Post by Dean Ward

Too much of the AI conversation is still focused on models, and not nearly enough on whether the underlying data architecture can actually support them in production. This is something we see consistently. AI agents struggle not because of model limitations, but because they lack a complete view of context. Real-time and historical data are separated, and joining them in a reliable, scalable way is still a challenge for many organisations. My colleague and Streambased's CEO Tom Scott articulates this clearly. Without a unified approach to data, you are not building production-ready AI systems, you are building isolated capabilities that are difficult to scale and govern. This is exactly the shift many CTOs and CDOs are now prioritising: moving from fragmented pipelines to architectures that deliver a continuous, coherent view of data. If that is on your roadmap, this is well worth your time: https://lnkd.in/ekufeCh3 #AI #DataEngineering #ApacheKafka #Streambased