Workday has released new research warning that UK employees are losing nearly a full working day each week navigating disconnected AI tools and enterprise systems, creating what the company describes as a growing “Copy/Paste Economy.”
The report, titled The Copy/Paste Economy: Why Task-Oriented AI Is Failing the Enterprise, argues that while AI adoption continues to accelerate across businesses, many organizations are failing to translate those investments into meaningful productivity gains. Instead, employees are increasingly spending time manually transferring information between systems, reconciling conflicting data, and repeatedly entering context into separate AI tools.
“Too many employees are serving as the human middleware between disconnected AI systems,”
Daniel Pell, Vice President and Country Manager, UKI, Workday, said.
While employees remain optimistic about AI’s ability to improve workflows, Workday suggests many organizations are prioritizing standalone AI features without ensuring those tools work cohesively across the broader workplace environment.
Inside Workday’s “Copy/Paste Economy” Findings
Workday’s research found that one in four UK workers spend seven or more hours each week manually copying information between applications, managing inconsistent outputs, or adding context to AI systems that cannot independently access data across platforms.
While more than half of UK employees reported that AI is already helping reduce the time required for certain tasks, those gains are often offset elsewhere in the workday. Employees may complete one activity faster but then lose time switching between tools, validating outputs, or manually bridging gaps between disconnected systems.
The report suggests this operational friction is becoming a major issue for UK organizations. More than 60% of UK workers said they experience “busy but unproductive” days often or very often, significantly above the global average identified in the study.
Administrative overload also emerged as a recurring theme throughout the research. According to Workday, 78% of UK employees are hindered by repetitive tasks, such as chasing down data just to feed it into an AI prompt.
The impact is increasingly affecting employee well-being as well as productivity. Workday found that 77% of UK workers report stress caused by navigating disconnected AI tools and fragmented digital workflows, highlighting how poorly integrated systems are creating additional operational pressure rather than removing it.
Enterprise AI Is Shifting From Adoption to ROI
The findings reflect a broader shift taking place across the enterprise AI landscape. Over the past two years, many organizations have focused heavily on deploying AI tools as quickly as possible, often layering new capabilities onto existing workflows without fully rethinking how systems interact.
That strategy is now coming under increasing scrutiny as businesses look for measurable operational improvements. AI may accelerate individual tasks, but many organizations are beginning to realize that fragmented deployment strategies can introduce new inefficiencies across the broader workday.
Workday’s research highlights the growing tension between task-level automation and end-to-end productivity. A standalone AI application may improve speed in one area, but if employees spend hours manually moving information between systems, the overall efficiency gains become far less clear.
This is becoming particularly important as enterprises expand AI usage across departments, including HR, finance, operations, and customer service. As deployments scale, disconnected workflows risk creating larger operational bottlenecks that ultimately limit the value organizations can extract from AI investments.
The report also reflects a growing shift toward platform-centric AI strategies. Rather than relying on multiple isolated tools, many enterprises are increasingly prioritizing integrated AI platforms that embed automation directly into core systems where work, data, and decision-making already take place.
“The companies seeing the most value from AI are building it directly into the systems where their people, data, and work come together,”
Pell said.
That transition mirrors a wider trend emerging across the technology industry. The conversation around AI is no longer centered purely on access to models or generative capabilities. Instead, organizations are increasingly focused on orchestration, interoperability, and whether AI can reduce operational friction at scale.
The Next Phase of AI May Depend on Integration
Workday argues that the next stage of enterprise AI adoption will depend less on how many tools organizations deploy and more on how effectively those systems operate together behind the scenes.
The findings suggest employees are not resistant to AI adoption itself. In many cases, workers remain positive about the technology’s long-term potential. Instead, frustration appears to stem from fragmented implementation strategies that force employees to manually compensate for disconnected systems throughout the day.
As enterprises continue expanding AI deployments, that distinction is likely to become increasingly important. The challenge for organizations is no longer simply enabling AI usage but ensuring those systems contribute to broader operational efficiency rather than creating new forms of digital overhead.
