By Sebastian Schrötel, Senior Vice President, Product Management, TeamViewer
AI is attracting extraordinary levels of investment. Yet the real question for most businesses is a simple one: when do we actually see a return on that investment?
For enterprise leaders, the answer is not found in model benchmarks or funding announcements. It appears in the daily experience of their organisations. Is AI helping teams operate more smoothly under pressure? Is it removing friction from everyday work?
Technology earns its place when it solves real problems. That principle becomes even more important during moments of rapid innovation. Investment figures may dominate the public conversation, but inside organisations the test is straightforward. Does this make the business run better than it did before?
If the improvement is not visible in day-to-day operations, scale alone will not justify the investment.
Moving beyond the excitement phase
The first wave of AI adoption was driven largely by possibility. What could these systems achieve? How far might they go? Pilot projects were launched across departments, often with genuine enthusiasm.
That phase of experimentation was necessary. It encouraged learning and surfaced new ideas.
Now the environment is more demanding. Economic uncertainty persists, growth forecasts remain cautious, and operational costs are under closer scrutiny. In that context, experimentation alone is no longer enough. Businesses need AI to deliver measurable operational value.
That matters because many organisations are already operating under strain. Hybrid working has added layers of complexity, technology stacks have expanded over time, and skills gaps remain difficult to close.
The result is digital friction. This is everyday dysfunction that interrupts workflows and drains productivity.
Our latest research shows how widespread this problem has become. Four in five employees say they lose time each month because of dysfunctional IT. On average they lose 1.3 workdays to digital friction, while nearly half report delays to critical operations or projects as a result.
In this context, AI should not add complexity. It should remove it.
The organisations seeing the strongest ROI from AI are focusing on a clear objective. They want to reduce friction in how work actually gets done.
From reactive IT to proactive and autonomous operations
One of the biggest opportunities for AI lies in how organisations manage their technology environments.
Many IT operations still follow a reactive model. A system fails, a ticket is opened, and teams respond. The process is familiar but inefficient. Teams spend large parts of their time responding to problems instead of preventing them.
AI enables organisations to move beyond this cycle.
By analysing operational signals across devices and systems, AI can identify patterns that signal emerging issues before they disrupt users. It can also generate and execute automations that resolve those issues earlier.
Instead of waiting for problems to escalate, organisations can address them proactively.
The next step is autonomous IT operations. AI systems can generate remediation steps, create automations, and resolve recurring issues automatically. Routine problems such as configuration errors, system slowdowns, or device inconsistencies can be detected and corrected without manual intervention.
This shift from reactive to proactive and ultimately to autonomous operations is where many organisations will realise the real ROI of AI in e.g. IT operations.
It reduces downtime, lowers support workloads, and allows IT teams to focus on strategic work instead of repetitive troubleshooting.
The role of generative and agentic AI
Recent advances in generative AI extend these capabilities significantly. AI systems are no longer limited to analysing problems. They can generate solutions.
Generative AI can automatically create documentation, troubleshooting guides, remediation plans, automation workflows, and even code. Instead of relying on individuals to manually capture operational knowledge, organisations can generate and distribute it instantly.
Another development gaining attention is agentic AI.
AI agents can operate within defined environments. They can trigger automations, coordinate responses across systems, and resolve IT issues as they occur. Rather than simply assisting employees, these systems can actively participate in maintaining and improving digital operations.
For organisations facing shortages of experienced technical professionals, this capability is particularly valuable.
It allows expertise to scale across the business while helping less experienced employees resolve issues more confidently.
Making work feel more manageable
These capabilities matter because modern organisations are deeply connected. Devices, systems, and people interact constantly. While this connectivity enables scale and flexibility, it also increases the likelihood of friction. Small inefficiencies can multiply quickly in complex digital environments.
AI can reduce that strain in practical ways. In environments besides IT support, such as manufacturing plants, logistics hubs, or field service operations, workers frequently encounter technical issues that interrupt productivity.
In those moments, AI systems can surface relevant knowledge instantly, generate troubleshooting steps, or trigger automated remediation processes.
Each of these improvements helps employees resolve issues faster and with less disruption. Over time, the impact on productivity and day to day work can be substantial.
When employees spend less time navigating systems or searching for context, they regain time and focus.
Work begins to feel more manageable.
That shift may not generate headlines, but it has a tangible effect on productivity and morale. Technology that reduces frustration often proves more valuable than technology that simply looks impressive.
Leadership discipline in an AI era
The pace of AI development can create pressure to move quickly. Yet speed without clarity often leads to fragmented adoption. Leadership discipline is essential. Senior leaders must define where AI can create meaningful operational improvements and where restraint is appropriate. Clear priorities protect focus and prevent scattered investment.
Communication also plays a central role. Employees need to understand how new tools will influence their daily work.
When the purpose of a technology is clear, adoption becomes easier. Broad adoption is essential if organisations want to unlock the value of AI.
A practical path forward
As AI becomes more embedded in daily operations, confidence will matter as much as capability. Organisations must be clear about how these systems are used, where human oversight remains essential, and how the technology is designed to support people in their work.
The next phase of AI adoption will be defined less by experimentation and more by integration. The organisations that gain the most will focus on outcomes. These include reducing friction, automating routine work, and helping employees operate more effectively.
When AI becomes a practical everyday helper across an organisation, adoption follows naturally. As the excitement around AI investment settles, usefulness will ultimately decide what stays. The technologies that endure will be those that quietly help organisations run better every day. That is where the ROI of AI will be found.