Ask ChatGPT or Claude what the highest mountain in the world is, and you’ll have an answer in seconds. You’re a person with a simple question, using an application that connects to an AI model, running on someone else’s servers in the background. Done. Most people reading this will have had that experience, and it works brilliantly.
But that’s very different from what organisations increasingly mean when they talk about deploying AI into their operations.
When the problem owner stops being a person and starts being an organisation, especially one with regulatory obligations, the shape of the problem changes completely. Think of a compliance team reviewing thousands of case files: documents in different formats, containing different kinds of information. “Review” doesn’t mean a yes-or-no glance. Is the file complete? Is it compliant? Can you summarise it? What action should happen next?
You can’t simply hand all of that to a model and expect it to handle the process reliably. You need a workflow, and that’s where enterprise AI becomes a fundamentally different problem from using AI as an individual.
The same five ingredients
However complex the task, the basic architecture breaks down into five elements: a problem owner, the problem they’re trying to solve, an application layer (how the problem is translated into an AI-enabled process), the models that power it, and the compute those models run on.
For an individual, that chain is simple: you, a chat application like ChatGPT or Claude, and their models such as GPT-4o or Claude 3.5 Sonnet and the infrastructure running behind it. For an organisation, the problem itself is rarely straightforward, and the application layer becomes the significant part. It may need to take data in, break a problem into stages, use different models or tools where appropriate, and produce an outcome that can be reviewed and governed.
How that layer is built has a major impact on whether an AI solution can be reused, maintained and scaled.
Three ways organisations approach it
Put the process into the model itself. Some organisations work closely with model providers and give them deep access to business information and processes. That can unlock powerful capabilities, but it raises real questions around dependency and control: who owns that organisational knowledge and what happens when the underlying model or provider changes?
Build the workflow bespoke. This means designing a custom process around particular data, models and business requirements. It offers a highly tailored solution, but it typically takes significant time and investment to build and govern, and if that workflow is tightly coupled to one model, what happens when that model changes or is switched off? Rebuilding from scratch across dozens of similar use cases is where the economics start to break down.
Add an orchestration layer. This approach introduces a layer between the organisation’s workflows and the underlying models and infrastructure, creating greater flexibility as those components change over time.
So, what is an orchestration layer?
At its simplest, an orchestration layer sits between how an organisation defines and runs its AI workflows and the underlying models and computing infrastructure. Orchestration connects the workflow to different models and compute environments while keeping the underlying business process separate.
The workflow and the model therefore serve different roles: the workflow defines how the organisation’s process operates, while the model performs particular tasks within it.
Take the case-file example again. That process might involve categorising documents, extracting information, checking completeness, generating a summary, and recommending a next action. Different stages can use different models, depending on what each task needs. The orchestration layer coordinates that process and provides the infrastructure to monitor, test and manage the workflow as it changes. This is particularly important when a model is replaced or updated.
Why that matters
The real advantage lies in what that flexibility means for the organisation using the models.
If a mature workflow uses one model for a particular task, that model can be replaced without necessarily rebuilding the entire workflow from scratch. The organisation can then run tests to assess whether the change has affected the workflow’s outcomes.
That creates a different relationship with the underlying technology: models become components within a workflow the organisation controls, and the business intelligence stays embedded in that workflow, separate from any individual model.
From AI agents to orchestrated AI
This distinction matters even more as organisations move from simple AI assistants toward AI agents. An AI assistant answers a question, an agent goes further, carrying out tasks and taking multiple steps toward an objective.
However, giving a system more capability doesn’t automatically solve the organisational problem; someone still has to determine how those capabilities fit into the wider workflow. A single case might require an agent to retrieve information, another system to process a document, a model to analyse it, a business rule to be applied, and a person to review the final recommendation.
Orchestration is what coordinates those different components inside a defined, governed workflow. For organisations, the real progression is a move from individual AI capabilities toward controlled, repeatable and reusable AI-enabled processes, well beyond the simple idea of chat bot to better model to better agent. That’s precisely where an orchestration layer becomes essential.
What to look for in an orchestration layer
If you’re evaluating whether an orchestration approach is right for your organisation, look for:
Model agnosticism: can you change models without rebuilding the workflow?
Compute flexibility: can the workflow run across different computing environments?
Workflow ownership: does your organisation retain control of its business processes and logic?
Reusability: can the same platform support multiple use cases without a new bespoke build each time?
Testing and observability: can you see what’s happening inside a workflow and test the impact of changes before they land?
Deployment flexibility: hosted service, your own cloud environment, or your own infrastructure where appropriate?
Accessibility: can teams build and adapt workflows without every change becoming a development or consulting project?
The Foundation Matters More Than the Model
Of the three approaches, orchestration is the odd one out: it acts as the layer that can absorb either of the other two into something more sustainable. You can still use the most advanced model on the market or still write bespoke logic for something unique to your business. Organisations will keep using frontier models, specialised models, traditional software and human decision-making alongside one another; orchestration is what connects those components into workflows that can withstand any single piece changing.
The next stage of enterprise AI is about building the infrastructure that lets organisations use the right model, for the right task and within a workflow they still control.