Your digital twin isn't alive yet. Should it be?

You know that feeling when you're trying to explain your company to someone new, and you realise how much context they're missing? All the unspoken knowledge, the "way things actually work here," the relationships between people and processes that make everything click?

What if we could capture all of that - not just once, but as a living, breathing digital representation that grows and learns alongside your organisation?

We've all heard about "digital twins" - those fancy virtual models that mirror real-world systems. Think of them like a detailed 3D model of a factory floor or a simulation of how your supply chain works. They're useful, but they're basically snapshots.

But what if we could build something more alive? Something that doesn't just copy your business but actually understands it, learns from it, and even helps shape it?

I'm talking about what I call a "Context Mesh" - imagine a web of knowledge that connects every person, process, and piece of data in your organisation. It would know that Sarah in accounting always catches billing errors before they become problems, that the Tuesday team meetings actually happen on Wednesdays, and that when the Manchester office says "urgent," they really mean "sometime next week."

What does it mean to build such a mesh? How can we govern its growth without letting it spin away from human purpose? And how do we ensure it remains a support for our own sense of reality?

What if a twin was only the beginning?

Some readers took my earlier exploration of the “The Ontology Revolution” even further. Paraphrased, they asked: Could we move beyond static models and build on-demand, mutable, machine-readable ecosystems of context? Imagine agents tapping into a live understanding of people, systems, and goals. These agents would act not as the star of the show, but as a supporting layer within a truly human-centered environment.

In such a system, a digital twin might transform from a one-time snapshot to a continuously negotiated and maintained mirror, reflecting and refining the organisation’s collective knowledge. But how would such a system work? And what trade-offs would it demand?

Who is in charge?

Here's where things get interesting (and maybe a little scary). If you have this living digital brain that's constantly learning and updating its understanding of your business, who gets to decide what's true?

Let's say your AI assistant notices that most "high priority" requests actually get handled in 3-5 days, not immediately. Should it start treating future "high priority" requests accordingly? Or should it stick to the official policy that says these should be handled right away?

It's like having a really smart intern who starts making judgment calls based on what they observe, rather than what the employee handbook says. Sometimes they're right, sometimes they're dangerously wrong.

Can we trust autonomous agents to interpret shifting meanings, or should business owners remain the final arbiters of truth? Where is the line between flexibility and fragility? If a living mesh is continuously rewriting its understanding of reality, how do we protect the boundaries that safeguard business performance, compliance, and even safety?

The technology behind the magic

I’m looking to provide a backbone to meaning, a scaffold that helps agents reason without dissolving everything into chaos. Acting within a governance framework but free enough to evolve and mature.

Yet the tension is clear: Some rigidity is there for a reason. We build guardrails in business processes not just to enforce rules, but to protect against runaway feedback loops and the tendency of self-updating systems to chase their own illusions. If a living mesh continually revises its worldview, how do we ensure it does not spin away from the ground truths we still depend on?

The tools to build something like this are actually starting to exist:

• Knowledge Graphs can map out how everything in your business connects - like a family tree, but for all your processes, people, and data.

• Vector Databases can capture the subtle meanings and relationships that are hard to put into words - the "vibe" of how things actually work.

• AI Reasoning Models can help make sense of it all and spot patterns humans might miss.

But here's the thing - just because we can build it doesn't mean we should let it run wild.

Keeping it human

The most important part isn't the technology - it's making sure this digital brain stays aligned with human judgment and business reality.

Think of it like teaching a teenager to drive. You give them the keys and let them learn, but you keep your hand near the wheel and your foot ready to hit the brakes.

We need guardrails. We need humans in the loop. We need ways to say "actually, that's not how we do things here" when the system starts going off track.

What this could look like

Imagine walking into work and your Agentic Enterprise, or you Context Mesh already knows:

• The real deadline for that project (not the one on paper)

• Which vendor actually delivers on time

• That the CEO is in a good mood because the quarterly numbers came in strong

• That the new hire in marketing needs extra support because they're coming from a completely different industry

It's not magic - it's just paying attention to patterns and context in ways that humans are good at, but computers traditionally haven't been.

The road ahead

We're still in the early days of figuring this out. It's like being at the dawn of the internet - we can see the potential, but we're still learning how to use it responsibly.

The goal isn't to replace human judgment with AI judgment. It's to create a system that amplifies human intelligence, helps us make better decisions, and keeps all that tribal knowledge from walking out the door when key people leave.

But we have to be careful not to create a system that's so convinced of its own logic that it loses touch with the messy, human reality of how businesses actually work.

The future of work might not be humans versus machines - it might be humans and machines thinking together, each bringing their own strengths to the table.