A Capgemini survey on enterprise automation found that while large-scale automation is still maturing, IT teams are increasingly treating automation as a strategic priority rather than a tactical fix. Areas such as self-healing infrastructure, event correlation, diagnostics, application releases, cybersecurity monitoring, and storage and server management are delivering measurable cost savings—and, more importantly, improving reliability and responsiveness to business demands.
This shift raises an important question: as DevOps practices such as agile methodology, continuous deployment, and iterative optimization become standard in modern engineering teams, should automation follow the same principles? The answer is yes—and this article explores how to apply a DevOps mindset to your automation strategy.
The Problem with “Set and Forget” Automation
Legacy automation was built for a more stable world: physical servers with predictable workloads, slow-moving infrastructure, and infrequent change cycles. You wrote a script, ran it on a schedule, and moved on. That model is increasingly incompatible with modern cloud-native environments where infrastructure is ephemeral, workloads are dynamic, and business requirements change rapidly.
The risk of static automation is compounded by scale. When a machine executes a flawed or outdated policy, it does so consistently and at speed—propagating mistakes across hundreds or thousands of resources before anyone notices. This is precisely why automation must be treated as a living system, subject to the same continuous improvement cycles that DevOps applies to software delivery.
The New Expectations of IT Automation
Applying a DevOps mindset to automation starts with asking the right questions before and after you automate anything. The following four questions provide a practical framework for building an iterative, continuously optimized automation practice.
1. Is This a Good Process Worth Automating?
This may seem like an obvious starting point, but it is frequently skipped. Processes and policies are often automated once and then forgotten—even as the underlying infrastructure, application architecture, or business goals change significantly. A continuous optimization approach forces teams to periodically revisit existing automation and ask whether each policy still serves its intended purpose.
Not every process is a good candidate for automation. Some workflows are sensitive enough that automating them without sufficient safeguards threatens data integrity or compliance. Others are high-level decisions that depend on contextual judgment that scripts cannot replicate. A good automation engineer identifies which tasks genuinely benefit from automation and which require human oversight, and they revisit that assessment regularly.
2. How Often Does This Process Occur?
Frequency matters both for justifying the investment in automation and for keeping automation schedules accurate. Tasks such as patching, load balancing, or workload orchestration may run on a fixed time-series schedule or be triggered on demand. As infrastructure evolves—particularly as teams adopt serverless and cloud-native patterns where workloads are short-lived and highly variable—those schedules must evolve too.
An automation schedule that made sense for a fleet of long-running virtual machines may be entirely wrong for a containerized microservices environment. Continuously adapting the schedule to match actual workload patterns and customer demand is a core part of keeping automation effective.
3. How Long Does the Process Take?
Automation must be tested for both efficiency and correctness against the actual duration and frequency of the task it replaces. Some legacy system updates take hours; some orchestration tasks are near-continuous. Automation that is slower than the manual process—or that introduces latency into a time-sensitive pipeline—creates new problems rather than solving old ones.
Treat automation performance as a measurable metric. Establish baselines for how long manual execution takes, then measure automated execution against those baselines regularly. Regressions in execution time are a signal that the automation needs to be revisited.
4. Is There an Irreplaceable Human Element?
Automation can handle well-defined, repeatable tasks with reliable inputs—but it struggles with ambiguity, novel situations, and tasks that require contextual judgment. Activities such as deriving insights from anomalous data, designing new workflows, program management decisions, or architectural trade-offs still benefit significantly from human involvement.
This is where AIOps—the application of artificial intelligence and machine learning to IT operations—becomes relevant. AIOps can extend automation into more complex domains by giving systems the ability to learn from historical patterns and make probabilistic decisions. However, for most organizations today, AIOps is still an emerging capability. Until it is mature and accessible, teams should explicitly map out the points in any automated workflow where human review and decision-making must occur, and build those checkpoints into the process deliberately.
Applying DevOps Principles to Automation
The parallels between DevOps and a mature automation practice are direct:
- Iterate frequently. Just as DevOps favors small, frequent releases over large infrequent ones, automation policies should be updated in small increments and tested in staging or limited-blast-radius environments before wide deployment.
- Measure everything. DevOps relies on observability to understand system behavior. Automation should emit metrics and logs—execution time, success and failure rates, number of resources affected—so that its performance can be monitored and regressed against.
- Fail fast and recover. Good automation includes error handling, rollback mechanisms, and alerting. When a policy fails, the failure should be surfaced quickly and the impact contained.
- Treat automation as code. Store automation scripts, runbooks, and policies in version control. Apply code review practices. This makes changes auditable and reversible.
- Collaborate across teams. Automation that spans development, security, and operations benefits from cross-functional input—the same way DevOps breaks down silos between dev and ops teams.
Automation as a Strategic Tool, Not an End Goal
Automation is one of the most impactful capabilities available to modern IT operations teams. It reduces the burden of repetitive manual work, accelerates incident response, and frees engineers to focus on higher-value problems. But automation is a means to an end, not the destination itself.
Think of automation as a system that turns operational data into action. The quality of that action depends entirely on the quality of the policies driving it—and those policies must be maintained, measured, and improved continuously. As AI and machine learning capabilities mature and become more accessible, they will extend what automation can do. Until then, it falls to IT professionals to apply the same discipline to automation that DevOps has brought to software delivery: iterate, measure, improve, and never stop asking whether the current approach is the right one.
This article is based on analysis originally published on devops.com, drawing on findings from the Capgemini enterprise automation survey.