I want to make one prediction for 2026 that I think will age well.
The enterprises that figure out context will pull away from those that don't.
Not model access. Not prompt engineering sophistication. Not even agent architecture. Context.
What I Mean by Context
Context is the totality of relevant information an AI system needs to be genuinely useful at a specific moment, for a specific person, in a specific situation.
Not generic information. Not static training data. Live, trusted, permission-appropriate context from the systems the enterprise actually runs.
- What is this customer's current contract status, open issues, and recent interactions?
- What is this employee's current project state, team dependencies, and pending approvals?
- What does the audit trail for this transaction look like, and what policies apply to it?
This is not a new problem. It's the knowledge management problem that enterprises have been failing to solve for 30 years.
AI makes it urgent in a way it wasn't before. Because now there's a consumer on the other end that can actually use the context — if it can get it.
Why Most AI Deployments Stall Here
I talk to a lot of enterprises about their AI programs. The pattern is consistent:
They acquire the model capability. They identify the use case. They build the proof of concept. It works beautifully in the demo.
Then they try to connect it to real production data, in real time, with real governance requirements. And that's where the project stalls.
The context layer — getting the right data, from the right systems, with the right permissions, with acceptable latency — is harder than the AI layer.
The Winners Have Already Built the Foundation
The enterprises I see moving fastest aren't necessarily the ones with the most advanced AI strategy. They're the ones that built a strong integration foundation before AI was the priority.
They have APIs for their core systems. They have event-driven architectures. They have a data mesh or at least a coherent data integration layer. They have governance for who can access what, enforced at the infrastructure level — not manually.
They built the context delivery infrastructure without knowing they were building AI infrastructure. Now they're lapping the competition.
The Practical Implication for 2026
If your AI program is stalling, I'd ask one question before you revisit your model selection or agent architecture:
Can the AI get the context it needs, from the systems that have it, in real time, with appropriate permissions, reliably?
If the answer is no — that's your AI problem. Not the AI itself.
Fix the context layer. Everything else gets easier.
Views are personal.