[{"data":1,"prerenderedAt":1079},["ShallowReactive",2],{"/en-us/the-source/ai/overcome-ai-sprawl-with-a-value-stream-management-approach/":3,"footer-en-us":50,"the-source-navigation-en-us":358,"the-source-newsletter-en-us":385,"overcome-ai-sprawl-with-a-value-stream-management-approach-article-hero-category-en-us":397,"overcome-ai-sprawl-with-a-value-stream-management-approach-the-source-gated-asset-en-us":421,"overcome-ai-sprawl-with-a-value-stream-management-approach-category-en-us":436,"overcome-ai-sprawl-with-a-value-stream-management-approach-the-source-resources-en-us":448,"overcome-ai-sprawl-with-a-value-stream-management-approach-article-hero-author-en-us":489},{"_path":4,"_dir":5,"_draft":6,"_partial":6,"_locale":7,"config":8,"seo":14,"content":18,"type":42,"slug":43,"category":5,"_id":44,"_type":45,"title":7,"_source":46,"_file":47,"_stem":48,"_extension":49},"/en-us/the-source/ai/overcome-ai-sprawl-with-a-value-stream-management-approach","ai",false,"",{"layout":9,"template":10,"articleType":11,"author":12,"featured":6,"gatedAsset":13},"the-source","TheSourceArticle","Regular","stephen-walters","source-lp-dora-insights-where-is-ai-really-driving-developer-productivity",{"title":15,"description":16,"ogImage":17},"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":15,"date":19,"description":16,"timeToRead":20,"heroImage":17,"keyTakeaways":21,"articleBody":25,"faq":26},"2024-12-12","7 min read",[22,23,24],"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.",[27,30,33,36,39],{"header":28,"content":29},"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":31,"content":32},"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":34,"content":35},"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":37,"content":38},"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":40,"content":41},"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.","article","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","yaml","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",{"_path":51,"_dir":52,"_draft":6,"_partial":6,"_locale":7,"data":53,"_id":354,"_type":45,"title":355,"_source":46,"_file":356,"_stem":357,"_extension":49},"/shared/en-us/main-footer","en-us",{"text":54,"source":55,"edit":61,"contribute":66,"config":71,"items":76,"minimal":346},"Git is a trademark of Software Freedom Conservancy and our use of 'GitLab' is under license",{"text":56,"config":57},"View page 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