The news: AI has become standard across marketing organizations, but widespread adoption hasn’t eliminated problems with data quality, unreliable outputs, and workforce readiness.

• 100% of marketers surveyed by Epsilon are using AI, per an August study.
• But 45% of respondents say that data quality—namely incomplete data feeding AI models—is their top technical challenge.
• 39% struggle to keep AI-generated assets compliant with brand guidelines; 32% point to issues with incorrect, inconsistent, or unreliable AI outputs.
• 47% cited difficulty training existing staff to work effectively with AI tools, while 45% report resistance to organizational change.

Why it matters: Epsilon’s findings reflect a broader AI readiness problem. Adoption is moving faster than the data, processes, and skills needed to make the technology effective.

• 42% of marketers reported that incomplete or missing data directly limits their ability to onboard broader agentic workflows that would help save on media costs and improve ROI, per August research from TransUnion and UTA Advisory.
• 70% saidcross-channel blind spots made it difficult to understand AI’s impact on the customer journey; despite this, some 64% are confident that their organization will achieve AI-enabled marketing goals.
• That confidence could be tempered by ongoing issues; 76% of global marketing leaders already spend at least three hours weekly editing, fact-checking, or correcting AI outputs, while only 4% say that AI saves them time at every stage of the marketing process, according to a June Optimizely survey.
• A major training gap is limiting AI effectiveness. Sixty-nine percent of marketers say their companies “strongly encourage” AI use, but 43% have never had formal training on it; of those who have, only 10% say the training was adequate, per Adweek and NewtonX.

Recommendations for marketing leaders: Focus less on accelerating AI adoption and more on building the foundations needed to make existing use effective.

• Invest in data infrastructure and governance. Clean, connected, and well-governed data systems will ensure AI agents have access to quality customer data. Without reliable data, AI is more likely to act on flawed inputs that undermine performance and trust.
• Prioritize training and upskilling. Push for company-provided resources that will close the competency gap, as most marketers are currently self-taught and lack format support.

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