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newsletter.",{"config":398},{"formId":399,"formName":342,"hideRequiredLabel":21},1077,{"amanda-rueda":401,"andre-michael-braun":402,"andrew-haschka":403,"ayoub-fandi":404,"bob-stevens":405,"brian-wald":406,"bryan-ross":407,"chandler-gibbons":408,"cherry-han":409,"dave-steer":410,"ddesanto":411,"derek-debellis":412,"emilio-salvador":413,"erika-feldman":414,"george-kichukov":415,"gitlab":416,"grant-hickman":417,"haim-snir":418,"iganbaruch":419,"jason-morgan":420,"jessie-young":421,"jlongo":422,"joel-krooswyk":423,"josh-lemos":424,"joshua-carroll":425,"julie-griffin":426,"kristina-weis":427,"lee-faus":428,"marco-caronna":429,"michelle-gill":5,"nathen-harvey":430,"ncregan":431,"rob-smith":432,"rschulman":433,"sabrina-farmer":434,"sandra-gittlen":435,"sharon-gaudin":436,"stephen-walters":437,"taylor-mccaslin":438},"Amanda Rueda","Andre Michael Braun","Andrew Haschka","Ayoub Fandi","Bob Stevens","Brian Wald","Bryan Ross","Chandler Gibbons","Cherry Han","Dave Steer","David DeSanto","Derek DeBellis","Emilio Salvador","Erika Feldman","George Kichukov","GitLab","Grant Hickman","Haim Snir","Itzik Gan Baruch","Jason Morgan","Jessie Young","Joseph Longo","Joel Krooswyk","Josh Lemos","Joshua Carroll","Julie Griffin","Kristina Weis","Lee Faus","Marco Caronna","Nathen Harvey","Niall Cregan","Rob Smith","Robin Schulman","Sabrina Farmer","Sandra Gittlen","Sharon Gaudin","Stephen Walters","Taylor McCaslin",{"ai":382,"platform":390,"security":386},[441,482],{"id":442,"title":443,"body":6,"category":444,"config":445,"content":448,"description":450,"extension":19,"meta":474,"navigation":21,"path":475,"seo":476,"slug":478,"stem":479,"type":480,"__hash__":481,"date":449,"timeToRead":451,"heroImage":452,"keyTakeaways":453,"articleBody":457,"faq":458},"theSource/en-us/the-source/ai/beyond-the-quick-win-how-enterprises-can-scale-ai-in-2026.yml","Beyond the quick win: How enterprises can scale AI in 2026","ai",{"layout":8,"template":446,"featured":25,"author":26,"sourceCTA":447,"isHighlighted":25,"authorName":5},"TheSourceArticle","application-security-in-the-digital-age",{"date":449,"title":443,"description":450,"timeToRead":451,"heroImage":452,"keyTakeaways":453,"articleBody":457,"faq":458},"2026-03-30","Turn early AI wins into lasting competitive advantage with three enterprise strategies to build on your AI momentum.","5 min read","https://res.cloudinary.com/about-gitlab-com/image/upload/v1751463704/u3dshy4qn6rtrklfalx7.png",[454,455,456],"Shadow AI creates unchecked costs and redundant solutions. Enterprises need governance frameworks that track which agents are running, the resources they consume, and the business value they deliver.","Point solutions that speed up isolated tasks create downstream bottlenecks. Agentic systems that manage end-to-end processes serve as connective tissue between teams and eliminate review backlogs.","AI systems are only as smart as the data they can access. A unified data architecture gives agents the institutional context they need to deliver results beyond technically sound output. ","Congratulations! With AI in your toolbelt, developers are shipping code faster, costs are down, and you started 2026 with concrete AI wins.\n\nBut, you’re about to face a new set of problems. \n\nThe decisions you make about scaling, governance, and integration in 2026 will set the foundation for your organization’s performance for years to come. Get it right, and the wins you’ve seen in the past year will compound. Get it wrong, and your growth will plateau or worse.\n\nHere are three ways to keep the AI momentum going. \n\n## Govern what you’ve built\n\nAt some point in the coming year, your CFO may ask why your cloud costs have skyrocketed and you’ll likely find that teams across the organization are using AI without approval or oversight from the organization. This shadow AI usage leads to multiple teams solving a similar problem using different agentic AI tools. \n\nThe spirit of experimentation is exactly what’s driving AI adoption and the discovery of helpful, quick fixes. But as teams spin up ad hoc solutions from development tools, cloud platforms and countless other sources, the lack of centralized oversight becomes an issue that you can’t afford to ignore. All of these agents will increase cloud and compute costs, but an even greater challenge is the risk to your intellectual property (IP). \n\nWhen developers use unvetted AI tools to ship faster, they end up pasting in proprietary code to debug or sharing internal architecture docs for context. That information may be stored, shared, or used to train third-party models, and lead to a breach of proprietary source code or compliance risks when customer data is pulled into unvetted AI tools. \n\nOrganizations need to implement [governance frameworks](https://about.gitlab.com/the-source/security/ai-agents-are-reshaping-software-what-cisos-need-to-know/) that track which agents are running, what data they have access to, and how they interact with each other and with critical systems. These frameworks give your teams clear guidelines on how to move fast without creating a security breach. \n\nWith governance in place, organizations will have lower cloud bills *and* they’ll be able to prove exactly how their AI systems are interacting with sensitive data. Developers still need the ability to experiment with new AI tools. But the most successful organizations will find the right [balance between AI innovation and governance](https://about.gitlab.com/the-source/security/compliance-at-the-speed-of-ai-reimagining-grc/).\n\n## Build agents that go beyond automating tasks\n\nYou should already be using AI to automate all of your safely repeatable tasks. If your teams tried to implement AI, but aren’t seeing results, the problem might be the approach. Many teams believe AI doesn’t work for them because they took a buckshot approach and used AI for every possible scenario. Instead, try two AI-integrated tasks 500 times rather than 500 tasks twice. This targeted strategy allows you to continually optimize and hone your AI solutions before adding more. \n\nOnce your repeatable tasks have been automated, it’s time to use AI holistically across your pipeline. The organizations that see long-term success with AI will be those that build [agentic AI systems](https://about.gitlab.com/the-source/ai/transform-development-with-agentic-ai-the-enterprise-guide/) to handle complex, multistep processes, such as code review cycles and pipeline failures. \n\nAI agents can serve as the connective tissue between teams, handling the administrative work and streamlining review cycles that create bottlenecks. For code reviews, this looks like routing reviews, surfacing relevant context, and keeping workflows moving. For pipeline durations and reliability, agents can diagnose pipeline failures and prioritize runs, saving developers time and mental bandwidth.\n\nBy managing end-to-end processes and overseeing the full software delivery pipeline, these agents move beyond isolated use cases. Instead, they own the workflows between tasks, removing dead time, friction, and coordination overhead. \n\n## Unify your data architecture\n\nYour AI is only as smart as the data it can access. Context is spread across different systems that operate in silos. AI might be able to write technically sound Python code, but without access to design decisions recorded in a wiki, compliance requirements buried in Jira, or a customer data model that exists only in the company’s customer relationship management (CRM) system, the code is strategically useless.\n\nThat fragmented data landscape is the main obstacle between your current AI performance and the full potential of your AI investments. To close that gap, you need to build a data architecture that actually supports the AI systems you're already running.\n\nWhen you prioritize [DevOps modernization](https://about.gitlab.com/the-source/platform/more-code-more-bottlenecks-tackling-the-ai-paradox/) with unified data and context infrastructure, you'll see faster deployments and reduced security risk. Plus, your AI systems can finally draw on institutional knowledge instead of working around it.\n\n## Are you reimagining? Or just automating?\n\nThis is the question that will separate AI leaders in 2026: Are you reimagining the way work gets done or are you just automating old processes? \n\nSucceeding in this next chapter of AI means integrating agentic capabilities into business operations, rather than adopting point solutions and haphazardly addressing challenges across teams. \n\nSetting your organization up for success starts with building a strong foundation: governance that enables experimentation, agentic systems that solve challenges holistically, and a data architecture that provides agents with the context they need. \n\nAccomplish these three things, and your AI gains will turn into lasting competitive advantage.",[459,462,465,468,471],{"header":460,"content":461},"What is shadow AI and why is it a problem for enterprises?","Shadow AI occurs when teams use AI tools without organizational approval or oversight. It leads to redundant solutions across teams, skyrocketing cloud and compute costs, and serious IP risks — including proprietary code or customer data being shared with unvetted third-party models.",{"header":463,"content":464},"What should an enterprise AI governance framework track?","An effective AI governance framework tracks which agents are running, what data they can access, and how they interact with each other and critical systems. This gives teams clear guidelines to move fast without creating security breaches or compliance risks.",{"header":466,"content":467},"Why do many enterprise AI implementations fail to deliver results?","Many teams take a buckshot approach — applying AI to every possible scenario without depth. A more effective strategy is to run two AI-integrated tasks 500 times rather than 500 tasks twice, allowing continuous optimization before expanding AI's scope.",{"header":469,"content":470},"How do agentic AI systems reduce bottlenecks in software delivery?","AI agents act as connective tissue between teams by handling administrative work, routing code reviews, surfacing relevant context, and diagnosing pipeline failures. By owning end-to-end workflows — not just isolated tasks — they eliminate dead time, friction, and coordination overhead.",{"header":472,"content":473},"Why does data architecture determine the ceiling of your AI performance?","AI can only act on data it can access. Without connections to wikis, compliance systems, or CRM data, AI produces technically sound but strategically useless output. A unified data architecture gives agents the institutional context needed to close the gap between current AI performance and its full potential.",{},"/en-us/the-source/ai/beyond-the-quick-win-how-enterprises-can-scale-ai-in-2026",{"config":477,"title":443,"description":450},{"noIndex":25},"beyond-the-quick-win-how-enterprises-can-scale-ai-in-2026","en-us/the-source/ai/beyond-the-quick-win-how-enterprises-can-scale-ai-in-2026","article","nObNyPIQP67AQGSEkgmvWAxaUDIibIwr5suIovGD_IE",{"id":483,"title":484,"body":6,"category":444,"config":485,"content":487,"description":488,"extension":19,"meta":495,"navigation":21,"path":496,"seo":497,"slug":499,"stem":500,"type":480,"__hash__":501,"date":489,"timeToRead":451,"heroImage":452,"keyTakeaways":490,"articleBody":494},"theSource/en-us/the-source/ai/building-ai-teams-that-move-fast-and-don-t-burn-out.yml","Building AI teams that move fast and don’t burn out",{"layout":8,"template":446,"featured":21,"author":26,"sourceCTA":486,"isHighlighted":25,"authorName":5},"global-devsecops-report-2025",{"title":484,"description":488,"date":489,"timeToRead":451,"heroImage":452,"keyTakeaways":490,"articleBody":494},"Learn practical strategies for managing AI talent, accelerating decisions, and keeping top engineers engaged in fast-moving environments.","2025-12-09",[491,492,493],"AI experts bring valuable depth but their strong opinions can create decision paralysis that slows down delivery timelines.","Four decision-making frameworks help teams move faster: assign single decision owners, separate planning from doing, require evidence for changes, and match communication to technical depth.","Retention requires meaningful challenges, transparent career paths, and dedicated time for learning in a rapidly evolving field.","Many leaders are focused on hiring top AI talent right now, but few are preparing for what happens next.\n\nWhen you set out to build an AI team, you’ll probably start by looking for people with natural curiosity, persistence, and broad technical skills spanning AI, machine learning, and software development. The right hires can work at the frontier, maintain deep knowledge, track emerging developments, and distinguish meaningful advances from marketing noise.\n\nBut assembling talented people and establishing a strong AI center of excellence is the straightforward part. The real test begins afterward: managing strong opinions, breaking decision deadlock, and keeping experts engaged when the field changes weekly.\n\n## The challenge of managing expertise\nIn my experience, the qualities that make AI engineers valuable also create management complexity. When you have 10 experts, you get 10 excellent approaches to each problem and 10 discussions requiring your input before anything launches. These are exactly the team members you need because they possess rare, specialized knowledge. They also hold firm perspectives, which can trigger extended debates and competing proposals where everyone has valid technical reasoning, but you still have to choose a single path forward.\n\nThis dynamic undermines speed. And in the age of AI, if something takes longer than two months from conception to production, it’s already stale. A large language model (LLM) may deliver similar capabilities first. Not every engineer can maintain this tempo, stay current with research while writing production code, and remain focused on goals when priorities frequently shift.\n\nYour role as a leader requires keeping the team advancing quickly, reaching conclusions that avoid infinite deliberation cycles, and assessing whether your current roster still fits the demands. Traditional engineering leadership approaches often fail in these conditions.\n\nHere's what produces results.\n\n## Four strategies for alignment and momentum\nBegin by simplifying your organizational hierarchy so extra management tiers don't extend decisions across multiple weeks. Compress your schedules to reflect innovation velocity and leverage deadline pressure to determine when to abandon failing experiments, when to develop talent, and when to provide an off-ramp. Establish clear, uncompromising expectations for adaptability and on-time delivery.\n\nAfter laying that groundwork, provide your specialists with these four strategies for making decisions and moving at the pace of AI:\n\n**Assign a single owner for every decision.** Once you've collected input, designate one person to make the final call. Set time limits for discussions with explicit criteria for success. Don't allow simultaneous debates across multiple communication channels.\n\n**Distinguish between planning and implementation.** After reaching a decision, commit to that direction for a defined timeframe. During this period, pause questions about the approach itself. Theoretical debates will continue endlessly if you allow them to. Choose a direction and collect real performance data before entertaining changes.\n\n**Require evidence, not just proposals, to change course.** Your success threshold doesn't need perfection; \"better than before\" can suffice. If a new method shows gains in your evaluation measures, give it serious consideration. If it doesn’t, move on quickly.\n\n**Communicate in your experts' language.** When working with people who reason through model architectures, embedding dimensions, and evaluation frameworks, don't force everything into business language. Business outcomes matter, but you're leading technical professionals solving technical challenges. Use technical vocabulary when appropriate, strategic language when it serves the goal, and understand which context requires which approach.\n\nThese strategies will help you ship faster and make better decisions. However, even with flawless implementation, you operate in an environment where rivals will launch breakthrough capabilities every few weeks and constantly try to recruit your strongest engineers. These strategies provide speed; keeping your talent is what keeps you in the game.\n\n## Maintaining momentum long-term\nAfter hiring strategically, implementing decision frameworks, and beginning delivery, you face the challenge of retention.\n\nHere’s what separates organizations that keep their AI talent from those that lose it:\n\n**Provide meaningful work.** The lack of a compelling vision, uninteresting challenges, and endless unresolved discussions destroy AI team motivation. Connect their contributions to a larger impact and reach decisions that enable actual progress.\n\n**Establish advancement opportunities.** AI positions have grown more complex faster than most organizational career structures have adapted. Define senior AI leadership in your company. Build transparent progression paths with milestones recognizing both technical mastery and strategic contribution. Leading AI professionals choose organizations offering visible growth potential, not just employment.\n\n**Support ongoing development.** The opportunity to tackle frontier problems, learn continuously, and stay ahead is why your team members joined the team in the first place. Protect space for this by enabling conference attendance, research time, and experimentation. This maintains your elite team's effectiveness in a field undergoing constant transformation.\n\nTechnology advances regardless of organizational readiness. Models will continue to improve, and competitors will continue to innovate. Success comes from engineers who deliver quickly without compromising quality, leaders who coordinate exceptional minds without limiting creativity, and teams that ship reliably despite operating in chaotic conditions. Support them well, and your organization will remain at the forefront of innovation.",{},"/en-us/the-source/ai/building-ai-teams-that-move-fast-and-don-t-burn-out",{"config":498,"title":484,"description":488},{"noIndex":25},"building-ai-teams-that-move-fast-and-don-t-burn-out","en-us/the-source/ai/building-ai-teams-that-move-fast-and-don-t-burn-out","waLBs5OQ1I96QJ6zn84SxsvEKR52ly5F_p71hJc7Cfw",1776432766277]