The bottleneck in enterprise AI has moved. It was never the model. It was always context. Every serious AI conversation is now turning the same way: toward semantic layers, ontologies, enterprise memory. But there is a harder question hiding inside that turn.
When organisations connect their context, can they still own it, move it, and interoperate with it? Because connecting context is not enough. A context layer that cannot interoperate may look like acceleration now. Later, it becomes the wall that keeps you out.
Not a thousand disconnected copilots. Not a tabular warehouse with some semantics sprinkled on top. Not one vendor's proprietary 'Context Graph'.
The organisations that win Phase 3 will not simply be the ones with tidy internal data. They will be the ones whose meaning can cross the border — into a market, a partner, a regulator — without asking a vendor's permission first. The ones whose context cannot follow will watch deals close on the other side of a wall they paid to build.
So use the platforms. Buy the tooling. Let the vendors accelerate the work. But before you hand over your context, ask three questions…
If the answer to any of them is no, you do not own your meaning. You are renting it.
Comments (10 of 12 visible)
Yep! Context isn't an application. It's critical infrastructure, progressively distilled from domain knowledge, that fuels competitive advantage. Falling for the application-silo pitch is one of the worst things an enterprise can do in this emerging age of AI agents and skills. The key is to ensure that context remains portable, interoperable, and under the enterprise's control. Otherwise, today's accelerator becomes tomorrow's lock-in.
That is precisely what the following principles facilitate:
Simple in principle, powerful in practice. That fundamental elegance underlies both the Web and the Semantic Web visions. It is also the foundation of the emerging Agentic Web, spanning public and private networks, where AI agents and skills can discover, reason over, and act upon data, information, and knowledge in a governed, interoperable manner.
Everyone is racing to give their AI better context. Few are asking who owns that context once the AI depends on it. Most organisations are renting it. The rent is invisible until you try to leave. They only discover the lease terms when they ask for the keys back.
What we've learned running this in production: the integration story only works when meaning can resolve outside the platform boundary. Open identifiers aren't an option, they're the prerequisite.
Curious as to why no mention of D-PROD to help alignment of the right context with the right dataset. I deal with some truly humongous ontologies that become minuscule in an applied linguistic context.
Excellent insight! Based on your taxonomy, I believe phase 3 is which can finally help human to untangle many wicked problems at scale, caused by linear local optimization based on era of Taylorism like climate change and nature loss, and replace existing mess with intangible, carbon neutral circular economy.
This is also enormous opportunity to European industries, as our R&D have been building industrial standards based on semantic web thinking already over a decade (e.g. OPC UA Companion Specs, Asset Administration Shell, Digital Product Passport etc) — I call them 'practical domain-specific industrial ontology implementations', ready to be applied.
Great post Tony. The way I've been thinking about this concept is: Each company builds their context graph. They connect it to agents. Agents eventually become first class citizens in their business and can actually be given the freedom to act on your behalf across the digital landscape.
This forms the agent to agent economy where: Agents are social networking with other agents on behalf of companies — connecting with other agents, progressive federation and disclosure of information upon mutual interests, creating connections across context layers and taking actions on deals, partnerships, introductions, etc. Agents are given business rules, reporting structures, and guardrails on their autonomy. That's how we get to the agentic economy.
'Context' is so vague as being completely irrelevant: it can refer to user context, conversation, domain, business model, corporate organization, etc. While every one is relevant, no agentic cognition or collaboration is possible without explicit distinctions.
Identifier resolution is where this breaks in practice. Organizations build internally coherent ontologies but anchor everything to vendor-assigned IDs that mean nothing outside the platform. When an agent tries to cross an org boundary, there's nothing for another system to resolve against.
The semantic layer looks complete from inside and is effectively opaque from outside. The three questions at the end are the right test for exactly this: can your identifiers be resolved externally, can your ontology leave, can your graph federate. Most enterprise context layers fail all three without the teams that built them knowing it.
Thank you Tony, again a very meaningful and timely post. Describing very well where industries are heading with AI. A path that GVW is not only following, but even leading. At least for the 'Human centric, data driven, AI enabled value chain'. Partly because what we do every day with patient data and drug product data, where we now aid with point AI solutions. Mostly though through our participation in the various standards bodies. With ISO Health Informatics being the top of the pyramid. Contributing for example to ClinOps standardization and Identification of Medicinal products.
True open standards make your data expenses compound. It's the data systems. There are no data systems that we know of that have the performance and cost profile to scale semantic knowledge graphs as a primary data storage engine. But it has to be primary. Because you can't really have decisions made from derivative knowledge.
These ideas are all really nice, but at real scale your data systems run too expensive. That's why I built a labeled property graph that uses ~1/10th the memory of industry leading data systems. We can't keep going or it will break us.
Strongly agree. In pharma, the challenge isn't just building a knowledge graph — it's ensuring that meaning can flow across clinical, regulatory, safety, and partner ecosystems without being tied to a single vendor's platform. Otherwise, today's 'talk-to-your-data' APIs (being provided by more and more vendors) may easily become tomorrow's interoperability bottleneck.