Codex are getting good enough that building a custom solution may now be faster and cheaper than working around the absence of one. For the first time, we can afford to solve problems that exist outside traditional economies of scale. That’s what makes micro tools interesting. They’re not revolutionary. They’re economically rational.

Mod Op’s Innovation Team builds micro-tools regularly. One micro-sized example: our PR team needed a way to make it easy for staff across the agency to share earned media coverage and other thought leadership on social media with proper tracking and on-brand messaging. In less than a day, we built a web app that generates post templates with pre-written commentary and automated UTM tracking, solving a workflow problem that would never have made it onto a traditional development roadmap. We’ve used the same approach to build internal-facing Mod Agents and increasingly, client-facing teams are building rapid “pretotype” agents and integrations on our Nexus AI platform. 

What Makes a Problem “Micro Scale”? 

Micro tools are purpose-built software solutions designed to solve specific workflow problems for small audiences, sometimes even just one person. They address the inefficiencies that have lingered in organizations for years simply because they weren’t big enough to justify traditional development costs. 

Every organization has dozens of these problems. Individually, they feel like minor day-to-day friction. Collectively, they could represent a significant amount of time every week. The reason these problems have gone unsolved isn’t technical difficulty. Traditional software development just has too much overhead to justify solving narrow problems: requirements gathering, stakeholder alignment, development cycles, QA, maintenance. The fixed costs are too high relative to the benefit. 

But when AI can handle much of that overhead, the economics flip. Suddenly, spending a handful of hours with a coding agent whipping up a tool that saves two people three hours a week becomes worth solving. That is micro scale. 

How to Start Building Micro Tools 

If you’re doing the work day-to-day, you’re best positioned to identify what’s worth fixing. Here’s how to start thinking at micro scale. 

1. Shift from Tactical Execution to Strategic Thinking. Most marketing roles are defined by execution: run the campaign, build the deck, send the report. But as AI tools become more capable, the value shifts toward people who can identify which problems are worth solving and how  to solve them efficiently. This doesn’t mean abandoning execution. It means thinking critically about the work itself. Why does this process exist? What’s the actual goal? Is there a faster way? Teams that develop this habit will spot opportunities others miss. 
2. Look for Problems Too Small for Engineering. You’re mining territory that traditional software development ignores. Challenges so specific, so narrow, or so boring that they’ve never justified formal development.  Start with one well-defined problem that affects you personally. Build something simple. Use it. If it saves you time, that’s success. If it covers a use case that your teammates could benefit from too, share it. That’s multiplying its impact. Just don’t get too attached because micro tools should be replaced when something better is introduced. 
3. Build Where You Work. The best place to start is right in your existing workflow. Think about browser extensions, app plugins, API connections that let you embed and integrate into tools you already use to minimize context switching. If you’re already working in Figma or your CRM or your browser, that’s where your tool should live. 
4. Your Domain Knowledge Is Your Advantage. You don’t need to be an AI expert, but you do need to understand your work well enough to articulate what’s broken and what better looks like. You know which data sources are trustworthy. You know which edge cases matter. You know what “good enough” means in your context. 

How to Enable Micro Scale Innovation 

If you manage a team, your job is to create the conditions where micro scale thinking thrives. Here’s what that looks like in practice. 

Create Space for Micro Scale Innovation. Budget time for people to solve their own workflow problems, even if those problems don’t map to team OKRs or affect anyone else. The economics work when someone saves themselves meaningful time – three hours per week is 150 hours per year. That’s worth celebrating for one person, and potentially scalable to others later. Make this explicit. In 1-on-1s, ask: “What’s one thing you do regularly that feels like unnecessary work?” Document the answers. You’re building a catalog of micro scale opportunities. 

Set Boundaries That Enable Speed. The economics of micro tools only work if they skip the overhead of traditional development. That means being clear about what requires stakeholder approval and what doesn’t. A tool that solves one person’s workflow problem and touches no sensitive data? Let them build it. A tool that becomes mission-critical or handles customer information? That needs proper governance. Help your team understand where the boundaries are so they don’t wait for permission they don’t need. 

Establish a Partnership with IT Early.  Proactively align with your IT department to create a secure sandbox for innovation. Frame the conversation as: “My team wants to experiment with micro tools to improve their workflows. What are the guardrails we need to work within?” Most organizations already have lists of approved AI tools – like Mod Op’s AI Playground  – that employees can use to understand what they can work with. When your team understands your company’s security, governance and compliance needs, they can work fast and smart within those limitations. 

What to Watch Out For 

The same speed that makes micro tools attractive can create hidden costs. AI-generated code solves the immediate problem you describe but often misses edge cases, error handling, and long-term maintainability. A tool that works perfectly for you today might break silently when an API changes or when it encounters unexpected inputs. That’s manageable when only you depend on it. It becomes a major blocker when others have reorganized their workflows around it. 

Build assuming the tool will be replaced in three months. Document as you go: what problem it solves, what data it touches, what happens if it breaks. Don’t build when an existing solution is “close enough” or when the risk of failure is high. The maintenance burden – fixing bugs, updating integrations, answering questions – can easily consume an hour per month indefinitely. Factor that into your initial calculation. 

Most importantly, watch for the transition from personal productivity hack to mission-critical dependency. When something becomes essential for multiple people, it needs proper documentation, maintenance plans, and potentially engineering support. A tool serving one person that breaks is an inconvenience. A tool serving twenty people that breaks is a crisis. Help your team recognize when to build, when to stop, and when to hand something off to scale it properly. 

What to Do This Quarter 

With these foundations in place, it’s time to move from theory to action. Translating micro tool concepts into tangible marketing advantages requires deliberate experimentation and clear ownership: 

If you’re an individual contributor: Pick one task that takes you 30+ minutes per week and feels repetitive. Spend a few hours exploring whether AI tools could help you solve it. You’re not trying to build production software, you’re testing if the economics work for you personally. 

If you’re a team lead: Run a pilot. Pick an interested team member and one specific problem. Give them two weeks, remove barriers, and share the results, whether it works or not. The learning compounds either way. 

Where We’re Headed from Here 

The ability to solve your own workflow problems won’t be a differentiator much longer, it will be a baseline expectation. The teams that start now won’t just build better tools; they’ll build the muscle to identify which problems are worth solving in the first place. That’s the real advantage: not the tools themselves, but a culture of strategic thinking and self-direction.