Recent reports show that the UK is lagging behind the US, France and Germany when it comes to productivity, mainly due to a lack of investment in capital skills. In the Spring the ONS reported that productivity levels have slipped by 0.2% over the course of the last 12 months. But there are signs of an uplift. A recent PWC study shows that workers in “AI-exposed” sectors are experiencing a boost in productivity.
However, we shouldn’t get carried away just yet. This boost hasn’t reached the DevOps teams who are responsible for managing the vast cloud computing estates at UK firms, despite huge investments in AI agents. From working with some of the biggest FTSE 100 organizations, we’ve been able to ascertain that many of these professionals are still struggling with repetitive tasks, like addressing system errors, failures, and data breaches. This is causing a major distraction, consuming time and resources that could be spent on adding value to the business.
Managing complexity
The issues are linked to the legacy monitoring tools that many enterprises have in place. They’re designed to detect failures and anomalies, triggering alerts that draw attention to potential problems before they escalate. The problem is that companies with multiple cloud environments tend to generate thousands of alerts, making it difficult for DevOps teams to distinguish between real issues and false positives. Teams are finding that rather than streamlining processes, AI agents are increasing the workload by triggering more alerts while offering no resolution. DevOps professionals need agents that assist in addressing problems, rather than flagging them.
Cloud systems are constantly evolving and becoming increasingly complex. To stay ahead of this complexity and the changes that occur, AI agents need access to the telemetry data that underpins these changes. By using this data, users can respond to issues with precision and efficiency. An approach that will improve incident response and remediation, significantly increasing productivity in the process.
Closer collaboration
This type of capability transforms AI agents into true co-pilots that become active participants in diagnosing and resolving issues, rather than being passive observers. This dynamic also alters how teams operate, letting the AI agents manage the heavy lifting involved in incident triage, while leaving users more time to dedicate on improving things rather than simply reacting to problems as they arise.
Recent developments in AI modelling have allowed agents to better communicate with telemetry systems. This has led to AI agents driving cross-team collaboration, especially during incidents. Their ability to remain active during an incident, offer guidance and support, while promoting collaboration. Crucially, these agents aren’t there to replace developers. Instead, they reduce friction, enabling teams to move faster, troubleshoot more effectively, and concentrate on building better systems instead of just maintaining them.
Cutting through the noise
However, developing AI-native systems that improve DevOps productivity requires more than simply adding a chatbot or incorporating AI as an afterthought into your observability stack. It involves integrating AI agents into daily workflows and giving them access to clean, structured data. Once they’re equipped with this data, they’ll be able to make recommendations and, eventually, act on that information.
DevOps teams also need to have the confidence that when the AI flags a malfunction, it should be taken seriously. Similarly, if it offers a solution, they should allow the AI to try and resolve the issue. Otherwise, it just becomes another signal in the noise, which is the last thing teams need right now. They require reliable systems that can analyse vast sprawling infrastructures, connect the dots, and act with authority. Ultimately, greater productivity depends on faster fixes, stronger collaboration, and a culture where AI functions as a member of the team.
The AI cultural shift
It doesn’t stop there. AI agents need to deliver more than just incident response. Teams in environments where AI has been integrated effectively often experience broader cultural shifts. For instance, new hires can onboard more quickly, and engineers can focus more on proactive tasks instead of reactive support. Graduates and younger professionals entering AI-augmented roles, expect AI tools to be part of their work environment. They prefer to work in spaces where technology enhances their efforts rather than hinders them.
The answer lies in creating environments where individuals can perform at their best. This includes making insights easily accessible and positioning AI as a partner in execution, rather than just an additional layer of technology that’s been added to the stack.
