The Secret to Solving the UK's Productivity Crisis? Better business operations

The UK economy is stagnating, and if we want things to change, we need to start talking about productivity. Our lack of 'get up and go' isn't a new problem, it's been festering for over a decade. Between 2011 and 2019, UK productivity grew by just 0.5% per year. Meanwhile, in the US, it grew by 0.8%. Workers have often shouldered the blame for this disparity. We hear that employees are slacking off, or managers aren't, well, managing effectively. But when you dig into the data, a different picture emerges. LSE reported that by 2019, the UK–US productivity gap stood at 28%. Adjust for capital and skills, and it narrows to just 11%.

That tells us that roughly 60% of the difference can be attributed to underinvestment in the technology and operational infrastructure needed to keep up. It's a shortfall that's hitting the economy where it hurts with frozen wages, slow growth, and shrinking competitiveness on the global stage. The real issue is systemic. British firms invest less in capital, skills, and management practices than their US counterparts. The result is a work environment that relies heavily on human effort, leaving teams without the tools or structure they need to make a significant impact.

I work with business leaders across the UK every week to help them get their operations in order. One theme comes up time and time again in these conversations: Leaders want to run leaner, more efficient operations, but the vision blurs when they're asked simple questions. Most struggle to answer the basics: "What are our people working on?, "How busy are they?" or "Do we need more resources?" Leaders can't afford for these questions to go unanswered. But for most, the data they need lies scattered across emails, spreadsheets and siloed systems. Time tracking is limited, visibility is non-existent, and without the ability to aggregate data across teams, it's nearly impossible to get a business-wide view of performance, bottlenecks, or capacity. Leaders are left guessing, while productivity grinds to a halt.

It's 2025, not 1999. With all the technology at our fingertips, the million-dollar question is: Why does manual work still dominate modern services?

The AI problem
Three years since ChatGPT first made headlines, AI has taken centre stage in boardroom discussions, conference keynotes, and transformation budgets. Hailed as the fix-all for slow service delivery, poor productivity and rising costs, it's promised a foolproof way to supercharge efficiency and start making real progress on the UK's productivity problem.

The reality simply doesn't live up to the hype. A recent MIT study of more than 300 enterprise AI projects found that 95% of companies have yet to see any return on their AI investments. Despite over $30 billion spent globally, most organisations are left with little more than chatbots and abandoned pilots that never got off the ground.

The gulf between AI's promise and performance isn't a technology problem, it's an operations problem. Business leaders are trying to bolt AI onto disjointed, manual processes that offer no structure and even less usable data. The systems in place don't track performance in a way that AI can learn from. There's no clear logic for what should be automated or how each step feeds into the next action. In other words, there's no visibility into what actually needs to change, and until that changes, neither will the results.

Processes matter more than you think
The businesses actually seeing results from AI are the ones who get their processes in order before deploying automation. As McKinsey noted in a recent report informed by 50 real-world builds, "Focusing on the workflow instead of the AI agent enabled teams to deploy the right technology at the right point, which is especially important when reengineering complex, multistep workflows." If you want to fix your processes, you need to start with one use case at a time. Pick one common process, for example employee onboarding or invoice processing, build visibility, clean it up, and then look at automating it.

This is where process orchestration comes in. Instead of emails, spreadsheets, and data scattered across six different systems, orchestration means you're using one unified platform. Everything is visible in real time. No more guesswork or silos. Think of orchestration like an orchestra, and your processes as the musicians. For a great performance, every player has to hit the right notes at exactly the right time. Orchestration does the same for operations.

Without orchestration, you end up with what Enate's CEO James Hall calls "operational soup". The work is getting done, but there's no clarity on who did what, how it was processed, or how much capacity you really have. This value is being recognised at pace, with the orchestration global market now worth $7.3 billion and growing more than 21% a year. It's no wonder it's gaining such traction. When you're managing complex services across thousands of people and clients, orchestration is what turns chaos into control

Get your house in order first, then use automation
Once you have the clarity, structure, and usable data in place that orchestration brings, you can finally make progress on your automation strategy. With orchestration, you'll be able to spot where resources are stretched too thin, or sitting idle, giving you the insight to make simple changes that can free up hours of manual effort. This is how you fix your processes. Then, and only then, are you ready to automate. As a rule of thumb, AI does its best work in environments that are high-volume, manual and repetitive.

Some of the most effective ways to use AI in operations aren't flashy, they're practical. For example, email triage tools work as a Harry Potter-style sorting hat and can be used to route and categorise thousands of emails in seconds. Another great one is Intelligent Document Processing (IDP) which can extract and categorise data from documents or contracts at speed. Or, bring in sentiment analysis across your client communications for data on how your customers are feeling in real-time, so you can act as problems arise and maintain excellence. These are a few targeted, high-impact improvements that can save your team time and raise the bar for customer experience.

Encourage GenAI use at an individual level
Not all progress needs a system overhaul. There's real value in encouraging GenAI use at the individual level, especially in low-risk areas like content creation, coding, or project management. At my own company Enate, we've seen the internal benefit of creating an AI task force. We nominated one person from each department to test out various AI tools and share what's working. This gives people the freedom to experiment safely and helps everyone find the best tools to do their job.

If you're resistant, I like to compare it to the rise of the sat nav. When it first came out, some drivers clung to the familiarity of A–Z maps. But imagine how annoyed you'd be these days if your cab journey took twice as long, just because your driver insisted on doing it the old-school way. AI isn't so different. As long as your team uses critical thinking and human judgement, it can be a powerful productivity tool for everyone.

Don't blame workers, fix your foundations
Post-pandemic, work is more complex than ever, and the blame keeps landing on workers. That's rubbish. If we want to fix the UK's productivity problem, we need to stop scapegoating people and start fixing what's actually broken, our way of working, and our operations.

The businesses that'll win aren't the ones burning cash on the shiniest tech. They're the ones who fix their foundations first, then build tech on top that wraps around their business. That's how you move from operational chaos to real, measurable results.