Executive Summary
Purpose and Scope
As organizations navigate the integration of Artificial Intelligence (AI) into critical software delivery streams, executives face a pivotal question: Does AI render traditional DevOps practices obsolete, or does it demand their evolution?
Perforce Software's 2026 State of DevOps report analyzes survey data from over 800 IT professionals to determine the relationship between DevOps maturity, AI success, and economic performance. These IT professionals come from global organizations across high-intensity sectors such as financial services, healthcare, and technology.
The data is clear: DevOps has not lost relevance in the age of AI; it has become the prerequisite for AI scalability. Incomplete DevOps practices remain the primary barrier to value realization.
Key Findings
1. DevOps Maturity Drives AI Success
AI adoption cannot succeed in a vacuum. The data reveals a direct correlation between established DevOps maturity and the ability to leverage AI effectively.
- 70% of organizations indicate that DevOps maturity meaningfully influences their AI success.
- 72% of leaders among high-maturity organizations report deeply embedded AI practices, compared to just 18% in low-maturity counterparts. This point further emphasizes the pivotal role DevOps maturity plays in enabling AI success.
- Centralized systems and control planes are key to scaling AI securely. High-maturity organizations are almost twice more likely than lower-maturity organizations to favor hybrid DevOps-platform engineering delivery models (79% vs 45%) to achieve this goal.
The DevOps Evolutionary Model
2. The Economic Advantage of Standardization1
Operational consistency translates directly to economic productivity and cost control. Mature organizations minimize rework and accelerate recovery, while immature processes inflate operational expenses.
- Automation ROI: High-maturity organizations are 36% more likely to automate the majority of deployments (at least 61%) from commit to production.
- Incident Response: These same organizations are 66% more likely to respond "very effectively" to production incidents, utilizing automated rollbacks and clear processes to mitigate downtime.
- The Cost of Inconsistency: Low-maturity organizations face higher variability, with 78% operating non-standardized delivery models. This lack of standardization leads to increased rework and cloud spend inefficiencies.
3. Governance and The "Confidence Gap"
A critical disconnect exists between perceived AI confidence and actual governance capabilities. While optimism regarding AI output is high, the mechanisms to verify and audit these outputs remain underdeveloped.
- Confidence vs. Verification: While 77% of respondents express confidence in AI outputs, only 38% have deeply embedded AI across multiple delivery stages.
- Compliance Risk: Auditability remains a significant vulnerability. Only 39% of organizations maintain fully automated audit trails, limiting visibility into AI performance and compliance adherence.
4. Organizational Bottlenecks Outweigh Tooling Constraints
Contrary to the common belief that better tools solve delivery challenges, the primary impediments to scaling are organizational. The most significant bottlenecks cited include:
Report Takeaways
The State of DevOps report will explore each of these findings in depth, but each of them lead to the following takeaways:
1. AI Amplifies DevOps Maturity Gaps
While DevOps practices are widely adopted, their implementation often varies significantly across organizations. AI further accentuates these disparities, creating sharper divides in outcomes, reliability, and operational costs. This uneven maturity leads to a paradox where DevOps is seen as both “solved” and “broken.”
2. Compliance is an Essential Growth Enabler
Compliance should not be seen as a hindrance. Organizations should adopt integrated compliance solutions that streamline adherence while enabling faster delivery and reducing risks. Automating audit trails will help ensure that AI outputs meet rigorous compliance standards in regulated industries.
3. Focus on People, Process, and Advanced Environment Management
Addressing coordination friction and skills gaps remains key to achieving success with AI. Improved environment management tools and strategies can also reduce delays, enhance testing efficiency, and ensure smooth system cutovers, which ultimately accelerates time-to-market.
Bottom Line:
AI amplifies existing organizational states. It accelerates success in mature DevOps environments and widens the risk profile in immature ones. Success with AI requires a commitment to maturing DevOps practices.
DevOps has not failed. Incomplete DevOps has.
1Note: Testing automation is a critical driver of both deployment automation and release velocity. See our companion AI in Testing report for detailed analysis of how test automation contributes to these economic outcomes.