2026年营销数据报告：AI无法拯救营销——真正有效的破局之道

Why AI won’t save marketing(and what actually will) AI won’t fix marketing—but a better data foundation will Inlast year’s report,we found that most marketersweren’t really using their data. This year, AI promises tochange that. Our research of 435 marketers from brands andagencies globally shows that there’s been a bit ofa holdup: (1) (2) AI is used where it’s theeasiest—not where it createsthe most value. While everyone is excitedabout AI, most marketers don’ttrust it. Most AI experiments happenin silos and without a clearstrategy. So what’s the problem? It’s the same problem that makes it hard to prove ROI andtrack your performance. Your data foundation is held together with duct tape.This isn’t your fault. You’ve inherited systems builtfor a simpler time. But fixing the problem is morestraightforward than you might think. First,own your data strategy. Over half of marketers(52%) said data decisions were made by external teams.Second, let go of unrealistic expectations. Marketing isabout people, and people are unpredictable. More datawon’t solve that. Third, get your data house in order.AI is only as good as what you feed it. Give it a solidfoundation, and it becomes genuinely transformative. In this report, you’ll learn how to get your data ready for AIanalytics, so you can finally stop tinkering and start takingaction. Table of contents 05Key takeaways 06Adopting AI 14Measuring ROI 25Owning marketing data 35Acting on your data 45Conclusion 46About Supermetrics 47Who we surveyed 6%have AI fully embeddedin their workflow 31%say CMO is involvedin data strategy 52%say external data team definesdata strategy and measurement 36%need to improve marketingdata connection 40%struggle to prove ROIacross channels Chapter 1 Adopting AI Not there yet?You’re not alone. Businesses are being told to use AI in their daily work,but often without the support needed to make it effective.When teams don’t have clear goals, AI use can become moreabout appearances than real results. Adopting a tool just forthe sake of it is a missed opportunity.” Zach Bricker,North American Solutions Engineering Lead,Supermetrics Key takeaways Leadership is pressuring teams to adopt AI,but a lack of trust, strategy and data is gettingin the way. 89%of pressure to adopt AI is comingfrom C-suite and board 6%have fully implemented AI in theirworkflows 39%report concerns about AIdata privacy 37%lack a clear AI strategy fromleadership Chapter 1.1 Leadership excitementabout AIhasn’t translatedinto everyday use Where AI adoption pressure comes from AI adoption hype is coming from the top. Over80%of ourrespondents said they felt under pressure to introduce AI into theirworkflows. 61% Leadership team But just6%say they have fully embedded AI in their workflows.Marketers were most likely (39%) to say they were still“experimenting” with AI. Board of investors28% And, despite C-suite enthusiasm about using AI, marketers don’t feelthey’re getting sufficient guidance about how to use it.37%told usthey lacked a clear AI strategy or vision from leadership, and35%said they lacked training. Direct managers26% Customers20% Chapter 1.2 AI adoption blockers:lack of trust, data andstrategy Less than half of marketers say they have the skills, integrations, anddata to use AI for marketing: •1%have complete trust in AI•17%have high trust in AI•39%have concerns about AI data privacy•37%lack a clear AI strategy or vision from leadership Chapter 1.3 AI is being used where it’seasiest,not most valuable Marketers are mostly using AI for low-hanging fruits:70%forefficiency,61%for automating repetitive tasks. It’s a good start, butalso a missed opportunity. AI has the potential to finally solve the capacity gap that has blockedmarketers fromusing their data to make better decisions.But thatwill only happen if teams start using AI to meaningfully acceleratedata analytics, instead of only generating content and runningautomations. The risk is that AI becomes pigeonholed as nothing more than anaccelerator of basic tasks. That feels like a very real outcome unlessorganizations start thinking more creatively and embedding AI intocomplex, higher-value work.”“ — Marianna Imprialou, Head of Data Science, Supermetrics. Chapter 1.4 Everyone agrees they would see better results with strongeranalytics— but the teams responsible for measurement andmarketing analytics are extremely small. This is where AI could solve areal business need. ”“ AI can help— but only ifyour data’s ready — Marianna Imprialou, Head of Data Science, Supermetrics For teams hoping to get on top of their marketing data and analytics,AI might be a lifeline. Marketing analytics is notoriously understaffed.Even large enterprises struggle with undersized data teams:36%have fewer than five dedicated data and analytics team members,while nearly half of mid-market enterprises (49%) face thischallenge. How would access to better data and analyticsimpact your