UK businesses are being asked to rethink how they define and measure productivity during the third annual National Productivity Week. Senior technology leaders warn that a narrow focus on “doing more with less” risks overlooking deeper structural issues that limit performance.
The government-backed awareness week has prompted a flurry of comments from industry executives who say traditional productivity measures are no longer worth the pressures businesses face. They point to artificial intelligence, modern business applications, and increased operational volatility as factors that demand a broader and more adaptive approach.
Many people reject the idea of productivity as a simple production metric. Instead, we recommend an ongoing review of workflows, data usage, and skills, along with careful adoption of automation. Others are emphasizing the need for stronger partnerships with specialized providers as organizations face complex technology selection and integration challenges.
Mark Wilson, director of technology and innovation at cloud and digital services provider Node4, said leaders first need to have a clear understanding of the constraints within their operations.
“For businesses, increasing productivity starts with understanding what’s holding them back. Everyone is trying to work harder or ‘do more with less,’ but what does this actually mean? The reality is that every organization is different. That’s why businesses need to step back and look across the company to identify where they can unlock efficiency. It’s not a one-time effort. Your productivity strategy must evolve with your business and keep pace with advances in technology that allow companies to automate simple tasks, enhance connectivity, and integrate systems,” Wilson said.
His comments reflect a broader shift in thinking that treats productivity as an ongoing program rather than a single initiative. Organizations are rethinking their use of data, collaboration tools, and back-office systems to simplify processes and increase production.
Wilson directly tied that change to the use of AI and other software platforms.
“By implementing tools like AI and modern business applications like ERP and CRM, companies can streamline operations, increase collaboration, and begin to build a more connected ecosystem. “However, the long-term benefits will quickly outweigh the concerns. Providing people with the right tools reduces overall business costs, saves time on tedious tasks, reduces workload, and improves overall employee motivation.”
He also argued that many organizations will need external support as they modernize their environments and connect disparate systems.
“For many businesses, however, this is often a challenge to tackle in the short term, and choosing the right partner is often critical. Managed service providers can provide organizations with the technical expertise they need to implement tools and technologies that drive productivity gains, without the stress and complexity that organizations would face if they went it alone. The key takeaway is that when it comes to increasing efficiency, productivity is It’s not just one solution, it’s an ecosystem,” Wilson said.
Manufacturing leaders are looking at this challenge from a similarly broad perspective, but with a greater focus on operational resiliency and downtime. Factory operators face increasing supply chain disruptions, skills gaps and pressure on margins, placing new demands on maintenance and information flows.
“For too long, manufacturing productivity has been framed as a mathematical equation: more output, lower costs, fewer people. Early AI reinforced that idea. But that’s not where the biggest losses or gains come from anymore.
“Today, productivity is defined by how well operations hold up under pressure. The real enemy is not throughput, but variability. Unplanned downtime, supply chain disruptions, inconsistently performed maintenance, hard-to-find information, and critical expertise are exposed.”
“The mistake is not that automation is moving too slowly; we are trying to automate everything all at once. If technicians spend more time searching than solving, they become less productive long before they are capable. AI must remove friction and turn asset data into instant answers, manuals into actionable guidance, and front-line input into repeatable execution.”
“This isn’t about doing more with less. In a high-stakes environment, productivity comes from ensuring that skilled people perform consistently every day, every shift, and every plant,” said Paraic O’Lochlein, vice president of eMaint, a Fluke Corporation brand.
His comments highlight that manufacturers are beginning to evaluate AI not only for cost savings, but also for its impact on maintenance quality, knowledge retention, and consistency of decision-making on the shop floor. The focus is shifting from comprehensive automation to targeted interventions that reduce variation and reduce the time technicians spend searching for information.
Across the UK’s broader business landscape, executives also believe AI has the potential to transform the nature of work by reducing repetitive administrative tasks. This raises questions about training, monitoring, and performance metrics.
“Productivity in UK businesses is often limited by how much time is spent on repetitive administrative tasks rather than finding solutions to more complex problems. AI is starting to change this by accelerating the speed with which teams can quickly move from problems to workable solutions, whether in planning, analysis or day-to-day operations. In effect, it provides an additional layer of support for employees, shortens workflows and enables faster progress from intent to results.” said Josef Al-Sibaie, COO of Syspro.
He argued that the greatest value is created when staff use AI to expand the scope of their roles, rather than simply completing existing tasks faster.
“The real opportunity here goes beyond efficiency. As routine work is automated, employees have more space to focus on critical thinking and more creative problem-solving. Unlocking that potential will depend on leadership that gives employees the freedom to experiment with AI, explore use cases relevant to their roles, and build confidence through hands-on use. Curiosity and shared learning across teams are at the heart of this, and organizations will have more space to focus on critical thinking and more creative problem-solving. “It helps us move beyond viewing workflows simply as tools to answer questions, and instead view them as something that can actively do meaningful work, a way to automate manual tasks,” said Al Sibai.
Al Sibai also cited skills and governance as constraints that will determine whether early experiments with AI will lead to sustainable benefits. Organizations need clearer measures of success and stronger analytical literacy across staff groups, he said.
“However, there are still barriers to address. As well as ensuring employees across the enterprise have the technical skills to take advantage of AI’s full potential, it is also important to know when to trust, challenge, and refine the output. Knowledge is now readily available to everyone, so what truly differentiates them is the ability to synthesize actionable insights from this knowledge. Without these capabilities, productivity gains will be limited.”
“Companies need to define clear business goals and measure the impact AI has on achieving them, whether through reduced time spent on tasks, reduced human intervention in processes, or lower error rates. These quantifiable proof points will serve as a clear reminder of why AI is so powerful and will drive continued excitement across the organization,” he said.
