AI needs context: Data isn't the problem

Most organizations think more data solves AI's biggest problems, but the real breakthrough comes from something else entirely. What if understanding your data’s context, ownership and connections is the missing piece that prevents inconsistent results and unexpected risks? In this video, we challenge the common narrative around data quantity and quality. AI struggles not because it lacks data, but because it can’t grasp how that data connects across the organization. You’ll discover why context is the secret to unlocking reliable, explainable AI outputs and how bridging the gap between data and meaning transforms decision-making. We break down how to identify and close the “context gap,” leveraging metadata and platforms like Collibra paired with protocols like MCP that enable AI systems to act on governed, connected dat, not just read raw tables. Expect practical insights on building a resilient data foundation that anticipates upstream changes and downstream impacts, avoiding costly misinterpretations and risks. If you’re tired of inconsistent AI results, unexplainable decisions and siloed data, this video is your starting point for much smarter data use. Understanding and acting on data context isn’t just tech talk, it’s your edge in building trustworthy, scalable AI that truly understands your business. More here: https://www.collibra.com/blog/ai-needs-context-why-data-alone-is-not-enough