> **来源：[研报客](https://pc.yanbaoke.cn)** # The 2026 Marketing Intelligence Report New research from Funnel shows how high-performing marketers harness analytics and AI to go full throttle. Here's how you can join them. # The marketing data paradox Why progress without transformation is holding teams back # Marketers today are surrounded by more data, tools and technology than ever before. When scrolling through LinkedIn, it looks like everyone has cracked the code: AI-powered campaigns, predictive analytics, automated everything. But beneath the polished posts, a different story emerges. When we asked marketers to grade their performance, they gave themselves a B- (82%) at best. And when asked to rate their agencies, marketers gave a similar, mediocre grade: 81%. For an industry that's never had more data, tools or technology, that grade says it all: progress without transformation. This middling self-evaluation isn't modesty. It's a candid admission that — despite all the investment in martech, data platforms and now AI — something fundamental isn't working. # Despite all the investment in martech, data platforms and now AI, something fundamental isn't working. In our 2026 study, we wanted to unpack this issue: Why is it that with so much innovative tech at their fingertips, marketers rate their impact only slightly above average? And what exactly are the highest performers doing differently that others can replicate? In other words, we want to give you a prescriptive playbook to close the gap between where you are and where you need to be. We won't give away all the answers just yet, but here's a preview. Marketers need to evolve in three critical ways: From reporting to discovery: Using dashboards as hypothesis engines — where analysis isn't the finish line, but the starting point for testing, learning and improving what comes next. From caution to experimentation: Building cultures where testing new approaches is rewarded, not punished — powered by measurement systems that make failure low-cost and learning high-value. - From fragmented data to AI-ready infrastructure: Creating unified, trustworthy data foundations that enable advanced analytics today and prepare teams for AI-driven decision-making tomorrow. Read on for a clear-eyed look at what's working, what's broken and what to do about it. # Table of contents So much data, so little insight 5 AI to the rescue? 7 Disruptors wanted 14 Unlocking advanced analytics 18 Making marketing intelligent 23 Marketers need to lead the AI shift 29 # About the research Funnel surveyed 238 in-house marketing professionals as well as agency professionals globally. We also interviewed six agency leaders from high-performing, data-savvy shops in the U.S. and Europe. The research was managed by a third-party partner, Ravn Research, and sponsored by Funnel. For more detailed demographic information, go to page 31. # So much data, so little insight Funnel's latest research shows many marketers are surrounded by ample data and advanced tools, but lack true insight and direction. They're piloting advanced machines, but still flying blind. Just look at the numbers: 72% of in-house marketers and 55% of marketers working in agencies say they have mountains of data, but turning it into insights is challenging. 86% of in-house marketers and 79% of agency marketers say they don't have a clear signal through the noise. In other words, they struggle to determine the impact of each marketing channel on overall performance. One marketer explains, "We're drowning in information, but what's the insight? What's the action that needs to be taken?" The problem: Many teams spend an enormous amount of time generating dashboards that look impressive but reveal little about what to do next. And in our experience, too many teams are still chasing vanity metrics — clicks, followers, impressions — that are disconnected from real business impact. "We have plenty of data, but turning it into useful customer insights is challenging." To be fair, the sheer complexity of modern marketing means many teams struggle to identify what's working across a maze of channels, touchpoints and devices. Sixty-eight percent of in-house marketers and over half (52%) of agency marketers say they don't have up-to-date visibility into campaign performance across channels. And Funnel's research shows in-house marketers, far more than their agency counterparts, feel adrift: $41 \%$ of in- house marketers say that when they report results, they don’t analyze the “why” or identify actions to take (agency marketers often fail to do this, but to a lesser extent, since they must defend their results to paying clients). Let's repeat that: More than two in five in-house marketers say they're simply documenting past performance, with little investigation into root causes, or next steps for improvement. They are maintaining — even protecting — the status quo rather than driving change. Agencies are 2x more likely than in-house marketers to make ‘robust recommendations’ in performance reports Q: Do your marketing performance reports include recommendations for action, or just summary data? # AI to the rescue? AI tools and automations are hailed as a lifeline to rescue marketers from the complexity of modern marketing. Will they? A study by SurveyMonkey found $93\%$ of marketers say AI helps them generate content faster. But great marketing isn't just about speed, volume and throughput — it's about resonance, distinctiveness and impact. So the question becomes: Is AI actually delivering on both promises? Can it make marketers faster and better? The answer, it turns out, is complicated. The first problem: AI doesn't fix messy data; it amplifies it. When the inputs are inconsistent and fragmented, the outputs may suggest accuracy but be totally unreliable (that's one of the problems with AI: It can be confidently incorrect). Clean, unified data is essential for generating clear signals and actionable insights, yet many marketers lack reliable access to such data. # Gen AI vs. Machine Learning When marketers talk about AI, they often refer to very different things. There's generative AI: tools like ChatGPT, Gemini and Midjourney that create text, images and ideas that marketers directly use and edit. Then there's machine learning, which powers ad platforms behind the scenes — systems that automate bidding, targeting and budget optimization based on patterns in data. The two are often conflated, but they work at different layers. With machine learning, you consume the AI's output indirectly through improved results (e.g., better CPCs, higher conversion rates). With generative AI, you're creating and consuming the output yourself in the form of an ad, headline or image. Both rely on data, but without a strong data foundation, neither delivers meaningful intelligence. # AI and creativity: A relationship still taking shape Funnel's research points to other uncomfortable contradictions at the heart of AI-driven marketing. Marketers are conflicted about AI's creative impact. A sizable number — $39\%$ of agency marketers and $23\%$ of brand marketers — say AI tools generate repetitive, generic campaigns. Yet more than half $(54\%)$ also believe AI enhances creativity on their teams. That tension captures where marketing stands today: inspired by AI's potential, but still learning how to use it well. Nearly half (46%) of marketers admit creative roles are most at risk of decline or replacement due to AI. Yet evidence shows the opposite should be true, that creativity is marketing's biggest multiplier. Research covering 1,250 campaigns and $140 billion in ad spend found that distinctive, emotionally impactful creative can drive up to 12x more profit than generic or forgettable ads. At the same time, $47\%$ of in-house marketers and $32\%$ of agency marketers say they find it difficult to keep up with the data-driven aspects of their work. Yet fewer than one in three $(30\%)$ use automation to handle repetitive SEO and optimization tasks — the very tools that could free them up for higher-value work. These contradictions reveal a deeper tension: Marketers know AI is powerful, but they're unsure whether it's a threat or an opportunity. That uncertainty is driving two very different responses. # How AI is changing creative work 39% of agency marketers and $23\%$ of brand marketers say AI tools create repetitive, generic campaigns. 46% of marketers admit creative roles are most at risk of decline or replacement due to AI. 54% say AI enhances creativity. 12x more profit with standout creatives versus generic ads. # The data skills and automation gap 47% of in-house marketers say they find it difficult to keep up with the data-driven aspects of their work. 32% of agency marketers say they experience the same difficulty. 30% use automation to take over repetitive SEO/optimization tasks — meaning most teams could free time for higher-value work. "Marketers feed AI platforms datasets for analysis, and get very self-assured answers back. But often, if you truly examine the data, it tells a very different story. That's the big challenge. Als are fantastic, PhD-level minds, but with so many different datasets, as well as a huge amount of business context they don't have access to, they require significant training and ongoing refinement to be truly useful." # Henry Arkell Co-founder and Director Millena # Fear versus optimism: Which will prevail? Marketers stand at a crossroads. Will they lean into fear and use AI primarily as a cost-cutting tool (think: more, faster)? Or will they embrace optimism and wield AI as a true competitive advantage? The fear scenario: Given weak economic indicators, marketing leaders may be tempted to use AI and automation to trim headcount and "do more with less." But cutting creative talent risks hollowing out the very thing that drives differentiation and emotional connection with customers. The ultimate irony? When OpenAI released its first high-profile brand campaign, it chose to hire human creatives and debut the work on television — a reminder that breakthrough creativity remains a fundamentally human advantage. "Al opens a window where we can start to do new things. The problem is that sometimes clients aren't ready to change." # Paula Gomez Global Data & Adtech Director Making Science The optimistic path: The smartest marketers treat AI as a tool for doing better work, not just more work. They're using it to eliminate drudgery, surface insights faster and free their teams to focus on higher-order thinking — strategy, storytelling and the kind of creative work that actually moves the needle. Taking the optimistic path, however, requires doing things very differently, including: - Investing in clean, unified marketing data as a pre-requisite, foundational step. Just $33\%$ of in-house marketers invest in structured data and metadata — the critical “fuel” that AI systems need to understand and surface your content. And only one in five in-house marketers say collecting and using first-party data as part of their overall marketing strategy is a top priority. - Deploying automations to eliminate drudgery and give marketers more time to pursue skills development and more advanced tasks. Currently fewer than one in three (30%) use automation to handle repetitive SEO optimization tasks, and even fewer use automations extensively for data-related tasks. - Offering opportunities to advance data skills, including learning advanced methods. Today, nearly half of in-house marketers say they struggle to keep up with data-driven tasks. - Rewiring organizational culture to value experimentation and calculated risk-taking. Well over half of in-house marketers say they don't feel free to experiment. So why aren't more marketers acting like innovators? The answer has less to do with talent and more to do with the environments they're working in. # The new rules of visibility: From SEO to GEO The playbook marketers spent decades mastering — SEO, funnel optimization, predictable customer journeys — is being rewritten in real time. Welcome to GEO (Generative Engine Optimization): optimizing content for inclusion in AI-generated results in tools like ChatGPT, Perplexity and Gemini, rather than just traditional SEO for ranking in standard search engines. Marketers are already experiencing this: - $64\%$ predict their customers will use traditional search engines less often in the coming 2-3 years. 43% expect a shorter customer journey due to AI-assisted decision-making. 41% say customer journeys will become more fragmented and unpredictable. At the moment, however, far too few marketers are prepared: Only half of in-house marketers (52%) create content optimized for AI and conversational search. Just 44% train their teams for AI-driven search and visibility practices. And fewer than one in three (30%) use automations for content optimization. Agencies are doing a bit better, but still fall far short of expectations. # Marketers are preparing for AI changes to search In-house marketers Agency marketers Q: How is your team preparing for AI-driven changes to search and visibility? # AI's impacts on digital advertising in the next 2-3 years Q: Which AI-driven shifts in consumer behavior will most impact digital advertising over the next 2-3 years? # Disruptors wanted Marketing leaders need to create cultures where smart risks are rewarded, not punished. Today, marketing teams have powerful, disruptive technologies at their fingertips, but a striking shortage of people who feel empowered to use them boldly. Despite readily available tech, $56\%$ of in-house marketers and $43\%$ of agency marketers say they don't feel consistently empowered to experiment with new approaches and adapt strategies. And a sizable portion — $41\%$ of in-house marketers — say they aren't fully comfortable raising concerns or challenging existing strategies. The problem is nearly four times worse for Gen Z than for their oldest (born pre-1965) colleagues. The primary culprits, say marketers? Leaders who aren't open to new ideas and company cultures that actively discourage risk-taking. "We have a leadership team that doesn't know how to and is scared to push the envelope and try new strategies," one marketer told us. # Do marketers feel empowered to experiment with approaches and strategies? Consistently experiment & adapt based on testing/results Sometimes experiment and adjust Rarely experiment; generally follow set strategies Q: Does your team feel empowered to experiment with new marketing approaches and adapt strategies based on what you learn? The net result of this caution: Many marketers simply aren't trying out new tactics, even though playing it safe is the riskiest move of all. For example, $64\%$ of in-house marketers and $53\%$ of agency marketers say it's been more than three months since they've launched a campaign that deviated from their typical practices (i.e., marketers are using a revolutionary technology to do just average work and achieve average results). Instead, marketers focus on small, less risky wins. One marketer explains, "People are afraid to change old habits for fear it will be unsuccessful, which would put a target on their back." And about those small optimizations? Many teams aren't even doing that consistently. Only $13\%$ say continuous review and refinement is embedded in their culture. Maybe this cautious, heads-down approach is understandable given how much is changing and how quickly. When markets are weak and organizations are under strain, fear of failure and job loss is amplified. "Playing it safe is actually the riskiest long-term strategy, as it leads to stagnation. Brands can make bolder moves by adopting a test-and-learn mindset and ring-fencing a small innovation budget. I like the 70/20/10 approach: $10 \%$ devoted to trying new things, $20 \%$ to optimizing what works and $70 \%$ to basic foundations." # Tom Roach VP, Brand Strategy Jellyfish But there's also a deeper issue at play: Many marketers lack the confidence to navigate the growing complexity of modern marketing. Nearly half (47%) of in-house marketers and 32% of agency marketers say they find it difficult to keep up with the data-driven aspects of their work. And many struggle to admit when they don't understand something at work. Again, younger marketers feel this more acutely. This creates a vicious cycle: Teams feel overwhelmed, so they stick to what they know. But sticking to what they know means they fall further behind as the discipline evolves. "To derive prescriptive insights and form connections between different types of data, you need experience and an understanding of how dimensions and metrics relate to one another. That requires a certain skill set. Unfortunately, a lot of marketers, especially in bigger companies, are preoccupied with meetings, senior executives and creating presentations explaining what they're doing, not necessarily working. If marketers had more mental capacity to really dive deep into data, they could create those insights themselves better than your average analyst." # Thijs Bongertman Chief Data Officer SPAIK # Moving beyond the status quo requires removing the barriers that make experimentation feel dangerous. And the most consequential barrier of all is a lack of trust in the data itself. When marketers can't trust their data — whether due to inconsistent numbers, murky attribution, or questionable results — every experiment feels like a career risk. Yet this is exactly where most marketing organizations fall short. Funnel's research shows that just one in three invest in structured data and metadata. High-quality, trusted data lowers the cost of failure. It transforms experiments into low-stakes learning opportunities. Marketing leaders who want bolder teams need to invest in a transparent, trustworthy intelligence foundation: clean data infrastructure and reliable measurement. "I find it difficult to keep up with the data-driven aspects of modern marketing." "Data analysts are very good at reporting on what happened. But to interrogate why something happened requires additional skills, including a broader understanding of how communications work, how campaigns are supposed to work, how brand growth works... and the myriad ways things can go wrong. That's less about data analysis and more about detective work. It requires strategy, curiosity and storytelling on top of the data analysis. That is a rare combination of skills, so it's not surprising that marketing teams are struggling with it." # Tom Roach VP, Brand Strategy Jellyfish # Unlocking advanced analytics Advanced analytics separate the confident from the cautious, but these methods are still out of reach for most. Last-click attribution was once the standard, but it's slowly being eroded by rising privacy regulations and changes by big tech firms — GDPR, CCPA, restrictions on third-party cookies and tracking. And search is no longer about chasing top keywords, but creating content that answers questions and stands out in AI-driven, conversational results. As search fragments across AI assistants and conversational platforms, marketers can't just rely on rankings or publisher-reported metrics. Independent measurement is now critical to truly understand how and where a brand shows up. Add pressure from boardrooms and budgets, and the stakes get even higher. Marketers are being asked to show revenue impact quickly and with defensible metrics — not engagement figures, which have lost credibility with leadership. Given these disruptive forces, marketing is evolving toward more advanced forms of analytics — models that don't rely on crumbling third-party data infrastructure to measure return on marketing investment, such as: - Data-driven attribution: Tracking customer touchpoints across channels to understand which interactions contribute to conversions. - Marketing mix modeling (MMM): Using statistical models to measure the impact of various marketing activities on business outcomes, such as sales and ROI. - Incrementality testing: Estimating the additional impact of a specific campaign by comparing the results of a test group exposed to the campaign with a control group that was not. Advanced measurement can also go beyond attribution to find correlations between channels, identify the lag between funnel steps, understand saturation points and build a genuine theory for how marketing drives business results — and ultimately revenue. "Marketing mix modeling is having a huge moment in part because it accommodates the reality of the world we live in: With the information I have at my fingertips, what is the best decision I can make with the highest level of confidence? And how can I devise a test to validate whether those assertions were accurate? We have to be willing to make decisions with confidence levels that are not $100\%$ ." # Dan Temby Senior VP, Technology & Analytics DAC Yet Funnel's research shows a striking gap: Only $21\%$ of agency marketers and just $8\%$ of in-house marketers consistently use advanced analytics. One in three marketers $(36\%)$ say they have "advanced" skills using at least one kind of data analytics to measure performance, but far fewer have advanced skills with more sophisticated, probabilistic models — such as market mix modeling $(15\%)$ , incrementality testing $(18\%)$ and attribution modeling $(27\%)$ . Agencies tend to show greater finesse with these tools, but still, few rate themselves "advanced." This is somewhat understandable, of course, given that a significant number of in-house marketers outsource advanced analytics work — and for agencies, refined measurement models are a product that can be monetized. Put simply: The marketing discipline has evolved faster than its practitioners. The good news? Marketers recognize this gap: $77\%$ of agency marketers and $58\%$ of in-house marketers say improving data analysis skills is "very important." The path forward involves democratizing analytics, moving these capabilities beyond specialized teams and making them accessible to marketers at every level. That means creating structured learning opportunities, as well as environments where teams can develop skills through hands-on practice. Experience with advanced analytics varies among marketing teams Q: Does your marketing team use any of these advanced analytics methods? "People need to be less afraid of switching places. Why are marketers not understanding advanced analytics? Why are they afraid to make change? It's because they don't understand what's going on at the other side of the table. Similarly, data people have no clue what marketing people do. This cross-functionality would serve everyone to create business value, but it's really under-leveraged by most companies I've ever worked at." # Thijs Bongertman, Chief Data Officer SPAIK A lack of data skills isn't the only problem. While most marketers (76%) say they connect their efforts to business goals, the data suggests a significant gap between perception and reality. When it comes to communicating with finance — the function that actually tracks business outcomes — just 13% of marketers do it very well. Thijs Bongertman, chief data officer at SPAIK, captured the problem: "A lot of companies have a reporting culture instead of an actionable insight culture. And what's often missing is business acumen — understanding the nitty gritty about what actually drives the business." A survey taker put it more bluntly: "The marketing team does not understand numbers, statistics or basic split testing — but remains adamant that they are in the driver's seat when it comes to the marketing automation implementation." Here's where it gets interesting: The teams that do consistently use advanced analytics aren't just better at measurement — they're better at marketing. They're more confident in their recommendations. They experiment more boldly. They provide stronger strategic guidance to leadership. And crucially, they speak the language of business impact rather than marketing activity. In other words, analytics maturity isn't just a technical capability. It's a cultural and performance differentiator that unlocks growth across the entire marketing function. The path forward: Start small and build systematically. Despite inconsistent use of advanced analytics today, most marketers say they want to improve the level of data analysis skills on their teams, particularly by developing marketing mix modeling capabilities (70% say this). They recognize that stronger data analysis skills are essential. After all, advanced analytics shouldn't be reserved for experts or large enterprises with massive budgets. These capabilities need to be democratized. Marketing leaders should take a graduated approach: - Start with data-driven attribution to understand the customer journey and which touchpoints matter most. This builds the muscle of thinking beyond last-click and starts creating a more sophisticated view of performance. - Layer in correlation analysis and funnel diagnostics to understand how channels work together, where there are lag effects and which combinations drive outsized results. Graduate to scenario planning and forecasting with market mix modeling and incrementality testing to reallocate budgets with confidence and move from reactive reporting to proactive strategy. The key is to start somewhere. Not every team needs a fully built-out market mix modeling practice on day one. But every team can take baby steps toward more rigorous measurement — and those steps compound quickly. # Analytics maturity ladder # Marketing Mix Modeling # Forecast and optimize with confidence # Correlation Analysis # Understand how channels work together # Data-Driven Attribution Understand the customer journey and which touchpoints matter most The evolution of marketing measurement # Take your measurement further Learn much more about the future of marketing measurement, including how to combine data-driven attribution, market mix modeling and incrementality testing for accurate performance measurement. Download the document. # Making marketing intelligent Turning data overload into competitive advantage means changing how teams work, not just what tools they use. # 1. Build the foundation: # Automate reporting and invest in clean, unified data Marketers are awash in data and short on insight. A large majority $(80\% +)$ of all marketers surveyed say they don't have a clear signal that helps them understand what's working. Before AI can deliver intelligent insights, the groundwork must be in place. That means automating data pipelines, unifying sources and establishing consistent measurement across the organization. Start by aligning all marketing-related functions on shared definitions and KPIs so that every team measures success in the same way. From there, centralize key data sources (e.g., media, CRM, web analytics and sales) into one environment where reporting can be automated and standardized. The goal isn't perfection; it's consistency. When marketers automate recurring reports and connect their data in one place, they create the foundation for clarity, faster analysis and more confident decision-making. Marketers also need to automate time-intensive tasks so they can spend more time strategizing, experimenting and taking action. Just $14 \%$ of agency marketers and $12 \%$ of in- house marketers say they’ve automated data- related tasks extensively. The shift is simple but profound: Stop spending hours compiling reports manually and start spending that time interpreting what the data actually means and what to do about it. # 2. Develop data capabilities: # Upskill teams and democratize access to insights Teams that use advanced analytics effectively and consistently outperform across a wide range of areas — from how effectively they turn data into useful customer insights to how often they experiment and try new strategies. For example, among those who use advanced analytics, $76\%$ say they feel empowered to experiment with new marketing approaches; just $36\%$ of those who use limited or no advanced analytics say the same. Developing data capabilities means democratizing access to marketing data and building confidence at every level of the organization. Today, only one in three (32%) in-house marketers say they have visibility into campaign performance across all channels. Agencies deploy automation for data-related tasks more extensively than in-house marketers Q: Has your team automated data-related tasks, such as data integration, report generation, and performance monitoring, to free up time for strategic analysis? Marketing leaders need to create a culture of full transparency and inclusion — where data isn't hoarded by analysts but shared widely, and where the tools to explore and understand performance are available across the team. It's also critical to invest in skills development for your team. Marketers themselves recognize this need. Eighty-seven percent of marketers say they're interested in learning new data skills like marketing mix modeling, attribution modeling, incrementality testing and even A/B testing. The goal isn't to turn every marketer into a data scientist. It's to ensure every marketer can ask good questions of the data, interpret the answers and act on what they learn. # 3. Operationalize AI # Move from experimentation to systematic implementation Marketers are eager to try out new AI use cases. For example, $69\%$ are interested in deploying automated insights, and $59\%$ would like to use autonomous customer journey mapping. Operationalizing AI means embedding it into everyday decision-making, not as a novelty but as a system. It requires clear ownership, strong data governance and a continuous feedback loop between AI outputs and human judgment. Teams that get this right move from one-off experiments to making AI part of their operating rhythm. Marketers are eager to try out new AI capabilities Q: Which of these AI-driven capabilities would you most like to see implemented in your marketing strategy within the next 1-2 years? # 4. Prioritize first-party data # Build collection strategies as part of your critical infrastructure As third-party cookies disappear and privacy regulations tighten, first-party data has shifted from "nice to have" to essential infrastructure. Yet just $20\%$ of in-house marketers say collecting and using first-party data is a top priority in their overall marketing strategy. And agencies report that fewer than half $(47\%)$ of their clients have a clear strategy for collecting and using first-party data. Marketers don't need more dashboards; they need better feedback loops. Conversion APIs (CAPIs) help close that gap by turning insights into impact, feeding platforms with trusted data that improves optimization and attribution. These tools let marketers send server-side conversion events directly back to platforms like Meta, Google and TikTok. While CAPI can work with standard pixel events, the real value comes when those events are enriched with first-party identifiers — email addresses, phone numbers, CRM or transaction data. That enrichment dramatically boosts match rates, improves attribution accuracy and helps futureproof measurement as cookies disappear. First-party data isn't strictly required for CAPI, but it makes it far more valuable. Marketing leaders need to prioritize building first-party data collection strategies now — not as a future project, but as foundational infrastructure for everything else they're trying to accomplish. # 5. Cultivate a bold culture Reward experimentation and calculated risk-taking Disruptive technology demands a disruptive culture. Yet most marketing teams are stuck in risk-averse modes. Fifty-six percent of in-house marketers and $43\%$ of agency marketers say they don't feel empowered to experiment with new approaches and adapt strategies. To renew culture for the AI age, leaders need to normalize questioning and create an environment that encourages experimentation (make it acceptable to say "I don't know" or "I don't understand"). Marketing intelligence isn't defined by your tech stack. It's defined by clarity — knowing what's really driving growth — and by the confidence to act on it with courage and intent. # Note to agencies: Advanced analytics services are your competitive advantage There's widespread agreement that generative AI will replace at least some traditional agency services. The majority of in-house marketers (88%) and agency marketers (72%) believe this. But here's the opportunity: While in-house marketers are falling behind on advanced analytics capabilities, agencies have the expertise to fill that gap. The race is on for agencies to build up their data intelligence skills and help marketers bridge the growing capabilities divide. Advanced analytics services — market mix modeling, incrementality testing, sophisticated attribution — represent the future of high-value agency work. One ongoing challenge agencies face is getting access to client data. Agencies that can solve this access problem and deliver analytics-driven insights won't just survive the AI era — they'll thrive in it. # AI is poised to replace parts of agency work In-house marketers Agencies Q: Do you believe gen AI will replace some of the services agencies currently provide to your company? Q: Do you believe gen AI will replace some of the services your agency currently provides to clients? # Marketers need to lead the AI shift The future of marketing intelligence will belong to those who build trust, question the data and act with clarity. The road ahead is filled with enormous promise as well as significant risk. Today's generative systems are giving way to agentic AI — models that can reason, plan and act with increasing autonomy. These advances point toward a future where AI doesn't just produce ideas but executes them: surfacing insights in real time, creating and optimizing campaigns and interacting with marketers through natural, conversational interfaces. As AI systems make more decisions, automate more workflows and move at speeds no human can match, the stakes get higher. Without trusted measurement, clear governance and continuous validation, marketers risk mistaking AI confidence for truth — letting automation run unchecked while losing sight of what actually drives results. "We used to joke that, as an analytics firm, our job was to argue with Bob — this generic name for the person who's been at the company longest. Bob's always done it the way Bob does it, and he's invariably correct. That's why he's been there for 20 years. We're either supporting Bob or arguing with him, which is tenuous because no matter how right we might be, we could be undermining someone who has agency over whether we live or die in that relationship." # Dan Temby Senior VP, Technology & Analytics DAC The marketers who will thrive in this next era aren't the ones chasing every new AI capability. They're the ones building solid foundations now: unified data, transparent measurement and teams that know how to question, validate and act on what AI surfaces. This isn't the end of the story. It's the beginning. Disruption is inevitable. The advantage goes to those who are ready for it. "We're in a specific moment where we have a lot of information and tools, but it's difficult to know what's true. That's because there are way too many tools integrated into clients' ecosystems, and the way we measure performance is changing quickly. Plus, there's a lot of pressure for immediate results, but not enough knowledge. Clients need the right partners to guide them through all of it." # Paula Gomez Global Data & Adtech Director Making Science # Demographics In this fourth annual study, we set out to understand the barriers marketers face as they strive to extract actionable insights from their data. We surveyed 238 in-house marketers and agency professionals from July to September 2025. Respondents came from a wide range of countries, company sizes, industries and roles. We also interviewed six agency leaders to understand the particular challenges marketers face in the age of advanced technology and analytics. The quantitative and qualitative research was conducted by Ravn Research, an independent research firm. If you have questions about the research or want to learn more, contact christopher@funnel.io. Region Number of employees 49% I work on an in-house marketing team 51% I work at a marketing agency, advertising agency, or consultancy Seniority Role Want to be a smarter marketer? Follow us on LinkedIn, YouTube and our blog for insights to boost your impact Funnel in