Successful AI adoption, especially agentic AI, depends less on model selection and more on strong data foundations: discoverability, quality, governance, and accessibility. Data silos across legacy systems are a major barrier to moving AI from prototype to production. As organisations grant AI systems more autonomy, the cost of poor data quality rises, since autonomous agents acting on bad data can cause larger problems than a chatbot giving a wrong answer. Investing in data architecture and modernisation is framed as more important long-term than chasing the newest models or tools.
Table of contents
Start with the business problemAgentic AI raises the stakesData silos remain one of the biggest barriersThe prototype is only the beginningLegacy data platforms often become an AI challengeFocus on fundamentals307 Impressions