Organisation Intelligence: the missing layer in enterprise AI
- Spark
- 1 hour ago
- 2 min read
Everyone’s shipping copilots and AI agents right now.
But inside most companies, there’s a quieter problem that keeps showing up the moment you try to deploy AI for real: the organisation itself is a black box.
We’ve got systems of record (CRM, HR, finance), systems of work (Jira, Notion, docs), and systems of communication (Slack, email, meetings).
What we don’t have is a system of understanding, a way to answer basic questions like:
Where do decisions actually get made?
How does knowledge move through the business?
Where do projects get stuck (and why)?
Who’s overloaded, and where are the hidden dependencies?
What work is creating outcomes… versus noise?
That context is exactly what AI needs.
The 2026 problem: AI-ready organisational context
The biggest enterprise data challenge going into 2026 isn’t “more data.”
It’s trusted, governed context, so AI can be helpful without being risky.
Because if AI is trained on messy, fragmented, unstructured signals, you don’t just get “imperfect outputs.”
You get confident wrongness, and that’s expensive.
Organisation Intelligence is how you fix that
Organisation Intelligence is the layer that turns day-to-day organisational exhaust into structured insight:
identity resolution across systems
relationships between people, teams, work, and decisions
lineage (where information came from and what it influenced)
permissions and governance by design
It’s not just dashboards.
It’s a living model of how the company works... built from the reality of execution.
Why this matters now
AI is moving from “assist me” to “act for me.”
And the moment AI starts acting inside companies, it needs to understand:
what’s sensitive
who owns what
what’s current
what’s reliable
what’s actually happening
Without Organisation Intelligence, AI is guesswork.
With it, AI becomes context-aware.
Spark is building that system of understanding, so enterprise AI can operate safely, accurately, and at scale.
