Despite promises of a single source of truth, modern data platforms like Snowflake are often deployed for specific departments (e.g., marketing, finance), creating larger, more entrenched silos. This decentralization paradox persists because different business functions like analytics and operations require purpose-built data repositories, preventing true enterprise-wide consolidation.

Companies struggle with AI not because of the models, but because their data is siloed. Adopting an 'integration-first' mindset is crucial for creating the unified data foundation AI requires.

Before implementing AI, organizations must first build a unified data platform. Many companies have multiple, inconsistent "data lakes" and lack basic definitions for concepts like "customer" or "transaction." Without this foundational data consolidation, any attempt to derive insights with AI is doomed to fail due to semantic mismatches.

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.

The core problem for many small and mid-market businesses isn't a lack of software, but an excess of it, using 7 to 25 different apps. This creates massive data fragmentation. The crucial first step isn't buying more tools, but unifying existing data into a single customer profile to enable smarter, automated marketing.

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.

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.

Managing 6-15+ marketing tools isn't just about license fees or lost productivity. This 'tech sprawl' is a hidden strategic cost that prevents a single view of the customer, making personalization difficult and ultimately hindering growth and increasing acquisition costs.

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.