So the question is not just whether automation is improving. It is whether engineers are gradually being removed from the loop, or simply moving to a different role within it.
Why Observability Is Shifting Faster Than Roles Are
Observability used to be about visibility. You collected logs, tracked metrics, and set alerts. Engineers interpreted the data and made decisions.
Now, that model is evolving.
Modern platforms can ingest large volumes of telemetry and identify patterns far faster than a human ever could. They can flag anomalies in real time and connect events across systems that would otherwise appear unrelated.
According to Gartner, organisations are increasingly investing in intelligent monitoring capabilities that go beyond traditional dashboards, with a focus on reducing manual analysis.
That shift is changing expectations.
Instead of simply monitoring systems, engineers are being asked to oversee systems that monitor themselves.
What AI Is Actually Changing Day to Day
From a practical standpoint, the biggest change is not automation of fixes. It is automation of interpretation.
In many environments today, engineers no longer need to manually sift through logs to identify an issue. Systems can highlight anomalies, suggest possible root causes, and prioritise incidents based on impact.
This is where AI observability comes into play. It acts as a layer that sits between raw data and human decision making, reducing the amount of effort required to understand what is happening.
For example, instead of receiving a generic alert about increased latency, an engineer might receive:
A summary of the issue
A likely cause based on historical patterns
A list of affected services
Suggested next steps
This does not remove the engineer from the process. It changes how they engage with it.
Why Engineers Are Still Critical
Despite these advances, there are clear limits to what automated systems can do.
Most operational decisions are not purely technical. They involve trade offs.
Should a deployment be rolled back to restore performance, even if it delays a feature release?
Should traffic be rerouted to a secondary region that is more expensive but more stable?
Should an issue be prioritised now or monitored further to avoid unnecessary disruption?
These decisions require context that goes beyond system data.
A report from McKinsey & Company highlights that while automation can improve efficiency, human judgement remains essential in complex and ambiguous scenarios, particularly where business impact is involved.
Engineers are not just problem solvers. They are decision makers.
The Risk of Over Automation
There is also a risk in relying too heavily on automated systems.
If engineers become too removed from the underlying data, they may lose the ability to question or validate the system’s conclusions.
This can lead to situations where:
Incorrect assumptions go unnoticed
Automated actions create unintended side effects
Teams become overly dependent on tools they do not fully understand
Maintaining a balance is important.
Automation should reduce workload, not reduce awareness.
A Shift in Skill Sets, Not Relevance
What is changing is not the need for engineers, but the nature of their work.
Less time is spent on manual investigation
More time is spent on interpreting insights and making decisions
Greater focus is placed on understanding system behaviour at a higher level
Engineers are moving from being operators to being supervisors of complex systems.
This requires a different set of skills.
Instead of deep familiarity with individual tools, there is a greater emphasis on systems thinking, pattern recognition, and decision making under uncertainty.
Real World Examples of the Balance
In high scale environments, this balance is already visible.
In financial systems, automated monitoring can detect anomalies in transaction processing, but engineers decide how to respond based on risk and impact.
In cloud infrastructure, systems can automatically scale resources based on demand, but engineers define the policies and thresholds that guide those actions.
In communication platforms, performance issues can be identified and prioritised automatically, but engineers determine how to resolve them in a way that aligns with broader objectives.
In each case, automation handles the heavy lifting, but humans remain in control of outcomes.
What Staying “In the Loop” Actually Means
Being in the loop does not necessarily mean being involved in every step.
It means:
Understanding how decisions are being made
Having the ability to intervene when necessary
Maintaining visibility into system behaviour
Taking responsibility for outcomes
Engineers may not need to investigate every alert manually, but they still need to trust and validate the systems that do it for them.
That trust is built over time through consistent performance and transparency.
How Teams Can Adapt Without Losing Control
For organisations adopting more advanced observability capabilities, a few practical approaches can help maintain the right balance.
Keep humans involved in high impact decisions
Use automation for repetitive and low risk tasks
Provide clear visibility into how automated decisions are made
Encourage engineers to challenge and validate system outputs
This approach allows teams to benefit from automation without losing control or accountability.
Conclusion
The rise of AI observability is not removing engineers from the loop. It is reshaping where they sit within it.
Instead of spending time collecting and interpreting raw data, engineers are focusing on making better decisions with the insights they are given.
That shift makes their role more strategic, not less important.
Because no matter how advanced the tools become, the responsibility for understanding impact, weighing trade offs, and making the final call still sits with humans.
