The Engineer’s Guide to Automation Maturity
Automation is business-critical for speed, scalability and resilience — a mechanism for survival in today’s complex, digital landscape. Yet the uncomfortable truth is that many organizations are nowhere near where they need to be with their automation strategy.
While some teams are drowning in manual processes and tribal knowledge, others have cobbled together scripts without any cohesive vision. This patchwork approach creates operational drag, blown service-level agreements (SLAs), sluggish incident response and costs that keep climbing.
The result? Organizations are constantly playing catch-up instead of getting ahead of problems.
Automation maturity isn’t binary. It’s not about having automation versus not having it. It’s about how strategically and effectively an organization uses it. An organization’s position on this maturity spectrum directly determines whether automation becomes its competitive advantage or just another source of technical debt.
Why Measure Automation Maturity?
Automation maturity is a strategic indicator of how well an organization is using automation to drive meaningful business outcomes. When automation efforts lack strategy or consistency, the operational fallout is immediate and measurable. Teams burn cycles on repetitive work, critical processes bottleneck around a few experts and fragmented tooling creates dangerous visibility gaps.
The compound effect? Manual toil leads to burnout, dependency bottlenecks slow everything down, security risks multiply without proper governance and innovation stalls as engineers spend their time firefighting instead of building the future.
Progress is rarely straightforward. Without an overarching automation strategy, organizations can suffer from manual, repetitive work characterized by visibility gaps, disconnected islands of automation only available to experts, security risk and weak change controls. That’s why measuring maturity is so important.
Teams can’t improve what they don’t measure, so benchmarking automation maturity helps teams see not just where their processes stand, but how those gaps ripple out into everything else. Beyond the technical benefits, a maturity model reveals opportunities to strengthen culture and collaboration, areas that often make or break automation success.
Five Stages of Maturity
The first step toward embracing, optimizing and scaling automation within ITOps and engineering is to understand where the organization currently stands. Consider the following maturity model and its five distinct stages:
1. Manual
Teams still rely heavily on manual, time-consuming and error-prone processes. Where it does exist, documentation can be found in wikis or runbooks, but demands a high degree of human intervention and is often confined to a few key experts. Over-relying on a small group of people using manual processes to complete tasks inevitably hurts productivity, increases operations costs and lengthens resolution times. Bottlenecks emerge and innovation is forced to take a back seat as engineering talent ends up being forced to solve the same old problems time and time again.
Even if automation exists, it’s likely at this stage to be accessible only to a limited few and to handle only the most basic tasks, such as server restarts. A lack of visibility into where bottlenecks are occurring makes breaking free of the cycle that much harder.
2. Siloed
Teams are starting to use basic automation, but it’s not fully integrated into daily workflows and is still confined to a few experts. They may use it to gather diagnostic data, provision infrastructure, make configuration changes or resolve common issues. However, there’s still an over-reliance on tickets and manual hand-offs.
The result is that, while the organization is likely to see some operational efficiency gains, bottlenecks persist, innovation remains slow and cost savings are minimal. To break the cycle, teams must embed automation into core operations so that it’s more widely accessible and replace isolated scripts with a more integrated approach.
3. Centralized
At this stage, everyone is empowered to use pre-approved automation on demand as a core part of operations, reducing time spent on manual toil and freeing it up for more strategic work. The organization may also have introduced frameworks for an automation Center of Excellence (CoE), which will help to standardize best practices across teams, supporting scalability and alignment with business goals.
Automation has been democratized so teams can do anything from restarting services and remediating issues to provisioning infrastructure and making configuration changes. The result is fewer interruptions for expert engineers so they can focus on high-value work.
4. Standardized
By this stage, teams have not only standardized but also centralized automation efforts. Rather than being confined to a few isolated scripts, automation is now integrated into critical workflows and, while centrally managed, is available via self-service within predefined guardrails.
The result is strategically impactful, organization-wide automation that reduces bottlenecks, accelerates incident resolution and supports business goals. It also means less human error and improved governance to support compliance efforts.
The stage is set for even more advanced and intelligent automation in the future.
5. Adaptive
The most mature stage in the journey is reached when automation runs autonomously and seamlessly in the background, dynamically responding to events in real time without human intervention. Users can tell AI agents in plain English what they need, and those agents will use automation to instantly execute the relevant tasks. AI can even help to build new automated processes in response to incidents or even planned work.
At this stage, organizations are often able to prevent operational issues before they even occur, reducing downtime and enhancing reliability and customer satisfaction. They can optimize resources, lower costs and improve employee efficiency to empower engineering talent to focus on strategic work. They take advantage of enterprise-wide governance to ensure automation scales without losing control.
Toward Continuous Progress
To begin moving through these stages, teams should consider which metrics will best help them to identify and address capability gaps.
In the early stages, it could be mean time to resolution (MTTR) and reduction in manual effort or task automation rates and time spent on innovation vs repetitive tasks. Later on, they may want to focus on governance and standardization, for example through automation adoption rates or the implementation of an automation COE. When approaching higher levels of maturity, it makes sense to consider scalability, agility and customer and business impact.
The end goal in tracking these metrics is to keep moving forward, refining and scaling their approach. Each small step brings the team and the organization closer to harnessing the true value of automation.
On this journey, technology is important. But it’s as much about changing the way teams work as the tools they use to do so.
