All engine, no steering: Why orchestration is the key driver for AI

The productivity crisis in the UK seems to be a never-ending problem. Recently, The Telegraph reported on the “almost unprecedented” plunge in productivity seen over the past five years, now reaching the worse point since the financial crisis of the 1970’s. Consequently, and quite rightly, governments and employers are searching for solutions, and AI is often seen as the answer to bridge that gap. 

But while, AI can undoubtedly offer a way to turn the tide and speed up some tasks in isolation, expecting it to work a miracle by simply plugging it in and watching it go, is completely shortsighted. 

Before even considering introducing AI, it's essential to explore the very thing that enables AI to work coordinate and connect all the elements together for a smooth process: Orchestration. Think of orchestration like an orchestra, carefully pulling the strings so that every business process works in tandem. It’s a cohesive way of keeping all your processes under one roof, so you can see what’s working, and where there’s room for improvement. After all, a broken process with a layer of AI on top is still broken.

What does orchestration do?

In greater detail, orchestration brings all the elements of your operation together and helps processes run smoothly. It binds your people, systems, and automation efforts together in a single platform to run all your service lines through. With orchestration in place, you’re never left in the dark about visibility again as you can see and manage everything in one place. For organisations running across multiple markets, service leaders can access operational metrics in real time, gaining granular information about how teams in different markets are tracking against each other. This insight helps to identify gaps, broken processes, areas needing more resources, and where you can scale back. Without this insight, implementing automation at any point is an expensive gamble.

Fix your operational blind spots

One of the most common challenges I hear from businesses, is not having full visibility of their operations. You wouldn’t start driving a car if you couldn’t see out of the windscreen, so how can you fix operations if you don’t know how work flows through your organisation? More importantly, how can you apply AI in a meaningful way?

I see it so often with service leaders who have attempted to implement automation, expecting to see immediate changes in efficiency. However, work still gets stuck, errors persist, and teams remain bogged down in firefighting and manual work. But why?

The answer is simple, because they haven’t addressed the root cause of the problem. It’s the inefficient, unstructured processes are causing the issues, and yet they haven’t been addressed, because service leaders cannot see them. Sure, AI is very smart, but without visibility, it’s just a fancy widget. AI needs a clear framework to function effectively, which is exactly what orchestration provides.

Know your use case

In terms of progress, I suggest gaining control of your processes first, using the data you have to optimise and improve, and then start automating based on that orchestration data. Otherwise, you risk automating what your noisiest colleague wants, rather than something that will bring material value.

Think deeply about your business and how your processes currently look. Do you use multiple tools? Are you working across multiple markets? Some of these probably work fine as they are, but you’ll probably also have some that repeatedly cause headaches and are full of bottlenecks and inefficiencies. It’s these use cases that are the most tempting to stick an AI bandage on, however introducing AI into that environment only amplifies the chaos. Bad processes don’t become good just because AI is involved; they just move faster in the wrong direction.

The true benefit of AI can be seen when automating repetitive tasks, extracting insights from data, and making predictions. But it needs structure, you can’t just plug and play and hope for the best. If processes are fragmented, AI won’t know where to apply itself effectively. That’s where orchestration comes in. Orchestration is the hyper organised project manager that ensures work is co-ordinated, data flows smoothly, and automation occurs in the right place at the right time.

The exception to the rule

Although I believe anyone running a sophisticated service should orchestrate before automating, other business roles and departments can safely adopt AI right away.

If your job involves creating, whether as a Graphic Designer, Coder, or Copywriter then using generative AI is a low-risk move. Even in our own organisation, our content team leverages AI for proofreading, and our coders use AI to speed things up. These teams already have structured workflows with built-in testing, quality control, and approvals, making AI a no-brainer for automating repetitive tasks. It’s like having your own (affordable) personal assistant.

However, if you’re running a large service, you’re dealing with a different level of risk and that’s where orchestration becomes essential. It ensures AI and human teams work together cohesively while keeping humans in the loop to maintain full control over customer interactions.

AI has huge potential, but without orchestration, you’re building a tower block on unstable grounds. The fact is, without control over your processes, AI won’t solve your problems. By getting your operations in order first, AI can move from promise to performance.