Why traditional DevOps assessments fail to capture the measurement gap

Understanding the DevOps maturity framework

A DevOps maturity assessment framework evaluates an organization’s current practices against established research-based criteria. This evaluation covers the culture, tools, processes, and performance required for a successful DevOps transformation.

The goal of this framework isn’t to reach a theoretical “Level 5” in every category. Instead, it is to identify where friction is decoupled from business value. By determining DevOps maturity levels—from initial automation to continuous, AI-augmented deployment—companies can identify precise friction and align engineering capacity with business objectives.

Why conduct a DevOps maturity assessment?

It can be a challenge to determine the timing of your first DevOps maturity assessment. If you’re facing strategic challenges in these areas, it may be time to start to plan your first assessment.

• Identifying human latency: Assessments pinpoint precise friction like “slow feedback loops” or “lost deep work” rather than using vague terms like “things are hard.” We find that most organizations have optimized their CI pipelines, yet PRs still sit for long periods waiting for human review.
• Continuous improvement: Organizations can systematically improve the software development process by assessing “validated signals” like pull request throughput and the Developer Experience Index (DXI).
• Alignment with business goals: Assessments ensure DevOps initiatives contribute to broader business outcomes, such as increasing the percentage of time spent on new capabilities.
• Cultural integration: Measuring how well teams adopt a collaborative culture provides a “complete view of engineering performance.”

How to conduct a devops maturity assessment

Conducting a successful assessment follows a predictable, intentional arc designed to move from data to insight in weeks, not months:

1. Define assessment criteria: Use research-backed frameworks like the DX Core 4 (speed, effectiveness, quality, and impact) or the AI Measurement Framework to define what “good” looks like for your specific business context.
2. Gather the right team: Include representatives from development, operations, and cross-functional teams to ensure a comprehensive evaluation.
3. Use the right tools and resources: Leverage platforms that provide insights into DevOps metrics like lead time for changes, change failure rate, and AI tool usage (DAUs/WAUs).
4. Interview and survey: Gather “developer-reported friction” and qualitative data on culture through a DevOps maturity assessment questionnaire to capture the DXI. This is critical because system data often misses the “silent killers” of productivity.
5. Analyze and report: Draw insights from patterns in the data to create a roadmap for improvement, explaining why specific software development KPIs matter for team performance.

The stages of a maturity model

A modern DevOps maturity model categorizes an organization into four levels to provide a clearer view of where to focus next:

• Novice (Level 1): Processes are often manual and siloed. There is minimal automation and limited use of CI/CD. The organization reacts to failures rather than preventing them.
• Intermediate (Level 2): Basic automation is established. Teams are beginning to track baseline DORA metrics, though they often struggle with “measurement gaps” like tracking speed without understanding quality.
• Advanced (Level 3): Significant automation exists across the lifecycle. Teams use research-backed measurement to optimize the conditions developers need to deliver. The focus shifts from “tools” to “flow.”
• Elite (Level 4): High degrees of automation and predictive analytics are in place. These organizations treat ai-assisted engineering tools as extensions of the team and achieve rapid, on-demand deployment without sacrificing software quality.

The maturity matrix

DevOps maturity is based on key performance drivers and outcomes. The table below outlines how organizations advance through each stage.

Advanced considerations for engineering leaders

To advance from “Intermediate” to “Elite,” leaders must address the qualitative drivers that system metrics often miss.

Closing the measurement gap

System-only data tells you what happened, but not why. An elite assessment must include developer sentiment to uncover hidden bottlenecks. For instance, a high deployment frequency might look good on paper, but a high cognitive complexity in the codebase might be causing significant developer burnout.

The role of platform engineering

Mature organizations recognize that developers shouldn’t have to navigate infrastructure alone. Platform development acts as a force multiplier. By providing “golden paths,” you reduce the cognitive load on individual contributors, allowing them to focus on feature delivery rather than environment configuration. This is the hallmark of a mature platform engineering strategy.

AI agents as team members

By 2026, maturity also includes the integration of autonomous agents. Elite organizations don’t just use AI for code completion; they use it for incident response automation and predictive maintenance. The assessment should evaluate how these agents are governed and whether they are reducing the technical debt ratio or inadvertently contributing to code rot.

Strategic considerations for the CTO

Avoiding the “fear and gamification” trap

As you roll out engineering metrics, especially speed metrics like cycle time, you must be intentional about communication. If developers fear metrics will be used for individual performance reviews, they will “game” the system, rendering the data meaningless.

Executive Action: Clearly state that these metrics are for organizational investment and workflow health, not micromanagement or individual evaluation.

Balancing velocity with maintainability

AI can deliver impressive speed gains, but DevOps maturity requires balancing that speed with quality. AI-generated code can sometimes be less intuitive, potentially creating long-term bottlenecks if not monitored properly via code review checklists.

Executive Action: Pair AI throughput metrics with quality signals like change failure rate and mean time to restore to ensure you aren’t trading today’s speed for tomorrow’s technical debt.

DevOps maturity FAQ

Between DevEx, SPACE, and DORA, which framework should our organization prioritize?

You shouldn’t have to choose. The DX Core 4 was developed to simplify the landscape by encapsulating DORA, SPACE, and DevEx into a single, unified approach. This framework balances system-based metrics with developer experience data to provide a more complete view of productivity.

How long does a comprehensive DevOps maturity assessment typically take to deploy?

Many organizations believe they need months or years to build effective dashboards, but this often leads to wasted effort without realized value. By leveraging an automated platform like DX, you can fully deploy the Core 4 and gain actionable insights in weeks.

Should we use AI throughput metrics like “code volume” to measure AI maturity?

We strongly caution against using metrics like code generation volume in isolation, as they are highly susceptible to gaming. Instead, maturity should be assessed by combining direct impact signals, like measure ai impact, with overall productivity trends in the Core 4.

Is “Level 4” maturity necessary for every development team?

Not necessarily. Maturity assessments should be used to identify where friction exists, but the target level of maturity should align with your specific business goals. High-velocity product teams may require “Elite” status, while maintenance teams may find an “Advanced” state more than sufficient for their impact.

Can you reach DevOps maturity?

DevOps maturity is a moving target. In 2026, the organizations that “win” are those that can accurately measure the impact of their technology investments and move quickly to resolve the friction that slows their people down.

To build a high-performance culture, stop trying to find a single “productivity number.” Instead, focus on the SDLC best practices that empower your teams. Start by establishing a baseline for your organization’s DXI and AI-driven time savings. This will give you the data-informed answers needed to navigate the rapid shifts in software engineering and remain competitive.

Go deeper: For everything we’ve published on measuring engineering performance—from the Core 4 to real-world leader practices—see our developer productivity metrics guide.