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.

The company's initial attempt to build an AI Sales Development Representative failed because CRM data was too inaccurate. They realized that any AI application built on faulty data is wasted effort, leading them to focus on solving the foundational data problem first, as AI cannot discern data quality on its own.

AI's value is limited by the system it's built on. Simply adding an AI layer to a generic or shallow application yields poor results. True impact comes from integrating AI deeply into an industry-specific platform with well-structured data.

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.

Instead of building AI models, a company can create immense value by being 'AI adjacent'. The strategy is to focus on enabling good AI by solving the foundational 'garbage in, garbage out' problem. Providing high-quality, complete, and well-understood data is a critical and defensible niche in the AI value chain.

Many leaders focus on data for backward-looking reporting, treating it like infrastructure. The real value comes from using data strategically for prediction and prescription. This requires foundational investment in technology, architecture, and machine learning capabilities to forecast what will happen and what actions to take.

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.

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.

Adopting AI acts as a powerful diagnostic tool, exposing an organization's "ugly underbelly." It highlights pre-existing weaknesses in company culture, inter-departmental collaboration, data quality, and the tech stack. Success requires fixing these fundamentals first.