Building a strong data foundation.
Data powers every aspect of AI growth, but technical challenges often get in the way. Disconnected systems, siloed teams, and poor integration create gaps that limit real-time personalization and erode consumer trust. This creates a challenging cycle — consumers won’t share data without trust, yet data is essential for AI advancement. The stakes are high: 88% of consumers say responsible and secure data handling is important, with 60% rating it as critical.
Max Cuellar, vice president of marketing strategy, performance, and innovation for Adobe Digital Experience, explains: “If you’re piloting AI in a specific organizational cell, you’re often working with a narrow data domain and trying to figure out how to connect it to a broader data strategy. Taking time upfront to structure data at scale unlocks efficiencies, creates synergies, and leads to better outcomes in data management.”
As highlighted in Section 2, executives are making bold moves, with many planning to increase technology and data transformation budgets by over 10% in 2025 compared to 2024. However, key challenges remain — particularly around how resources are allocated as organizations grapple with fragmented data and integration issues.
Figure 15 pinpoints critical gaps. For instance, 33% of organizations report budget shortfalls for essential technology and tools, limiting their ability to connect data. To fully realize AI’s potential, organizations must prioritize funding for AI technologies — often requiring substantial investments — and initiatives that integrate customer data.
Additional barriers further complicate these efforts. Fragmented IT systems (32%) and unclear data strategies (30%) hinder progress, while 24% of organizations do not yet recognize data as a strategic asset.
