Research shows a major disconnect: two-thirds of CMOs are confident about AI, yet only one-third feel their data and teams are prepared. This overconfidence leads to stalling in the pilot phase because foundational elements required for scaling are not in place.
AI pilots often succeed in isolation but fail to integrate enterprise-wide. The core issues are foundational: fragmented data in separate databases, siloed functional teams, and human resistance to new workflows. Scaling AI requires solving these organizational problems first.
TransUnion's ability to serve the marketing industry stems from reframing its core business. Being a credit bureau is fundamentally an identity resolution problem—stitching disparate signals together to build a profile. This core competency was directly transferable to the needs of people-based marketing.
AI's initial foothold in marketing is creative development. This isn't a strategic choice but a practical one: marketing teams often have more direct, in-house influence over creative processes compared to media buying or audience development, which are frequently managed by external agencies, making creative an easier starting point.
Using fragmented data to train AI models creates a compounding error effect. Like laying tile from a slightly off corner, a small initial inaccuracy in audience segmentation can lead to massively flawed predictive models and poor campaign performance. The problem isn't the AI, but the flawed data foundation it's building upon.
The true power of AI in marketing is unlocked by feeding it a complete customer view. This requires establishing a common identity key that links disparate data sources like CRM, transactions, and media exposure. This foundational data plumbing is non-negotiable for getting meaningful results from AI.