Perhaps the issue is not that AI is creating new sprawl, but that the drive to adopt is exposing existing, disconnected processes as a deeper, more urgent problem.
The explosive growth of AI adoption across enterprises is impossible to ignore, but so is the complexity that’s building alongside it. A recent study from Harvard Business Review suggests that instead of making our workloads easier, AI may actually be contributing to mental fatigue, caused by excessive interaction with AI tools beyond a person’s cognitive capacity.
It raises an important question, how can organizations manage the next wave of automation, without creating even more complexity?
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There is a growing narrative that AI is SaaS’s logical replacement but there’s more to it than a simple swap out. For the technology to be truly transformational, it’s about the implementation.
Through the ease of adoption, AI tools will cause more pain if simply layered on top of current systems rather than rethinking them. This isn’t a specific AI problem, it’s a deployment one and organizations need to confront it by asking, “where is the real friction in the work process, and how can it be eliminated with AI?” Most people today don’t realize how important that differentiation is.
The productivity gap that AI alone won’t fix
To be able to address this challenge, businesses need to properly understand the issue at hand. This starts with looking closely at where time and money is actually going, but reading through this data can be an uncomfortable task.
UK businesses are losing billions of pounds every year to “shadow work” – manual tasks outside of people’s core jobs that are exacerbated by disconnected systems. UK employees spend hours a week on this kind of non-core work, and this is only set to increase, leading to a very real problem. Every employee, team and seniority level is affected by this structural drain on output.
Whilst most businesses don’t or only partially automate tasks like planning trips and submitting expenses, these recurrent weekly friction points build up to have a quantifiable effect on the employees time for their real work which drives impact. The drive to adopt the technology is there and for leaders, automating this work should be a high priority but only when clear plans are in place for how to do so.
The fundamental problem is that shadow work emerges because systems are unable to communicate with one another, and just layering AI tools on top won’t fix the problem. Often, the biggest roadblock to solving the issue in the first place is poorly integrated legacy systems.
This misalignment is draining employees productivity and satisfactions and harming business growth.
2026 – the year of stack rationalization
A true turning point may start to happen when businesses start cutting the amount of tools they have rather than adding more. For years, the cost of complexity has been gradually escalating. It’s getting harder to ignore. The argument for simplicity is not just significant but essential when workers are managing shadow work, using multiple tools which are often not properly integrated.
Previously, increasing the efficiency of individual systems was the goal of enterprise automation. Payroll ran itself and IT provisioning took place in a matter of seconds. However, the coordination, approvals and administrative work that spans operations, finance and human resources were mostly ignored.
Adding more tools won’t unlock the next wave of productivity. It will only come by integrating intelligence into the operational core, where people and systems come together as one.
When simplifying your tech stack is done well, it doesn’t mean doing less. It involves being deliberate, putting simplicity ahead of growth and placing people before software sprawl. The CIO’s with the most self-control to base their success on what they remove will be the ones leading the next generation.
The productivity gap won’t close itself
The issue for many organizations isn’t the lack of technology, it’s that systems and tools are misaligned. The automation is there, but in ways that have enhanced systems without freeing up those who use them. AI won’t automatically address the shadow work issue, and it won’t go away on its own.
A change in perspective is now required, to move away from just pursuing the next goal and towards creating workflows that really benefit the individuals performing the task.
When complexity is outpacing productivity, the real measure of progress won’t be calculated on the volume of AI deployed within the business, but instead on how much friction an organization has managed to remove.
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