Solve for Integration

There’s a lot of noise right now about what AI can do, and plenty of demos to back it up. Models are generating code, writing emails, triaging tickets and summarising documents. It all looks seamless. Effortless, even.

But behind the curtain, there’s a much harder truth: Integration is where everything breaks.

We’ve reached a point where spinning up an LLM is no longer impressive. The tooling is there. The models are fast, cheap, and getting better by the month. Proof-of-concepts are no longer the bottleneck. You can build something clever in a weekend.

But integrating it into the real operational cogs of a business? That’s where the real work begins. This is what people underestimate: AI isn’t useful until it’s embedded. Not as a standalone demo. Not as a separate chatbot. Embedded. In your systems. In your workflows. In the hands of people who are already drowning in legacy tools, compliance overhead, and processes held together by macros, meetings, and hope.

It’s not a model problem. It’s an architecture problem. A workflow problem. A people problem.

Integration is where AI projects stall.

• You discover the data is messier than expected.

• You hit the wall of system sprawl.

• You find out there’s a six month approval process for an API connection.

• You realise nobody actually owns the process you're trying to improve.

And suddenly that brilliant model becomes shelfware.

It’s easy to think the tech is the hardest part. It’s often not. What’s really hard is getting a model to run reliably, securely, and meaningfully inside a live business process that people trust enough to use.

And let’s be honest: most AI "adoption" isn’t adoption at all. It’s experimentation. Which is fine, until someone asks what it takes to scale.

That’s the moment the conversation shifts. Not to accuracy or latency or tool or model selection, but to integration.

• How do we wire this into what already exists?

• What systems does it touch?

• What rules does it need to follow?

• What’s the change impact?

• Who’s accountable when it makes a wrong decision?

Those aren’t technical questions. They’re operational ones. And they’re the difference between a clever demo and actual value.

So if you’re serious about AI, understand your processes, but solve for integration first.

Because the real constraint isn’t innovation - it’s everything that comes after.