[{"data":1,"prerenderedAt":519},["ShallowReactive",2],{"/en-us/the-source/platform/more-code-more-bottlenecks-tackling-the-ai-paradox":3,"footer-en-us":52,"the-source-banner-en-us":386,"the-source-navigation-en-us":392,"article-site-categories-en-us":415,"the-source-newsletter-en-us":417,"more-code-more-bottlenecks-tackling-the-ai-paradox-article-hero-category-en-us":424,"more-code-more-bottlenecks-tackling-the-ai-paradox-the-source-source-cta-en-us":450,"more-code-more-bottlenecks-tackling-the-ai-paradox-article-hero-author-en-us":459,"more-code-more-bottlenecks-tackling-the-ai-paradox-category-en-us":479,"more-code-more-bottlenecks-tackling-the-ai-paradox-the-source-resources-en-us":492},{"id":4,"title":5,"body":6,"category":7,"config":8,"content":14,"description":6,"extension":41,"meta":42,"navigation":11,"path":43,"seo":44,"slug":48,"stem":49,"type":50,"__hash__":51},"theSource/en-us/the-source/platform/more-code-more-bottlenecks-tackling-the-ai-paradox.yml","More Code More Bottlenecks Tackling The Ai Paradox",null,"platform",{"layout":9,"template":10,"featured":11,"author":12,"sourceCTA":13},"the-source","TheSourceArticle",true,"gitlab","global-devsecops-report-2025",{"title":15,"description":16,"date":17,"timeToRead":18,"heroImage":19,"keyTakeaways":20,"articleBody":24,"faq":25},"From fragmented tools to faster delivery: A modernization roadmap","AI coding tools speed up development but not delivery. Learn how DevOps, Security, and AI modernization work together to close the gap.","2026-03-05","5 min read","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772560228/bxbyenstqjwgyryegvno.png",[21,22,23],"AI coding tools accelerate code creation, but the work that happens downstream — review, testing, security, deployment — still relies on fragmented tools. More code going in means more pressure on bottlenecks that already exist.","Closing the delivery gap requires three interconnected modernization journeys: DevOps (unified toolchains), Security (continuous and automated), and AI (agents across the full lifecycle, not just coding). Progress in one accelerates the others.","Organizations like Ericsson, Ally Financial, and Barclays are already seeing results by addressing all three dimensions. Use GitLab's five-minute maturity assessments to identify where to start and build a personalized modernization roadmap.","AI is generating code at speeds that were unimaginable three years ago. Unfortunately, the pace of deploying that code to production hasn’t kept up. In fact, AI coding has _slowed_ delivery for many organizations.\n\nThe authors of the [DORA State of DevOps Report](https://dora.dev/research/2024/dora-report/) identified this trend as far back as 2024 when they found that increased AI adoption correlated with lower software delivery throughput and stability. This pattern only intensifies as code volumes grow.\n\nWhat explains this paradox? [Coding represents only about 15%](https://about.gitlab.com/resources/developer-survey/) of the work involved in shipping software. The other 85% — code review, testing, security scanning, compliance, deployment — still relies on fragmented tools and manual processes. AI coding tools accelerate code creation while leaving the manual work downstream intact. More code translates to more bottlenecks.\n\nSolving the AI paradox for software delivery requires a coordinated transformation across three interconnected journeys: [DevOps modernization](https://about.gitlab.com/assessments/devops-modernization-assessment/), [Security modernization](https://about.gitlab.com/assessments/security-modernization-assessment/), and [AI modernization](https://about.gitlab.com/assessments/ai-modernization-assessment/).\n\n## DevOps modernization: The foundation\n### What is DevOps modernization?\nMost engineering organizations are running a patchwork of tools that were never designed to work together. Each tool has its own contract, security review, and support overhead. That fragmentation is expensive to maintain, but the hidden cost is even larger: every handoff between disconnected systems introduces latency, context loss, and the risk of errors. Developers spend time chasing status across tools instead of building. Managers lack a clear picture of where work is stalled. The more code AI generates, the more those handoffs hurt.\n\n**DevOps modernization** is the process of consolidating fragmented toolchains into a unified platform, automating manual processes, and standardizing how teams build and ship software. It tackles the infrastructure of the 85%: the pipelines, handoffs, and coordination overhead where AI-generated code gets stuck.\n\n### How do you progress?\nOrganizations typically start by auditing their current toolchain and identifying where handoffs introduce the most friction. Consolidation usually begins with source control and CI/CD — getting pipelines running consistently and replacing project-by-project configurations with shared templates and reusable components.\n\nFrom there, the focus shifts to performance and scale: optimizing pipeline execution times, automating deployments across multiple environments, and standardizing practices across teams.\n\nThe final frontier is enterprise-scale automation, organization-wide standardization, and AI agents that automate routine tasks across the development lifecycle, freeing engineers to focus on the work that requires human judgment.\n\n### What outcomes can you expect?\nEricsson, the global telecommunications company, lived this reality before consolidating their toolchain. They were stuck in long release cycles, manual processes, and thousands of hours lost to coordination across disconnected systems. After unifying with a single DevSecOps platform, they [saved 130,000 engineering hours in six months](https://about.gitlab.com/customers/ericsson/) and cut release cycles from years to months.\n\nFor most organizations, the early payoff is visibility: a clear, real-time picture of where software is in the delivery pipeline and what’s slowing it down. Every manual step eliminated frees engineering capacity and accelerates the next stage of the journey.\n\n## Security modernization: The safeguard\n### What is Security modernization?\nAI is rapidly generating staggering amounts of code while security reviews remain stuck at the old pace. All that code needs to be scanned, reviewed, and approved. Security teams simply can’t keep up. Things get worse when compliance evidence is scattered across multiple disconnected systems, requiring weeks of manual effort to aggregate for audits.\n\n**Security modernization** means shifting security and compliance from manual checkpoints late in development to automated, continuous processes embedded earlier in the lifecycle. In a modernized security posture, scanning runs automatically inside CI/CD pipelines. Vulnerabilities surface to developers in context, at the moment they can most efficiently fix them, rather than arriving as a list of findings weeks after the code was written. Compliance evidence is collected continuously rather than assembled manually before each audit.\n\n### How do you progress?\nSecurity modernization typically starts with embedding automated scanning (dependency scanning, SAST, and secret detection) directly into existing pipelines, beginning with the vulnerability types that carry the most regulatory or business risk.\n\nAs scanning becomes routine, the focus shifts to ownership and scale: moving from project-level to group-level security policies, establishing defined SLAs for vulnerability response, and putting findings in front of developers with enough context to act on them rather than routing everything through a security team bottleneck.\n\nFrom there, the work becomes predictive — leveraging risk-based prioritization, automating compliance evidence collection, and deploying AI agents that can explain, triage, and remediate vulnerabilities automatically.\n\nAt the highest level of maturity, security is embedded enterprise-wide with policy-as-code enforcement across every project and executive dashboards that connect security posture directly to business outcomes.\n\n### What outcomes can you expect?\nAlly Financial, one of the largest digital financial services companies in the U.S., shifted security left and embedded scanning directly into their DevOps platform. As a result, they were able to [increase deployments by 55%](https://about.gitlab.com/customers/ally/) while reducing downtime by 100 hours per month and saving $300,000 annually.\n\nSpeed and security improved in lockstep. Modernizing security removes the tradeoff between velocity and security by making security a built-in property of the delivery process rather than a gate at the end of it.\n\n## AI modernization: The multiplier\n### What is AI modernization?\nMost organizations have deployed AI coding tools and seen individual developers get faster. But productivity at the individual level doesn't automatically translate into faster delivery at the organizational level. AI assistance that stops at code generation still leaves the downstream steps of the lifecycle running on manual processes. The gains stall. Worse, more code entering the pipeline can actively increase pressure on review, testing, and security processes that haven’t scaled to match.\n\n**AI modernization** builds on top of DevOps and Security modernization. Once your DevOps workflows are unified and your security processes are continuous, you can extend AI from coding into the rest of the software lifecycle. AI modernization is the progression from individual developers using AI coding tools in isolation to teams orchestrating AI agents across every stage, from code review and testing to security remediation and deployment.\n\nThis is where you fully resolve the AI paradox.\n\n### How do you progress?\nAI modernization typically begins with individual developers adopting pre-built AI capabilities such as code suggestions and agentic chat for code assistance, building confidence through hands-on experimentation.\n\n\nFrom there, the focus shifts to the team level: creating custom agents tailored to specific workflows and coding standards, establishing governance and best practices, and building repeatable multi-step flows that automate handoffs between development stages. Integrating external tools through Model Context Protocol expands the context available to agents and enables more sophisticated orchestration across the broader toolchain.\n\n\nThe final stage is organization-wide deployment: autonomous agent workflows executing across the full software lifecycle. Agents execute traditionally manual processes in real time and in parallel across multiple teams, projects, and releases and always within enterprise-level governance. AI impact on organization-wide operational efficiency is measured in real time and can be closely associated with business outcomes.\n\n### What outcomes can you expect?\nBarclays is [scaling this approach to 18,000 team members](https://home.barclays/insights/2025/07/scaling-AI-at-Barclays/), and their developers report that AI assistance across the full lifecycle is freeing them to focus on architecture, design, and customer-facing innovation rather than manual coordination. In organizations with a modernized AI approach, the work that genuinely requires human expertise gets more human attention, while agents handle the coordination, verification, and execution.\n\n## From incremental gains to wholesale transformation\nImagine an engineering organization operating at the intersection of all three journeys.\n\nDevelopers work on a unified platform where AI agents handle routine code generation, documentation, and test creation. Engineers focus on architecture, design, and the work that genuinely requires creative problem-solving. Those agents are embedded in a delivery pipeline that runs in minutes, not hours, with automated testing at every stage and production deployments happening multiple times a day.\n\nSecurity is continuous and invisible. Vulnerabilities are detected and often remediated before code even reaches review. Compliance evidence is collected automatically with every pipeline run. The security team focuses on threat modeling and policy, not triage.\n\nWhen a developer opens a merge request, AI agents review the code, generate tests, run security scans, and flag issues — all before a human reviewer ever looks at it. When something breaks in production, agents diagnose the failure, identify the root cause, and recommend a fix. The cycle from incident to resolution is measured in minutes.\n\nAcross the organization, multiple teams ship releases in parallel, each supported by AI agents that maintain context across projects, enforce governance, and execute workflows end to end. Human engineers define the strategy and guardrails. Agents handle the execution.\n\nIndustry leaders are already building toward this vision. Those that move deliberately now will compound their advantage over those that wait.\n\n## Where to start\nYou can begin with whichever journey addresses your most pressing pain. A team drowning in toolchain complexity might start with DevOps consolidation. An organization under regulatory pressure might prioritize security. A team that has already unified their software lifecycle but wants to multiply output might lead with AI. The entry point matters less than recognizing that all three journeys are dimensions of the same transformation — and that progress in one accelerates the others.\n\nWe've created maturity assessments for each journey ([DevOps](https://about.gitlab.com/assessments/devops-modernization-assessment/), [Security](https://about.gitlab.com/assessments/security-modernization-assessment/), and [AI](https://about.gitlab.com/assessments/ai-modernization-assessment/)) that help you understand where you stand today and what steps will deliver the greatest impact. They take about five minutes and provide a personalized roadmap based on your results.\n\nThe AI coding gains are already here. Closing the delivery gap is what turns them into a competitive advantage.",[26,29,32,35,38],{"header":27,"content":28},"Why does AI coding make software delivery slower? ","AI coding tools accelerate code creation but leave the downstream 85% of work — code review, testing, security scanning, and deployment — relying on fragmented tools and manual processes. More code entering the pipeline increases pressure on bottlenecks that already exist, slowing overall delivery.",{"header":30,"content":31},"What are the three modernization journeys that solve the AI paradox? ","The three interconnected journeys are DevOps modernization (consolidating fragmented toolchains), Security modernization (embedding automated scanning continuously into pipelines), and AI modernization (extending AI agents across the full software lifecycle beyond just code generation).",{"header":33,"content":34},"What is DevOps modernization and what results can it deliver? ","DevOps modernization consolidates fragmented toolchains into a unified platform and automates manual handoffs. Ericsson implemented this approach and saved 130,000 engineering hours in six months while cutting release cycles from years to months.",{"header":36,"content":37},"How does security modernization improve both speed and compliance? ","Security modernization shifts scanning from manual end-of-cycle checkpoints to automated processes embedded directly in CI/CD pipelines. Ally Financial achieved a 55% increase in deployments, reduced downtime by 100 hours per month, and saved $300,000 annually using this approach.",{"header":39,"content":40},"Where should an organization start its modernization journey? ","Organizations can begin with whichever journey addresses their most pressing pain — toolchain complexity, regulatory pressure, or AI scaling. 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