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Given the sustained attention artificial intelligence (AI) has received and pressure to drive growth and efficiency, it should come as no surprise that today's marketing leaders are placing significant bets on AI. According to new research from TransUnion and UTA Advisory, United Talent Agency's advisory division, there's a high degree of confidence in AI among marketers: Of the 100 senior marketing leaders surveyed in the research, 64 percent reported high confidence in achieving their AI goals, while 89 percent expected AI budgets to increase.

On paper, that's a ringing endorsement of AI's potential to transform marketing. But in practice, confidence and capability aren't the same thing.

While many marketing leaders indicated they're optimistic about AI's future, far fewer reported they have the foundations needed to support it. In the same survey, only 42 percent of respondents rated their teams' readiness to support their AI strategies as high or very high, and just 36 percent said the same for their organization's data and process readiness.

These findings suggest the industry's AI conversation may be focused on the wrong problem. Most attention remains fixed on tools and models, but the research suggests competitive advantage will come from organizational readiness, not new technology. Achieving that requires focusing on three pillars that truly define AI readiness: people, processes, and data.

One of the most common barriers marketing leaders cited had nothing to do with models, platforms, or infrastructure. It was talent. Among survey respondents who rated their organization's overall AI readiness as moderate, low, or not ready:

• 69 percent reported limited AI knowledge across marketing teams
• 57 percent stated a lack of in-house data science and AI expertise
• 46 percent highlighted insufficient training for end users

Put simply, access to AI is expanding faster than many organizations' abilities to use it effectively, especially considering AI usage in marketing has nearly doubled in the last two years, according to "The CMO Survey" released earlier this year.

To be fair, this is a natural stage of market evolution; new technologies disrupt the status quo and send teams scrambling to adapt. The difference with AI is it isn't merely another tool to integrate into the existing operating model. It creates opportunities to reimagine workflows, accelerate decision-making, and automate processes in ways that can fundamentally change how organizations operate. As a result, readiness extends far beyond marketing and becomes an enterprise-wide challenge.

Closing this people-readiness gap requires more than one-time training or software rollouts. It requires building the organizational capability to make faster decisions, scale successful use cases, and generate more value from AI investments.

Marketing leaders who invest in building AI fluency and upskilling across their teams will be better positioned to turn experimentation into sustainable value by minimizing confusion, accelerating time to insight, and putting more power in the hands of the people doing the work.

Most discussions about AI focus on innovation, while operating models receive less attention. However, as AI adoption grows, having solid, repeatable processes in place will only become more important.

A Huble report found that more than half (52 percent) of respondents who rated their organization's overall AI readiness as low to moderate reported limited coordination between marketing, data, and technology teams. Additionally, 45 percent said they lack clear processes for evaluating AI use cases, while another 45 percent said AI usage remains inconsistent or ad hoc across their organization.

For a pilot program, informal processes might be enough. But as usage expands, marketing teams need clear operating models, shared accountability, and repeatable workflows.

Without that foundation, AI adoption can fragment, with different teams pursuing different approaches and successful use cases remaining isolated. That kind of inefficiency can quickly translate into missed opportunities as best practices go undocumented and successful use cases fail to gain traction.

Conversely, organizations likely to gain the most value from AI are those increasingly treating it as a core capability rather than just a collection of individual experiments. They set up clear governance; create shared accountability across marketing, data, and technology teams; and define repeatable processes for evaluating, scaling, and measuring AI-driven performance. In doing so, they build the structure needed to turn successful pilots into positive business outcomes.

If people and processes determine how organizations use AI, data determines whether it works, but for many organizations, data readiness remains a significant challenge. In fact, according to research from Gartner on AI readiness, organizations across industries are expected to abandon 60 percent of their AI projects in 2026 due to a lack of AI ready data. TransUnion's survey findings help explain why: Among surveyed marketing leaders who felt their teams' overall AI readiness was low to moderate:

• 67 percent reported dealing with siloed or fragmented data
• 42 percent cited incomplete or missing data
• 31 percent struggled with data latency issues

Fragmented data is a tale as old as marketing itself, but these findings are particularly important because AI performance is directly tied to the quality of its inputs.

When data is fragmented, incomplete, or outdated, AI models inherit those weaknesses. The result can be inaccurate predictions, unreliable insights, and suboptimal recommendations. There's no denying AI can help organizations operate at greater speed and scale, but if the underlying data foundation is compromised, greater scale may simply accelerate existing problems.

Marketing leaders can close the data-readiness gap by partnering with data and technology teams to create a more connected view of the customer. By breaking down data silos, improving data quality, and ensuring information is accessible when and where it's needed, marketers can provide AI systems with the reliable inputs required to generate trustworthy outputs. After all, AI is only as intelligent as the data it relies on.

Ultimately, these results show AI readiness isn't a technology challenge, it's a people, process, and data challenge. Marketing teams that address these foundational gaps will be more likely to translate AI enthusiasm into real business results. Those that fail to do so risk being left behind.

TransUnion is a partner in the ANA Data, Tech, and Measurement Partner Program.

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