[{"data":1,"prerenderedAt":517},["ShallowReactive",2],{"/en-us/the-source/authors/stephen-walters/":3,"footer-en-us":34,"the-source-navigation-en-us":342,"the-source-newsletter-en-us":369,"stephen-walters-articles-list-authors-en-us":381,"stephen-walters-articles-list-en-us":412,"stephen-walters-page-categories-en-us":516},{"_path":4,"_dir":5,"_draft":6,"_partial":6,"_locale":7,"config":8,"seo":10,"content":12,"type":26,"slug":27,"_id":28,"_type":29,"title":11,"_source":30,"_file":31,"_stem":32,"_extension":33},"/en-us/the-source/authors/stephen-walters","authors",false,"",{"layout":9},"the-source",{"title":11},"Stephen Walters",[13,24],{"componentName":14,"type":14,"componentContent":15},"TheSourceAuthorHero",{"config":16,"name":11,"role":19,"bio":20,"headshot":21},{"gitlabHandle":17,"linkedInProfileUrl":18},"swalters1","https://www.linkedin.com/in/1stephenwalters/","Field CTO, GitLab","Stephen Walters is Field CTO for GitLab. Stephen has been in the IT industry for over 30 years. He is an extensively experienced subject matter expert in Value Stream Management, DevSecOps, DevOps, ALM, SDLC and IT4IT, with management and consultancy experience across end-to-end IT disciplines. Currently also operating as an Ambassador for the DevOps Institute and an Influencer in the Value Stream Management Consortium, he is interested in all things DevOps. Stephen is a co-author of the Value Stream Reference Architectures white paper and is currently pursuing further research into Value Stream Management, Organizational Architecture and AI.",{"altText":11,"config":22},{"src":23},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1751463392/g6ktk5qb4vcqc9wqjlf9.jpg",{"componentName":25,"type":25},"TheSourceArticlesList","author","stephen-walters","content:en-us:the-source:authors:stephen-walters.yml","yaml","content","en-us/the-source/authors/stephen-walters.yml","en-us/the-source/authors/stephen-walters","yml",{"_path":35,"_dir":36,"_draft":6,"_partial":6,"_locale":7,"data":37,"_id":338,"_type":29,"title":339,"_source":30,"_file":340,"_stem":341,"_extension":33},"/shared/en-us/main-footer","en-us",{"text":38,"source":39,"edit":45,"contribute":50,"config":55,"items":60,"minimal":330},"Git is a trademark of Software Freedom Conservancy and our use of 'GitLab' is under license",{"text":40,"config":41},"View page 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newsletter.",{"config":375},{"formId":376,"formName":377,"hideRequiredLabel":329},1077,"thesourcenewsletter","content:shared:en-us:the-source:newsletter.yml","shared/en-us/the-source/newsletter.yml","shared/en-us/the-source/newsletter",{"amanda-rueda":382,"andre-michael-braun":383,"andrew-haschka":384,"ayoub-fandi":385,"bob-stevens":386,"brian-wald":387,"bryan-ross":388,"chandler-gibbons":389,"dave-steer":390,"ddesanto":391,"derek-debellis":392,"emilio-salvador":393,"erika-feldman":394,"george-kichukov":395,"gitlab":396,"grant-hickman":397,"haim-snir":398,"iganbaruch":399,"jlongo":400,"joel-krooswyk":401,"josh-lemos":402,"julie-griffin":403,"kristina-weis":404,"lee-faus":405,"ncregan":406,"rschulman":407,"sabrina-farmer":408,"sandra-gittlen":409,"sharon-gaudin":410,"stephen-walters":11,"taylor-mccaslin":411},"Amanda Rueda","Andre Michael Braun","Andrew Haschka","Ayoub Fandi","Bob Stevens","Brian Wald","Bryan Ross","Chandler Gibbons","Dave Steer","David DeSanto","Derek DeBellis","Emilio 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McCaslin",{"allArticles":413,"visibleArticles":515,"showAllBtn":329},[414,441,480],{"_path":415,"_dir":416,"_draft":6,"_partial":6,"_locale":7,"config":417,"seo":424,"content":429,"type":436,"slug":437,"category":416,"_id":438,"_type":29,"title":425,"_source":30,"_file":439,"_stem":440,"_extension":33,"date":430,"description":426,"heroImage":427,"keyTakeaways":431,"articleBody":435},"/en-us/the-source/ai/dora-insights-where-is-ai-really-driving-developer-productivity","ai",{"layout":9,"template":418,"articleType":419,"featured":6,"gatedAsset":420,"speakers":421,"isHighlighted":6,"authorName":-1},"TheSourceArticle","Webinar","dora-insights",[422,27,423],"derek-debellis","haim-snir",{"title":425,"description":426,"ogImage":427,"config":428},"DORA insights: Where is AI really driving developer productivity?","Discover valuable insights from the 2024 Accelerate State of DevOps Report and learn how you can harness AI to maximize team performance and innovation.","https://res.cloudinary.com/about-gitlab-com/image/upload/v1751464086/p04zmdk6h3bbkipeqelh.png",{"ignoreTitleCharLimit":329},{"title":425,"date":430,"description":426,"heroImage":427,"keyTakeaways":431,"articleBody":435},"2025-01-16",[432,433,434],"DORA research underlines the significance of developer experience, the emergence of platform engineering, and AI's role in software development across various levels.","AI has been shown to have positive impacts in enhancing team performance across the software development process; however, a comprehensive AI strategy is critical to ensure that benefits for individuals translate into benefits for the product.","Creating a supportive, valued, and motivated workspace is key to high performance and mitigating burnout, making it essential for organizations to ready their teams for AI's innovative potential.","For over a decade, the DORA research program has examined what distinguishes high-performing technology teams and organizations. Their four key metrics - lead time for changes, deployment frequency, change fail rate, and failed deployment recovery time - have become the industry standard for assessing software delivery performance. The [2024 Accelerate State of DevOps Report](https://cloud.google.com/resources/devops/state-of-devops?hl=en) highlights the ongoing importance of developer experience, the rise of platform engineering, and how the adoption of artificial intelligence (AI) affects software development across multiple levels.\n\nSoftware developers across all industries increasingly depend on emerging AI-powered development tools to minimize a wide range of repetitive tasks and boost team performance, security, and code quality - and over a third of developers report \"moderate\" to \"extreme\" productivity gains from using AI. However, effective change management and a comprehensive AI strategy are essential to address the challenges of early adoption, such as the AI training gap, “AI sprawl,” finding the optimal level of trust, and the need for a clear vision of success that is captured by a robust set of metrics.\n\nCreating a work environment where teams feel supported, valued, and motivated is crucial for achieving high performance and minimizing burnout. How can organizations ready their teams, processes, and cultures to harness the full potential of an AI strategy for driving innovation?\n\nIn this webinar, Derek DeBellis, Lead Researcher on Google's DORA team, Stephen Walters, Field CTO at GitLab, and Haim Snir, Senior Product Manager, Dev & Analytics at GitLab reveal the key findings from the 2024 Accelerate State of DevOps DORA report.\n\n### Join us as we explore:\n\n- **Benefits and challenges of AI adoption:** Learn how AI boosts productivity, job satisfaction, retention, and code quality and how to address potential roadblocks in early adoption.\n- **Platform engineering and AI:** Discover how platform engineering can elevate developer productivity and performance when combined with AI.\n- **Measuring performance with AI:** Understand how assessing the right quantitative metrics can help organizations better understand AI's impact on development workflows and business goals.","article","dora-insights-where-is-ai-really-driving-developer-productivity","content:en-us:the-source:ai:dora-insights-where-is-ai-really-driving-developer-productivity:index.yml","en-us/the-source/ai/dora-insights-where-is-ai-really-driving-developer-productivity/index.yml","en-us/the-source/ai/dora-insights-where-is-ai-really-driving-developer-productivity/index",{"_path":442,"_dir":443,"_draft":6,"_partial":6,"_locale":7,"config":444,"seo":447,"content":452,"type":436,"slug":476,"category":443,"_id":477,"_type":29,"title":448,"_source":30,"_file":478,"_stem":479,"_extension":33,"date":453,"description":449,"timeToRead":454,"heroImage":450,"keyTakeaways":455,"articleBody":459,"faq":460},"/en-us/the-source/platform/optimize-value-stream-efficiency-to-do-more-with-less-faster","platform",{"layout":9,"template":418,"articleType":445,"author":27,"featured":6,"gatedAsset":446,"isHighlighted":6,"authorName":11},"Regular","source-lp-dora-insights-where-is-ai-really-driving-developer-productivity",{"title":448,"description":449,"ogImage":450,"config":451},"Optimize value stream efficiency to do more with less, faster","Discover how to optimize your software delivery process and increase operational efficiency with Value Stream Management.","https://res.cloudinary.com/about-gitlab-com/image/upload/v1751463530/doerc0wzbg75r8yixgnf.png",{"ignoreTitleCharLimit":329},{"title":448,"date":453,"description":449,"timeToRead":454,"heroImage":450,"keyTakeaways":455,"articleBody":459,"faq":460},"2024-12-18","6 min read",[456,457,458],"Effective value stream management can accelerate a business's time to market, improve process visibility, and deliver enhanced customer experiences.","There are two types of key metrics in value stream management: Value Flow metrics and Value Realization metrics. The former help identify software delivery bottlenecks, while the latter measure what has been delivered.","Adopting a unified platform for the entire software development lifecycle can provide comprehensive visibility across personas and products, thus making businesses faster and more competitive in the market.","Software defines the pace of innovation, and that means all organizations face the same imperative: deliver better, more secure code faster while spending less. Success in this digital transformation journey is rapidly becoming the dividing line between market leaders and their competitors, requiring organizations to fundamentally rethink how they develop, secure, and deploy software.\n\nThe answer lies in value stream management - a proven approach that accelerates time to market, eliminates common obstacles like handoffs and broken feedback loops, and provides the visibility leaders need to ensure high-quality customer experiences.\n\n## Why value stream management?\nOver the past year, I’ve participated in more than 10 executive round tables, spoken to countless customers from around the world, and taken input from organizations such as the [DevOps Institute](https://www.devopsinstitute.com/) and the [Value Stream Management Consortium](https://www.vsmconsortium.org/).\n\nI’ve noticed a common theme when discussing transformation goals with industry leaders. They recognize their organization can’t stop at becoming a software company - they need to be a high-performing one.\n\nWhile it’s no small task to align business objectives with IT work, accelerate the software delivery process, and improve software quality, there are four key tenets organizations can follow to propel their digital transformation journeys while creating more business value with fewer resources:\n\n1. **Make developers more productive**: Improve developer experience to more effectively recruit and retain tech talent and make developers more productive so they ship better software faster.\n2. **Measure productivity and efficiency**: Measure impact across the software delivery lifecycle to improve operational efficiency.\n3. **Secure the software supply chain**: Reduce security and compliance risk.\n4. **Accelerate cloud migration**: Move to the cloud with the right security controls in place to minimize risk.\n\nSuccessfully implementing these tenets requires a structured approach that connects people, processes, and technology. Value stream management provides this framework, offering a proven roadmap that helps organizations systematically transform how they deliver software. The Value Stream Management Consortium has developed this implementation path into nine key stages: Go, Assess, Vision, Identify, Organize, Map, Connect, Inspect, and Adapt.\n\n## Implementing value stream management\nA critical step early in the roadmap is defining the **Vision**, which sets the parameters for inspecting value streams. It’s key that the business outcomes drive the vision. For example, if an organization’s vision is to be the first to market with a new product, speed of delivery is an important factor. However, if customer satisfaction and service reliability are the most essential elements, quality metrics will be at the top of the list.\n\nOnce you’ve identified the vision, the remaining steps in the roadmap ensure you have the people, process, and technology in place to support the vision:\n\n* The **Identify** and **Organize** stages are about the people. Organizations should visually represent the human aspect of these phases in a [value stream reference architecture](https://skilupit.thinkific.com/courses/value-stream-reference-architecture-paper).\n* The **Map** stage is about bringing together the correct people with a lean and efficient process. Value stream mapping not only helps visualize workflows but also highlights areas of waste and areas for continuous improvement.\n* The **Connect** stage is about enabling technology that automates the process and simplifies operations for cross-functional teams, reducing cognitive load, improving quality and security, and enabling faster value delivery.\n* Finally, the organization can then **Inspect** and **Adapt** their software value streams for optimization, continuously and in real time.\n\nThis roadmap ensures that individuals are connected to the technology and equipped to utilize it effectively. [Value stream discovery](#putting-value-stream-discovery-to-work) also plays a crucial role in mapping individuals and teams into a workflow strategically designed to enhance the developer and user experience.\n\nA platform approach is essential for successful implementation. According to Gartner’s [Market Guide for DevOps Value Stream Delivery Platforms](https://www.gartner.com/en/documents/3991050), value stream delivery platforms provide fully integrated capabilities that enable continuous delivery of software. These capabilities include planning, version control, continuous integration, test automation, release orchestration, continuous deployment and rollback monitoring, security testing, and analyzing value stream metrics. Value stream delivery platforms integrate with infrastructure and compliance automation tools to automate infrastructure deployment and policy enforcement.\n\n## Measuring success with value stream metrics\nThere are two types of metrics in value stream management: flow and realization.\n\nValue flow metrics define how we deliver software, from ideation through realization. These metrics measure the flow of business value, including insight into the efficiency, quality, and speed at which software progresses through the entire value stream. By understanding value flow metrics, organizations can identify bottlenecks and areas for improvement.\n\nDORA metrics are a subset of flow metrics. DORA metrics provide a quantitative measure of performance and include:\n\n1. **Deployment Frequency**: How often an organization deploys code to production. A higher deployment frequency indicates that the development team can deliver changes more rapidly, which reflects a more agile and efficient software development process.\n2. **Lead Time for Changes**: The time it takes for a code change to go from commit to deploy. A shorter lead time signifies that the team efficiently converts ideas into actual deployments, allowing for quicker delivery of features or fulfillment of customer requests.\n3. **Time to Restore Service**: How long it takes to recover from a service failure and restore normal operations. A lower time to restore service indicates a more resilient system and a capable response team, minimizing downtime and enhancing user experience.\n4. **Change Failure Rate**: The percentage of changes that result in a service degradation, including incidents, bugs, or any changes that necessitate a rollback. Lowering the change failure rate reflects improved quality in code changes and builds greater confidence in the development process.\n\nWhen analyzed in combination with metrics such as issue resolution lead time, cycle time, new issues, and deployments, these metrics offer a holistic view of the value stream’s efficiency. Using these measures wisely and in combination is important for identifying areas for improvement across the software development lifecycle.\n\nValue realization metrics measure tangible outcomes of delivery efforts. While traditional measures like revenue, sales, and profit margins provide financial insights, other key indicators such as net promoter scores and customer journey time capture equally important dimensions of realized value. While these lagging metrics reflect past performance, leading indicators like visitor traffic, customer reviews, and conversion rates offer valuable predictions of future success.\n\n## Putting value stream discovery to work\nMetrics and inspection come together with value stream discovery, which looks at an organization’s current and desired future state in the context of its technology value stream - the amount of time and resources required to move from idea and requirements to deployment and customer value. Value stream discovery also establishes a baseline to measure software delivery performance progress and identify the touchpoints in the process that don’t add value for the customer or the business. The outputs from value stream discovery allow the organization to configure a lean setup for a DevSecOps toolchain more easily.\n\nA unified platform is essential for achieving the envisioned future state while catering to developers' and customers' needs. This systematic approach fosters transparency - essential for effective value stream inspections - and underscores the significance of applying metrics to assess and understand the current state. Value stream discovery is pivotal for comprehensively mapping processes, personas, tools, interactions, and measurements into a singular view.\n\n## Software defines the pace of innovation\nWhen we examine the rationale behind inspecting software development value streams, it becomes clear that visibility is key to understanding how and what organizations deliver. Having the right metrics in place ensures that organizations can see how their software delivery is progressing, where bottlenecks and inefficiencies exist, and how to adapt for continuous improvement. Implementing an end-to-end DevSecOps platform combined with value stream discovery techniques equips organizations to continuously refine and enhance their delivery processes, accelerating innovation and paving the way for long-term success.",[461,464,467,470,473],{"header":462,"content":463},"How does Value Stream Management improve operational efficiency?","VSM improves operational efficiency by eliminating inefficiencies such as handoffs, broken feedback loops, and redundant processes. It connects people, processes, and technology, enabling cross-functional teams to work more collaboratively and productively, thereby accelerating time to market.",{"header":465,"content":466},"What is Value Stream Management and why is it important for software delivery?","Value Stream Management (VSM) is a strategic approach that optimizes software delivery by mapping and analyzing every step from ideation to customer value. It provides end-to-end visibility, identifies bottlenecks, and streamlines workflows, enabling organizations to deliver high-quality software faster while reducing costs and risks.",{"header":468,"content":469},"What are value flow metrics and how do they help in measuring efficiency?","Value flow metrics track the movement of business value through the entire software delivery lifecycle, from ideation to deployment. Metrics like deployment frequency, lead time for changes, and change failure rate help organizations identify bottlenecks, improve workflow efficiency, and enhance software quality.",{"header":471,"content":472},"How does Value Stream Management enhance security and compliance in software delivery?","VSM enhances security and compliance by integrating security checks and policy enforcement into the development pipeline. It ensures continuous monitoring and auditing, reducing risks and ensuring that security and compliance measures are consistently applied throughout the software lifecycle.",{"header":474,"content":475},"What is the role of Value Stream Discovery in optimizing software delivery?","Value Stream Discovery involves mapping the current state of software delivery processes to identify inefficiencies and value-adding activities. It provides a baseline for measuring performance and guides the configuration of lean, efficient DevSecOps toolchains, leading to faster, more reliable software delivery.","optimize-value-stream-efficiency-to-do-more-with-less-faster","content:en-us:the-source:platform:optimize-value-stream-efficiency-to-do-more-with-less-faster:index.yml","en-us/the-source/platform/optimize-value-stream-efficiency-to-do-more-with-less-faster/index.yml","en-us/the-source/platform/optimize-value-stream-efficiency-to-do-more-with-less-faster/index",{"_path":481,"_dir":416,"_draft":6,"_partial":6,"_locale":7,"config":482,"seo":483,"content":487,"type":436,"slug":511,"category":416,"_id":512,"_type":29,"title":484,"_source":30,"_file":513,"_stem":514,"_extension":33,"date":488,"description":485,"timeToRead":489,"heroImage":486,"keyTakeaways":490,"articleBody":494,"faq":495},"/en-us/the-source/ai/overcome-ai-sprawl-with-a-value-stream-management-approach",{"layout":9,"template":418,"articleType":445,"author":27,"featured":6,"gatedAsset":446,"isHighlighted":6,"authorName":11},{"title":484,"description":485,"ogImage":486},"Overcome AI sprawl with a Value Stream Management approach","Learn how an AI strategy based on Value Stream Management can stop AI sprawl and supply chain constraints and drive ROI.","https://res.cloudinary.com/about-gitlab-com/image/upload/v1751464425/vox2seqkjomxok5i56lh.png",{"title":484,"date":488,"description":485,"timeToRead":489,"heroImage":486,"keyTakeaways":490,"articleBody":494,"faq":495},"2024-12-12","7 min read",[491,492,493],"A strategic approach to AI involves linking it to value streams, ensuring AI is implemented precisely where constraints exist for optimal value delivery.","Transitioning from project-based to flow-based thinking enhances team alignment and effectiveness in AI implementations.","AI should be seamlessly integrated within an effective Value Stream Management approach to eliminate inefficiencies and support organizational goals.","With the move to [platform engineering](https://about.gitlab.com/the-source/platform/driving-business-results-with-platform-engineering/), organizations can consolidate complex toolchains and deliver higher-quality software faster and with greater security. Now, as software development teams look to AI for further improvements, we’re seeing a concerning trend: the implementation of disconnected AI point solutions, creating “AI sprawl.”\n\nThis AI sprawl amplifies all of the toolchain sprawl problems listed above. Implementing AI as a point solution for a small group of individuals at one stage in the value stream is just as likely to negatively impact your business outcomes as it is to improve them. An 8x boost in development speed means little if it leads to an 8-fold increase in integration overhead, maintenance time, and data reconciliation.\n\nHaving a strategy to address AI sprawl is just as important as having a strategy to minimize toolchains - but what does that strategy look like? It’s more than simply the consolidation of AI tools. It’s also about joining up lean processes and establishing clear responsibilities and lines of communication among cross-functional teams. [Value Stream Management](https://about.gitlab.com/solutions/value-stream-management/) holds the answer.\n\n## What is Value Stream Management?\n\nValue Stream Management is a methodology that aims to identify areas for improvement in a process to help drive the flow of business value more efficiently. When applied to software development, Value Stream Management involves identifying and mapping out all the steps involved in delivering value to customers, analyzing and measuring the flow of work through these steps, and continuously improving the process through automation. In this case, _value_ means any outcome that benefits the customer - whether that's new features, better performance, increased reliability, or enhanced security.\n\nImplementing a Value Stream Management strategy brings numerous benefits to an organization, such as increased transparency, improved communication, reduced waste and bottlenecks, quicker feedback loops, and ultimately faster time to market. By gaining visibility into the entire software delivery process, organizations can identify areas for improvement and make data-driven decisions to continuously optimize their value stream.\n\n## Building a value stream and AI strategy\n\nReducing AI sprawl starts with identifying bottlenecks in your software value stream: your process is only as fast as its slowest step. In DevSecOps, our goal is a business outcome achieved by an IT system that is delivered via a fast, safe, and secure supply chain. AI’s purpose should be to limit or remove any potential constraints in the supply chain. _This supply chain is a value stream_, which is why Value Stream Management is fundamental for building an AI strategy.\n\nOne example of a constraint in a software development process is time wasted waiting for security vulnerability information, a security representative, or vulnerability details. AI-powered security scanning and resolution can eliminate this bottleneck.\n\nIf we look at the [Implementation Roadmap for Value Stream Management](https://www.vsmconsortium.org/implementation/the-value-stream-management-implementation-roadmap) from the Value Stream Management Consortium, we can see several important steps in building the strategy before any technology decisions are made.\n\n![Value Stream Management implementation roadmap](https://res.cloudinary.com/about-gitlab-com/image/upload/v1752176119/Blog/pxo4bfr26ofrvp8gdlwz.png)\n\n_Diagram courtesy of [Value Stream Management Consortium](https://www.vsmconsortium.org/)_\n\nHere, I’ll focus on three steps. After assessing our current position and determining a vision of what we want to achieve, we must:\n\n- **Identify** our value streams\n- **Organize** roles and responsibilities for each of them\n- **Map** our people, processes, and technology to understand how all of this connects\n\nLet’s take a closer look at each stage.\n\n### 1. Identify value streams that deliver business outcomes\n\nUnderstanding your value streams is critical for AI adoption because it reveals _where_ and _how_ AI can actually improve delivery. The first step in building an AI strategy is to ask yourself: What are your organization’s main processes and workflows that drive business value? And which of those value streams have limitations or bottlenecks - points of constraint - that can be addressed with AI? The answers to these questions will tell you where AI can deliver the best results and what the end goal of using AI should be.\n\nWhen identifying your value streams, connect them directly to the business goals your IT systems are established to achieve. For AI to enable value delivery of the goal, you will need to consider the entire value stream, end-to-end, from idea to realization, with the required business objectives in mind. For example, certain value streams may have regulatory and compliance requirements that other value streams do not. These should be documented in the business goals of the value stream and used to define the potential AI requirements for each value stream.\n\n### 2. Organize people, processes, and tools around specific roles and responsibilities\n\nOver the last few decades, IT has moved from a **Project** mindset to a **Product** mindset. A project mindset focuses on delivering a specific result to fulfill a set of requirements, while a product mindset focuses on the bigger picture, including long-term success for users and customers. However, this shift has often just replaced activity-based silos with artifact-based silos - in other words, we've moved from teams organized by what they do (coding, testing, security) to teams organized by what they make (apps, APIs, platforms). Methods such as Scrum of Scrums try to address this, but typically swap under-collaboration with over-collaboration, where teams spend more time discussing than doing.\n\nWith Value Stream Management comes the concept of moving from **Product** to **Flow**. Instead of focusing on individual products, teams focus on how value moves - or flows - through the organization. Flow Engineering is then about designing team structures and handoffs around making that journey as smooth as possible.\n\nThis shift changes how we implement AI. AI solutions can’t focus on either a singular role or artifact. To remove handoffs and ensure alignment, AI must understand the scope and parameters of its operation, which teams it works with, and when it must be used.\n\nIn other words, AI must have a clearly defined role and responsibility, just as humans do. AI should understand its part in enabling flow along the value stream, working in an interactive manner with other people and AI tools. [Team Topologies](https://teamtopologies.com/) and a [Value Stream Reference Architecture](https://www.vsmconsortium.org/value-stream-reference-architectures) are invaluable at this stage because they provide a framework for designing and documenting a team structure that will help your team create value faster.\n\n### 3. Map the value stream to ensure everyone’s on the same page\n\nAI must be implemented precisely at the point of the constraint to provide the required benefit. In other words, adding AI to the wrong place in your process can actually make things worse. Let’s say security reviews are the slowest step in your workflow because developers need to spend time going back and forth with the security team to understand and address vulnerabilities. In this case, implementing AI only _before_ the security stage to help developers write more code faster is just going to make the bottleneck worse, because there will be even more code (and more potential vulnerabilities) for the team to sort through.\n\nBut how do we pinpoint where AI should operate? By mapping the existing workflow or golden path for a single value stream. This detailed mapping allows us to identify the precise point of constraint and determine whether AI will provide the required benefit to remove or reduce the impact.\n\nA value stream reference architecture lets us define team actions and map out an ideal future state, showing activities in their most efficient sequence and where AI fits into the bigger picture.\n\nA simplified example of a value stream map for developing a new software feature might look something like this: It starts with a customer request for a specific feature. Then, developers build the code, which is followed by testing and security scanning before the code is finally deployed to production. The value stream map should include each of these steps as a distinct section.\n\nContinuing with the example above, you’ve identified a bottleneck at the security stage. This point of constraint might have a couple of different potential solutions. Adopting an [end-to-end DevSecOps platform](https://about.gitlab.com/platform/) will allow you to shift security closer to the developers’ workflow to reduce the cognitive burden. You might also identify an AI solution to help developers understand and resolve vulnerabilities faster. In the previous stage, you would have defined the role and responsibility of AI in enabling flow along the value stream. Now, the value stream map captures this whole picture - which AI tools work where, what they're meant to achieve, and how they help value flow faster through your system.\n\n## Addressing AI sprawl: A value stream-based approach\n\nValue stream mapping helps prevent AI sprawl in several ways:\n\n- **Identifying** important value streams illustrates how different value streams can rely upon a single AI-powered platform to provide consistency and standards, particularly for regulatory needs.\n- **Organizing** people, processes, and tools around specific roles and responsibilities allows you to see how that platform should work holistically with context across the length of each value stream.\n- **Mapping** the value stream allows you to see where and how AI operates at different stages in the value stream so you can identify where duplicate efforts might be creating waste. This will enable flow in the value stream, remove handoffs, improve team alignment, and ensure that AI tools deliver on the organization’s goals.\n\nBy repeating the process for different value streams and AI solutions with different goals and roles, you’ll have a framework for your AI strategy.\n\n## Conclusion\n\nSoftware development teams can remove waste, improve operational efficiency, and ensure security by using technology to enhance and automate manual tasks across the software development lifecycle. Historically, this has been achieved through toolchain automation, but organizations can now leverage emerging technologies such as generative AI.\n\nBy identifying, organizing, and mapping AI to value streams, we can strategically implement AI to enable flow and remove waste. AI isn’t a standalone solution, but should rather be integrated into a holistic strategy with clear roles and responsibilities. By viewing AI through the lens of Value Stream Management, we see the real key to success: AI's effectiveness depends entirely on understanding how you manage your value streams.",[496,499,502,505,508],{"header":497,"content":498},"What role does AI play in accelerating security compliance within value streams?","AI enhances security compliance by automating vulnerability assessments, detecting threats in real time, and assisting teams with security policy enforcement. Within value streams, AI can reduce the time needed for compliance approvals and improve risk assessment accuracy.",{"header":500,"content":501},"How does Value Stream Management ensure AI is used effectively?","Value Stream Management ensures AI is integrated at points where it adds measurable value, such as removing bottlenecks, reducing manual effort, or improving security scanning. By mapping workflows end-to-end, organizations can strategically place AI tools where they will have the highest impact.",{"header":503,"content":504},"How can organizations align AI initiatives with business outcomes using Value Stream Management?","By identifying critical value streams and aligning AI with key business objectives, organizations ensure that AI investments drive tangible improvements in speed, cost efficiency, and product reliability. This approach prevents AI from becoming a siloed experiment and ensures it supports enterprise-wide innovation.",{"header":506,"content":507},"How does AI sprawl affect software development efficiency?","AI sprawl increases integration overhead, maintenance complexity, and data reconciliation issues. When multiple AI tools are introduced in isolation, they create redundant processes, fragmented workflows, and operational inefficiencies that slow down development instead of accelerating it.",{"header":509,"content":510},"What are the risks of implementing AI without a Value Stream Management strategy?","Without Value Stream Management, AI tools may be applied at inefficient points in the workflow, worsening bottlenecks rather than eliminating them. Duplicative AI models, lack of visibility, and misaligned automation can lead to wasted resources and increased operational costs.","overcome-ai-sprawl-with-a-value-stream-management-approach","content:en-us:the-source:ai:overcome-ai-sprawl-with-a-value-stream-management-approach:index.yml","en-us/the-source/ai/overcome-ai-sprawl-with-a-value-stream-management-approach/index.yml","en-us/the-source/ai/overcome-ai-sprawl-with-a-value-stream-management-approach/index",[414,441,480],{"ai":355,"platform":362,"security":97},1753981672477]