Many pharma companies chase advanced AI without solving the foundational challenge of data integration. With only 10% of firms having unified data, true personalization is impossible until a central data platform is established to break down the typical 100+ data silos.

We possess millions of data points on interventions, but they are useless to AI models because they're trapped in thousands of disparate EMRs in varied formats. The challenge is not generating more data, but solving the human incentive and alignment problems required to create unified data registries.

Contrary to the belief that AI requires perfect, clean data, the biggest opportunity lies in building technology that can find signals in messy, diverse data sets across different modalities and organisms. The tech should solve the data problem, not wait for it to be solved.

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.

A major hurdle for enterprise AI is messy, siloed data. A synergistic solution is emerging where AI software agents are used for the data engineering tasks of cleansing, normalization, and linking. This creates a powerful feedback loop where AI helps prepare the very data it needs to function effectively.

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.

Instead of costly proprietary data generation, Turbine focused on the 'unsexy' work of combining many different public and partner datasets. This capital-efficient approach forced them to build an AI model architected for generalization and data efficiency from the very beginning.

For tools like Harvey AI, the primary technical challenge is connecting all necessary context for a lawyer's task—emails, private documents, case law—before even considering model customization. The data plumbing is paramount and precedes personalization.

The key to valuable enterprise AI is solving the underlying data problem first. Knowledge is fragmented across systems and employee heads. Build a platform to unify this data before applying AI, which becomes the final, easier step.