Three years ago we launched a DevOps transformation initiative. The goal was to move from our traditional waterfall development process to continuous integration, automated deployment, and infrastructure as code. We’d ship software faster, with better quality, and more reliability.

That was the vision. The reality has been slower, messier, and more complicated than anyone expected. We’ve made progress, but DevOps in a traditional enterprise is fundamentally different than DevOps at a startup or digital native company.

Here’s what the journey actually looks like and what I wish we’d understood from the beginning.

The Cultural Resistance Was Stronger Than Expected

DevOps isn’t primarily a technology change. It’s a cultural transformation that requires breaking down silos between development and operations teams.

Our developers and operations staff had worked in separate organisations with different incentives for years. Developers were measured on delivering features. Operations was measured on system stability. These goals are somewhat contradictory. Developers want to ship changes quickly. Operations wants stability, which means resisting change.

DevOps requires these teams to work together toward shared goals. In theory, everyone agrees this is better. In practice, people resist giving up established ways of working.

Operations staff worried that DevOps meant developers would deploy changes without proper oversight, breaking production systems. Developers worried that operations would block their work under the guise of stability concerns. Both had some validity to their concerns based on past experiences.

Building trust between these teams took years, not months. We needed leadership changes, team reorganisations, and numerous failures before people genuinely embraced shared responsibility. That cultural shift was far harder than implementing the technical practices.

The Legacy Applications Weren’t Ready

DevOps practices like continuous deployment and infrastructure as code work beautifully with modern, cloud-native applications. They’re far more challenging with legacy enterprise applications.

Our portfolio includes applications that were never designed for automated deployment. They have manual configuration steps. They require specific server configurations. They have complex interdependencies with other systems. Automating deployment for these applications required substantial refactoring.

Some applications simply can’t be continuously deployed without architectural changes. They’re monolithic systems where any change requires deploying the entire application. Testing the full application thoroughly takes days. Continuous deployment isn’t feasible without breaking them into smaller services first.

We ended up with a two-speed IT environment. New applications get built using DevOps practices. Legacy applications continue with traditional processes. This creates complexity and inconsistency but it’s the pragmatic reality.

Automation Required Significant Upfront Investment

The DevOps promise is that automation increases productivity. That’s true eventually, but the initial investment is substantial.

Building deployment pipelines, writing infrastructure as code, creating automated tests, and developing monitoring and observability capabilities requires months of engineering work. During this time, feature development slows because engineers are focused on tooling instead of business functionality.

We faced pressure to show business value from DevOps quickly. But the benefits don’t appear until the infrastructure is in place. That created tension between our need to invest in foundation and stakeholder expectations for immediate results.

We should have set better expectations upfront that DevOps adoption requires significant investment before productivity increases materialise. Instead we oversold quick wins and then struggled to explain why progress was slow.

Tool Proliferation Became a Problem

DevOps involves many categories of tools. Source control, CI/CD pipelines, artifact repositories, configuration management, container orchestration, monitoring, logging, security scanning. Each category has multiple competing tools.

We let teams choose their preferred tools rather than mandating standards. This felt aligned with DevOps culture of empowering teams. The unintended consequence was tool sprawl that became difficult to support.

One team used Jenkins for CI/CD. Another used GitLab. A third used GitHub Actions. Each tool required different expertise, different security configurations, and different operational procedures. The diversity created complexity that undermined the efficiency gains from automation.

We should have established more standardisation from the beginning. Empowering teams is good, but some consistency is necessary for manageability. We’re now consolidating tools, which requires migration work we could have avoided.

Security and Compliance Created Friction

DevOps emphasises speed and automation. Security and compliance processes emphasise controls and review. These can conflict.

Our security team initially saw DevOps as risk-increasing. Automated deployments meant less opportunity for security review. Infrastructure as code meant developers could provision resources without central oversight. The velocity felt like loss of control.

Compliance requirements created similar friction. Change management processes required approval and documentation. Automated deployments seemed to bypass these controls. Auditors questioned whether we could demonstrate appropriate oversight.

We had to build security and compliance into DevOps practices rather than treating them as gates. Security scanning in CI/CD pipelines. Policy-as-code for infrastructure provisioning. Automated documentation and audit trails. This took time and required collaboration between teams that historically worked independently.

The integration of security and compliance into DevOps is essential but it slows the transformation. You can’t just bolt security onto the end of a DevOps pipeline and expect it to work.

Measuring Success Was Difficult

DevOps advocates promote metrics like deployment frequency, lead time for changes, mean time to recovery, and change failure rate. These metrics work well for digital companies shipping software products.

They’re harder to apply in traditional enterprises with mixed application portfolios. How do you measure deployment frequency for an application that should only update quarterly? What’s appropriate lead time for a change that requires coordination across multiple legacy systems?

We struggled to define success metrics that were meaningful for our environment. The textbook DevOps metrics didn’t fit our reality. Creating custom metrics required thoughtful consideration of what we were actually trying to improve.

We eventually settled on metrics focused on reducing toil, improving reliability, and accelerating feedback cycles. These felt more relevant than pure velocity metrics. But figuring this out took time and created confusion when stakeholders expected standard DevOps metrics.

What Actually Worked

Despite challenges, we’ve made genuine progress. Some practices delivered clear value.

Automated testing caught regressions earlier in the development cycle. This reduced production defects and increased confidence in releases. The return on investment from test automation has been clear and substantial.

Infrastructure as code improved consistency and reduced configuration errors. Provisioning environments became repeatable and documented. This reduced operational burden and improved reliability.

Improved observability through better logging and monitoring reduced mean time to recovery for incidents. When problems occur, we can diagnose them faster because we have better visibility into system behavior.

Continuous integration reduced integration problems by forcing frequent merges. Large, infrequent integrations that caused multi-day debugging sessions became smaller, more manageable updates.

These tactical improvements delivered value even though the full DevOps transformation vision remains incomplete.

Advice for Other Traditional Enterprises

Set realistic timeframes. DevOps transformation takes years, not quarters. Anyone promising quick transformation is either lying or doesn’t understand traditional enterprise complexity.

Start with new applications and greenfield development. Prove the practices work in favourable conditions before trying to retrofit them onto legacy systems. Success builds momentum for broader adoption.

Invest heavily in cultural change and team integration. The technology is solvable. The people and process challenges are harder and more important.

Standardise more than DevOps purists recommend. Tool proliferation creates operational burden that undermines efficiency gains. Some consistency is necessary.

Build security and compliance into practices from the beginning. Don’t treat them as afterthoughts or gates. Collaborate early with security and compliance teams to design DevOps practices that meet their requirements.

Finally, be patient. Traditional enterprises can’t transform like startups because we have different constraints, different cultures, and different starting points. That’s fine. Focus on incremental improvement rather than revolutionary transformation. Over years, incremental improvements compound into substantial change.

We’re better today than three years ago. We’ll be better three years from now. That’s success, even if it doesn’t match the DevOps ideal.