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.

The primary barrier to deploying AI agents at scale isn't the models but poor data infrastructure. The vast majority of organizations have immature data systems—uncatalogued, siloed, or outdated—making them unprepared for advanced AI and setting them up for failure.

The NCIC, a key FBI database for warrants and stolen vehicles, is more like a daily CSV file than a real-time system. This lag, combined with a lack of data integrity protocols, means outdated information, like a recovered rental car still listed as stolen, persists and puts civilians at risk.

Contrary to popular belief, law enforcement in the U.S. fails to solve the majority of homicides. The national average clearance rate is only 40%. The situation is even worse for non-violent crimes like car theft, where offenders have an 85% chance of getting away with it entirely.

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.

With no default data-sharing protocols, police agencies resort to primitive methods. The first step up from nothing is emailing PDF bulletins. More advanced groups create private Slack or WhatsApp channels for real-time collaboration, despite the data retention and security risks of using consumer tech.

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.

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.