About

Every quarter the models get better, yet most enterprise AI still stalls between pilot and production. The reason isn’t the model. It’s context. As AI shifts from answering questions to operating, the bottleneck has moved. It’s no longer how fast a query runs but whether an agent understands what your data means. Most organisations respond with a semantic layer. It’s a necessary start, but it’s not enough. Real context also includes the institutional knowledge that never made it into a table, skills that steer agents to the right tool for each job, memory that improves with every interaction, and governance that travels with the data. Without it, agents guess, retry and brute-force their way to answers, inflating cost and eroding trust. Ben Sanders unpacks what enterprise-grade context requires, why it compounds when done well, and how leading organisations are building it as a platform capability, not a per-pilot project.

Takeaways

• Why AI pilots stall on the road to production, and why better models alone won’t fix it.
• What enterprise context really consists of (semantics, institutional knowledge, skills, memory and governance), and why a semantic layer on its own falls short.
• The economics of context: why agents without it burn tokens brute-forcing answers, and how domain-specific skills steer them to the cheapest tool that can do the job, so the tenth agent costs less than the first.
• A practical blueprint for treating context as a governed, reusable asset that compounds with use, instead of rebuilding it for every use case.