The Role Of Machine Learning In UK Employee Productivity

Conor Donohoe

Part 1 in a 3-part series. Read Part 2 and Part 3.

Though it probably doesn’t feel this way to those who spend their lives running between meetings, dealing with customers, or negotiating with suppliers, the UK isn’t working hard enough. Or at least it isn’t working smart enough.

UK productivity—how much all of us produce over a year divided by how many hours we spend doing it—lags France, Germany, and the U.S. by up to 30%, according to the Office of National Statistics. And it’s not just the G7’s most productive three countries that outperform the UK. Irish, Spanish, Belgian, and Dutch workers all significantly outperform their UK counterparts.

We should be freeing people to focus on adding value, not restricting them to transactional tasks that are better and more quickly performed by automation. The power to let machine learning tools comb databases and user behaviour to identify productivity gains is available now—but too often it’s not even in companies’ toolkits.

But we’re not just performing worse than our chief competitors; we’re underperforming historically, too. Not only are we producing less per person than our closest competitors, but we still haven’t regained the ability to become ever more efficient, which was the case prior to the 2007-08 financial crisis.

We have the technology

In the last quarter of 2016, the output of the UK economy per hour increased by just 0.4%. Before the 2007-08 crash, we were increasing how much we produced by up to 5% per year. That adds up to tens of billions of pounds of lost income for businesses, consumers, and government.

Some sectors are feeling the crisis in productivity more acutely than others. Two-thirds of the slowdown comes from banking, telecoms, utilities, management consulting, and legal and accounting services, despite these sectors accounting for less than 12% of the economy.

Members of Parliament on the House of Commons Science and Technology Committee argue that businesses are surrendering productivity—and therefore competitive advantage—by not fully leveraging the data available to them.

In their 2016 Big Data Dilemma report, MPs write: “Despite data-driven companies being 10% more productive than those that do not operationalise their data, most companies estimate they are analysing just 12% of their data.” MPs went on to warn this has “massive implications” for UK PLC.

The productivity gap between pre-financial crisis trends and today’s business landscape is clearest in services industries. Finance in particular has increased staff numbers to cope with tougher regulatory and reporting demands. But since the crisis, there has been a digital revolution in which machine learning can evolve processes, increase automation, and reduce complexity. We should have the insights available to us via intelligent cloud ERP’s analytical power provided to us in context and when required, because a machine has learned that’s what we need.

The possibilities are endless

The Bank of England’s chief economist, Andy Haldane, believes our leading companies are becoming more productive while a long tail of mediocre performers are a drag on the overall figure. The Economist Intelligence Unit research, which found 29% of executives are finding productivity gains in cloud computing, would suggest he’s on to something.

The core of cloud computing’s power is that it enables the automatic and continuous identification and fixing of wasteful processes. The same processes that are a drag on productivity and competitive advantage at your company can be systematically honed and improved.

The possibilities for machine learning touch all businesses and all sectors of the economy. Those that act first are going to gain huge advantages over their rivals:

  • Manufacturers will enjoy predictive failure analysis, preventive maintenance, anomaly detection, and condition monitoring.
  • Retailers will leverage inventory planning, recommendations, and upsell/cross-channel marketing.
  • Healthcare providers will access alerts, diagnostics, and proactive management.
  • Travel and hospitality firms will deploy aircraft maintenance and scheduling, customer-complaint resolution, and dynamic pricing.
  • Financial businesses will employ risk analytics, fraud detection, and customer segmentation.
  • Utilities will employ smart grid management and power-usage analytics.

All these improvements mean firms with machine-learning savvy will offer better, cheaper products and services that more closely meet customer demand and are delivered more efficiently than their rivals can match.

The question can no longer be, “Can machine learning unlock productivity gains?” It must be, “Am I going to let my competitors beat me while I do nothing?” Find out how SAP can help you to unlock innovation for your business today.

Conor Donohoe

About Conor Donohoe

Conor Donohoe is an ERP consultant for SAP S/4HANA Cloud at SAP. He is a qualified chartered accountant, with first-hand experience of how technology can drive business change. Trained in a Big Four accounting firm, he is experienced in advanced analytics and reporting within the professional services, banking, and pharmaceutical industries. Conor comes from the ERP user side, so understands the challenges ERP users can encounter with their systems – and where real gains can be made.