Moving AI from pilot to production starts with the data (www.techradar.com)

A recent analysis by McKinsey highlights a growing gap in AI adoption within organizations, revealing that while 88% of businesses utilize AI for at least one function, only 7% have fully scaled it across their operations. This discrepancy is particularly evident for marketing teams, where the transition from AI pilot projects—often streamlined with controlled data—to full production environments introduces significant complexity. A major challenge lies in disparate marketing data that is inconsistently defined and scattered across various platforms, complicating the AI's ability to generate reliable insights. To bridge this gap, the establishment of a "knowledge layer" between data management and AI tools is crucial. This layer would standardize definitions of key metrics across platforms and clarify the organizational rules that govern performance measurement. By ensuring AI systems can interpret metrics with accurate context and established definitions, businesses can foster more reliable decision-making and reduce the risk of misleading outputs. As AI models evolve, organizations need to prioritize building robust data foundations to unlock the true potential of their AI investments, moving beyond mere pilot programs to scalable, impactful solutions.