Over the past year, “AI-powered” tools have flooded the enterprise landscape. Each promises to automate a workflow, integrate data, or generate insights, often with impressive results. Yet most of them share the same limitation: they work well within a narrow scope but rarely build on what came before. Every new use case starts over, and every project begins with fresh data plumbing.
That constant reinvention isn’t just inefficient – it exposes a deeper issue. Most AI tools aren’t designed around the data they depend on.
Disconnected AI is just band-aids
AI systems don’t create knowledge – they reason over it.
Their ability to reason, and to act intelligently, depends entirely on how well the underlying data is organized, contextualized, and governed.
But in most enterprises today, that foundation doesn’t exist. Data is often:
- limited to one source, leaving the AI with partial context
- stored in silos, with no central visibility or control
- untracked and ungoverned, making trust and auditability impossible
The result is a familiar pattern. Each “smart” initiative becomes a standalone pilot. Data remains disconnected. There’s no shared memory or cumulative learning – only a patchwork of automations held together by short-term fixes.
The knowledge graph: The necessary condition for FAIR AI
Breaking that cycle requires a missing layer between raw data and AI models — a semantic layer that unifies, describes, and makes information reusable before the model ever sees it. That layer is the knowledge graph.
In the context of DevRev’s Agentic AI platform, Computer, this graph acts as Computer Memory - the shared brain where data from across the organization becomes FAIR:
- Findable: every entity, event, and relationship is indexed and discoverable.
- Accessible: through governed APIs and permissions that respect ownership.
- Interoperable: built on open schemas, enabling agents to connect context across systems.
- Reusable: so each AI agent builds upon the knowledge of the last one.
This is not a theoretical luxury. It’s the practical foundation for making AI trustworthy, governable, and cumulative.
DevRev’s Computer Memory: FAIR by design
At DevRev, this principle is not an afterthought – it’s the core of the platform.
Computer Memory, the evolution of the DevRev knowledge graph, ensures that every piece of data entering the system is automatically contextualized, governed, and stored according to the FAIR framework.
This architecture allows developers and system owners to:
- Govern data lineage and access centrally,
- Enable AI agents to act with full context,
- Ensure that every new use case benefits from prior knowledge – not reinvention.
Without that intermediate memory, AI solutions remain shallow at best, brittle at worst.
From point solutions to systemic intelligence
The next wave of AI innovation will not come from larger models or endless integrations. It will come from systems that operationalize FAIR data principles, transforming scattered automations into coherent networks of intelligence.
When every piece of enterprise data is findable, accessible, interoperable, and reusable, AI evolves from a one-off experiment into a living, learning partner.
That is the promise of agentic AI – AI with memory, with governance, and with FAIR data at its core. Because without FAIR data, there is no real intelligence. Only isolated automation.
And when intelligence is grounded in FAIR data, it stops being a black box and becomes an open, collaborative system – one that learns continuously, reasons transparently, and scales trust as much as insight. That is how enterprises will build not just smarter AI, but enduring intelligence.
