AI Won’t Improve Your Marketing Until You Improve Your Data

Artificial intelligence has moved from experimental to operational in marketing. Today, AI writes content, recommends products, personalizes experiences across platforms and channels, and automates workflows across the customer lifecycle. However, despite this rapid integration, the outcomes often don’t meet expectations.

A recent industry report shows that while 73% of enterprises have adopted AI technologies, only 52 % are realizing the results they had anticipated. This signals a fundamental disconnect between deployment and performance. The culprit is not model design or computing power; it’s data quality.

When organizations feed AI, fragmented, null, outdated, or “bad” data, the outputs reflect those imperfections. Flawed data not only reduces accuracy, it introduces bias, accelerates drift, and undermines customer trust. For marketing teams depending on AI to improve efficiencies while delivering scaled personalization and growth, this is a critical failure point.

Infrastructure Determines Intelligence

AI is a system that learns by example, and its effectiveness is directly linked to the structure and reliability of the data it receives. If a company’s systems interpret “Chris Smith,” “Christopher Smith,” and “C. Smith” as three separate individuals, the model cannot generate cohesive insight. It will produce predictions and analytics that appear informed but lack context without unifying data points to create a singular profile. This profile is formed by synthesizing business and consumer data, online and offline behavior, to gain a 360-view of the individual, wherever they are, however they interact with the brand.

This issue is more common than some may think. According to Forrester, nearly a third of global marketing leaders cite data silos as a leading obstacle. When data lives in disconnected systems, like email marketing platforms, CRM tools, ecommerce engines, etc., it becomes almost impossible to link behaviors across touchpoints. This not only confuses AI systems, but it also prevents businesses from answering basic questions about customer value, loyalty, or intent.

In short, continuity in data is required before consistency in engagement can be achieved.

Readiness Is a Strategic Decision

The pace of AI investment often exceeds an organization’s technical maturity or workforce aptitude. Marketing teams are under pressure to integrate generative tools, deploy real-time personalization, and reduce reliance on traditional segmentation, but these capabilities require strategy, infrastructure, and a knowledgeable team that can offer human oversight for them to be effective.

According to IBM, 68% of CEOs now view enterprise-wide data architecture as a critical enabler of cross-functional collaboration. Another 72% say proprietary data will be central to capturing value from generative AI. These leaders understand that meaningful AI outcomes require both experimentation and operational discipline.

When companies attempt to layer advanced AI models onto fragmented systems, the result is inefficiency at scale. AI cannot course-correct if the information it receives is inaccurate, so it might accelerate, but it may not be moving in the right direction with the desired clarity.

It is also true that AI, as it stands currently, is not a holistic solution for marketers’ needs. This leads to using one AI model for one task, a second for another, and so on, creating another challenge in gathering cohesive insights if the AI models aren’t communicating. Outputs become something of a patchwork quilt that needs to be stitched together to get a complete view of the whole AI ecosystem.

Volume Without Structure Produces Noise

Many marketing teams focus on data collection, expanding their pipelines to capture more first-party signals, more engagement metrics, and more transactional detail. But without orchestration, more data simply compounds the problem.

Real value comes when data is organized, contextualized, and connected in real time. That includes zero-party preferences, first-party behaviors, second-party partnerships, and third-party enrichment. Each plays a role in customer understanding. Most importantly, all of this data needs to come together to create shared identifiers.

Google and Econsultancy research shows that 92% of leading marketers consider first-party data essential to growth. But even high-quality data loses value if it can’t be interpreted within a broader view of the customer journey. Another study found that 72% of consumers are more likely to engage with brands that understand their full identity. This requires systems that can reconcile records across time, channels, and formats.

 Identity Is the Enabler

AI cannot personalize what it does not recognize. Identity resolution remains one of the most technical—and most overlooked—aspects of modern marketing. A persistent customer identity allows models to associate behavior with individuals, not just sessions or devices. It creates the continuity required to track evolving preferences, detect anomalies, and anticipate needs.

Effective identity frameworks rely on clean data and consistent logic. They are not achieved through acquisition alone. They require matching algorithms, data governance, and real-time behavior reconciliation. When implemented correctly, they give AI the clarity needed to generate outcomes that align with customer expectations.

Without a unified identity, personalization breaks down. AI defaults to irrelevant messaging, redundant touchpoints, and inefficient bidding. These aren’t just surface-level annoyances. They erode trust, reduce ROI, and stall progress.

Data Hygiene Is a Marketing Imperative

Historically, marketing teams could rely on IT to manage back-end systems while focusing on creative and strategy. That division no longer applies. To succeed with AI, marketers and data scientists must understand how data moves, where it breaks, and how to resolve inconsistencies at scale.

This includes validation, deduplication, metadata alignment, and governance protocols that enforce quality. It also means establishing clear taxonomies, managing version control, and building systems that can adapt as new signals and platforms emerge.

While this work may seem operational, it is increasingly strategic. It ensures that AI outputs are grounded in fact, not noise. It allows teams to test, learn, and iterate with confidence. Most importantly, it ensures that customer experiences feel coherent, relevant, and respectful.

Marketing’s Future Depends on Data Leadership

With the pace of AI investment expected to double over the next two years, marketing organizations must move quickly to build structured, governed, and accessible data environments. Competitive advantage will not come from model sophistication alone. It will come from the ability to deliver insight at speed across every customer interaction.

At Data Axle, most of the clients I speak with are focused on building a central data lake with common identity across all of their data.  This allows AI not only to drive insights but make them actionable as well.

The gap between AI ambition and AI performance is widening, but there are steps brands can take to bridge the gap, starting with teams who understand that the real engine behind intelligent marketing is clean, connected, compliant data. It’s not going to happen overnight, but with investment in upskilling to build employees’ understanding of AI tools and best practices and the power of data, it lays a strong foundation for successful AI implementation.