Marketing leaders pressured to adopt AI are discovering the primary obstacle isn't the technology, but their own internal data infrastructure. Siloed, inconsistently structured data across teams prevents them from effectively leveraging AI for consumer insights and business growth.

Many leaders mistakenly halt AI adoption while waiting for perfect data governance. This is a strategic error. Organizations should immediately identify and implement the hundreds of high-value generative AI use cases that require no access to proprietary data, creating immediate wins while larger data initiatives continue.

For years, access to compute was the primary bottleneck in AI development. Now, as public web data is largely exhausted, the limiting factor is access to high-quality, proprietary data from enterprises and human experts. This shifts the focus from building massive infrastructure to forming data partnerships and expertise.

The effectiveness of an AI system isn't solely dependent on the model's sophistication. It's a collaboration between high-quality training data, the model itself, and the contextual understanding of how to apply both to solve a real-world problem. Neglecting data or context leads to poor outcomes.

Companies struggle to get value from AI because their data is fragmented across different systems (ERP, CRM, finance) with poor integrity. The primary challenge isn't the AI models themselves, but integrating these disparate data sets into a unified platform that agents can act upon.

Headlines about high AI pilot failure rates are misleading because it's incredibly easy to start a project, inflating the denominator of attempts. Robust, successful AI implementations are happening, but they require 6-12 months of serious effort, not the quick wins promised by hype cycles.

The primary reason multi-million dollar AI initiatives stall or fail is not the sophistication of the models, but the underlying data layer. Traditional data infrastructure creates delays in moving and duplicating information, preventing the real-time, comprehensive data access required for AI to deliver business value. The focus on algorithms misses this foundational roadblock.

The excitement around AI capabilities often masks the real hurdle to enterprise adoption: infrastructure. Success is not determined by the model's sophistication, but by first solving foundational problems of security, cost control, and data integration. This requires a shift from an application-centric to an infrastructure-first mindset.

According to Salesforce's AI chief, the primary challenge for large companies deploying AI is harmonizing data across siloed departments, like sales and marketing. AI cannot operate effectively without connected, unified data, making data integration the crucial first step before any advanced AI implementation.