Your AI is only as good as what it knows about your organisation
Context is the new competitive edge in enterprise AI. The organisations experiencing measurable impact are not running better models; they are running their AI on richer context. Here, we share what is widening the gap and how to close it.
The productivity gap between people using AI well and those who are not is widening, and it is widening faster within organisations than between them. The difference comes down to how AI has been wired into their work. This is the pattern Ben Chan, Quantium’s Chief AI Officer, described at the recent AFR Workforce Summit, and one we are seeing across our enterprise clients and our own AI transformation
We saw this play out at scale on our US AI Study Tour, where we met the teams building frontier AI inside OpenAI, Anthropic, Google, AWS, Zoom and others. The organisations pulling ahead were not picking better platforms, they were connecting AI to the systems where their work actually lives, so every interaction with their AI platform starts with more context than the last.
The Zoom team shared an example that every leader in the room recognised immediately. An AI assistant with access to 50 prior meetings on a project surfaces tensions, dependencies, and decisions no individual could recall. The same assistant, given a two-sentence brief, produces something competent but shallow.
OpenAI’s team made the same point from a different angle. Among the key success patterns from their own internal transformation was this: connect your data. Not build a better prompt, not select a more powerful model; connect your data. Third-party integrations, first-party Model Context Protocol (MCP) connectors, and a centralised knowledge and data warehouse were the foundation on which genuinely useful internal AI was built. The model is not the differentiator. The context pipeline is.
Where building these integrations once required weeks of bespoke work, the major AI platforms now ship native connectors to the systems most enterprises already run, including Slack, Outlook, Zoom, Google Drive and Atlassian. Each new system you connect makes every subsequent interaction richer than the last.
How to build your context pipeline
- Audit what context your AI currently has access to. Meeting histories, CRM records, and project documentation are the most common gaps.
- Treat meeting summaries as organisational infrastructure, not administrative overhead. When conversations and decisions become structured data your AI can search across, the compounding advantage starts.
- Stress-test any large system of record investment against the next three to five years. If a system does not expose its data to AI through open interfaces, its value erodes.
AI is most useful when it does not need to be briefed. The organisations pulling ahead have made sure theirs rarely is.
If you are working through how to close the context gap inside your own organisation, reach out to our team.