Every technology leader knows the gap between the AI roadmap and the architecture required to support it. The initiatives are scoped, the budget is committed, and the ambition is entirely reasonable. What the roadmap often doesn't account for is whether the data foundation underneath is ready to deliver.

Accenture and Klaviyo explore why fragmented customer data architecture is one of the most expensive and underestimated obstacles to AI performance in enterprise B2C organizations, and what it takes to close it.

According to Accenture research, 70% of tech budgets still support legacy systems that slow the flow of information, and 50% of companies lack the internal resources to build their own AI agents. Organizations that address the underlying fragmentation by unifying customer data on a shared, real-time foundation are better positioned to get reliable outputs from AI, free up engineering capacity, and build the kind of architecture that scales with confidence.

Getting the architecture right is the prerequisite for AI that performs, not a workstream to address after the models are already in production.