AI models fail in business applications because they lack the specific context of an organization's operations. Siloed data from sales, marketing, and service leads to disconnected and irrelevant AI-driven actions, making agents seem ineffective despite their power. Unified data provides the necessary 'corporate intelligence'.
Related Insights
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.
Off-the-shelf AI models can only go so far. The true bottleneck for enterprise adoption is "digitizing judgment"—capturing the unique, context-specific expertise of employees within that company. A document's meaning can change entirely from one company to another, requiring internal labeling.
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.
Before deploying AI across a business, companies must first harmonize data definitions, especially after mergers. When different units call a "raw lead" something different, AI models cannot function reliably. This foundational data work is a critical prerequisite for moving beyond proofs-of-concept to scalable AI solutions.
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.
Research shows employees are rapidly adopting AI agents. The primary risk isn't a lack of adoption but that these agents are handicapped by fragmented, incomplete, or siloed data. To succeed, companies must first focus on creating structured, centralized knowledge bases for AI to leverage effectively.
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.
Many companies focus on AI models first, only to hit a wall. An "integration-first" approach is a strategic imperative. Connecting disparate systems *before* building agents ensures they have the necessary data to be effective, avoiding the "garbage in, garbage out" trap at a foundational level.
