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.

Tools like Kernel differentiate from multi-provider waterfall solutions (e.g., Clay) by taking direct responsibility for data accuracy. Kernel provides a 48-hour data-fix SLA, eliminating the customer's burden of managing and validating multiple data sources. This shifts the model from a simple tool provider to an accountable data partner.

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.

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.

Before building sophisticated AI models, Personio invested heavily in data hygiene. They deduped their Salesforce instance, where one-third of data were duplicates, and spent months cleaning their prospect database. This foundational work is essential for making subsequent AI initiatives accurate and effective.

A shocking 30% of generative AI projects are abandoned after the proof-of-concept stage. The root cause isn't the AI's intelligence, but foundational issues like poor data quality, inadequate risk controls, and escalating costs, all of which stem from weak data management and infrastructure.

The traditional marketing focus on acquiring 'more data' for larger audiences is becoming obsolete. As AI increasingly drives content and offer generation, the cost of bad data skyrockets. Flawed inputs no longer just waste ad spend; they create poor experiences, making data quality, not quantity, the new imperative.