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newsletter.",{"config":398},{"formId":399,"skeletonFieldCount":400,"formName":342,"hideRequiredLabel":21},1077,3,{"amanda-rueda":402,"andre-michael-braun":403,"andrew-haschka":404,"ayoub-fandi":405,"bob-stevens":406,"brian-wald":407,"bryan-ross":408,"chandler-gibbons":409,"cherry-han":410,"dave-steer":411,"ddesanto":412,"derek-debellis":413,"emilio-salvador":414,"erika-feldman":415,"george-kichukov":416,"gitlab":417,"grant-hickman":418,"haim-snir":419,"iganbaruch":420,"james-nyika":5,"jason-morgan":421,"jessie-young":422,"jlongo":423,"joel-krooswyk":424,"josh-lemos":425,"joshua-carroll":426,"julie-griffin":427,"kristina-weis":428,"lee-faus":429,"marco-caronna":430,"michelle-gill":431,"nathen-harvey":432,"ncregan":433,"rob-smith":434,"rschulman":435,"sabrina-farmer":436,"sandra-gittlen":437,"sharon-gaudin":438,"stephen-walters":439,"taylor-mccaslin":440},"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","Michelle Gill","Nathen Harvey","Niall Cregan","Rob Smith","Robin Schulman","Sabrina Farmer","Sandra Gittlen","Sharon Gaudin","Stephen Walters","Taylor McCaslin",{"ai":382,"platform":390,"security":386},[443],{"id":444,"title":445,"body":6,"category":446,"config":447,"content":450,"description":452,"extension":19,"meta":476,"navigation":21,"path":477,"seo":478,"slug":481,"stem":482,"type":483,"__hash__":484,"date":451,"timeToRead":453,"heroImage":454,"keyTakeaways":455,"articleBody":459,"faq":460},"theSource/en-us/the-source/ai/the-framework-financial-institutions-need-to-scale-ai-in-2026.yml","The framework financial institutions need to scale AI in 2026","ai",{"layout":8,"template":448,"featured":25,"author":26,"sourceCTA":449,"isHighlighted":25,"authorName":5},"TheSourceArticle","source-lp-scaling-ai-investments-in-financial-services",{"date":451,"title":445,"description":452,"timeToRead":453,"heroImage":454,"keyTakeaways":455,"articleBody":459,"faq":460},"2026-04-28","Learn how leading financial institutions are moving from AI experimentation to measurable, scalable results.","5 min read","https://res.cloudinary.com/about-gitlab-com/image/upload/v1777327834/mxapawizpwcpl7fjufvt.png",[456,457,458],"Most AI revenue forecasts are built on ambition, not analysis, and the consequences compound over time.","The institutions that have broken through share three traits: robust data governance, end-to-end platform engineering, and systematic measurement capabilities.","Scaling AI requires intelligent orchestration across the entire software lifecycle, not point solutions stitched together after the fact.","Financial services institutions are making unprecedented investments in AI, yet only 31% can actually track returns on that spending. According to a [2025 Harris Poll of 506 financial services executives](https://about.gitlab.com/resources/software-innovation-report-finserv/), 78% express high confidence in future AI results without any reliable measurement methods to back it up.\n\nMost organizations cannot distinguish between AI initiatives that are genuinely underperforming and those that are succeeding but failing to meet unrealistic expectations.\n\n## The gap no one is talking about\n\nOnly 12% of financial institutions have successfully implemented enterprise-wide AI strategies. The remaining 88% are cycling through pilots with no clear path to scale.\n\nWhat separates the two groups is not budget or board support but execution discipline: robust data governance, end-to-end platform engineering, and measurement capabilities that connect AI activity to actual business outcomes. \n\nEach of these traits works differently at the ground level. [**Data governance**](https://about.gitlab.com/the-source/security/compliance-at-the-speed-of-ai-reimagining-grc/) in financial services goes beyond access controls and into lineage: knowing where training data came from, how it has been transformed, and whether it can be reproduced for an auditor six months later. [**End-to-end platform engineering**](https://about.gitlab.com/the-source/platform/platform-engineering-its-about-culture-not-tools/) means AI agents operate on a consistent surface across planning, development, security review, and deployment, rather than context-switching between tools that do not share state. [**Measurement capability**](https://about.gitlab.com/solutions/visibility-measurement/) is the discipline of connecting AI activity to the outcomes leadership actually cares about, such as cycle time, defect escape rate, and time to remediate a vulnerability, not the usage metrics vendors ship by default.\n\nWhen organizations cannot measure what is working, they cannot make informed decisions about where to invest next. AI budgets grow, timelines slip, and confidence remains high while results stay elusive.\n\n## Why forecasts fail\n\nMost AI revenue projections in financial services are built on vendor case studies, best-case pilot results, and competitive pressure. They assume optimal execution, static markets, and customer adoption that moves on the institution's timeline.\n\nNone of those assumptions hold at scale. And in a regulated environment, the gap between a controlled proof of concept and enterprise-wide deployment is wider than most strategic plans account for.\n\nHere’s a common pattern: A firm runs a successful proof of concept in a sandboxed environment and projects enterprise-wide results based on those numbers. What the pilot never surfaced was the integration complexity with production systems, the change approval cycles required in a regulated environment, and the staff adoption curves that vary significantly across business lines. By the time those variables compound, the original projection bears little resemblance to the actual implementation timeline or cost.\n\nThe institutions that have broken through treat AI investment the way they treat any other major capital allocation decision: with clear success criteria, realistic timelines, and accountability frameworks that hold up to scrutiny.\n\n## What measurement actually looks like\n\nMost organizations are measuring AI adoption. Very few are measuring AI impact. Those are not the same thing.\n\nSeats licensed, prompts sent, and suggestions accepted tell you whether people are using the tools. They do not tell you whether the investment is moving the business. The gap between those two things is where most AI ROI narratives fall apart.\n\nThe metrics that matter to a CIO or CISO sit one layer deeper: cycle time from commit to production, defect escape rate, time to remediate a critical vulnerability, deployment frequency. Tracked consistently over several quarters, these connect AI activity to the outcomes leadership actually cares about. None of them require new instrumentation. They require the discipline to prioritize them over the usage metrics that are easier to report.\n\n## Measurement as regulatory posture\n\nFor financial institutions, measurement discipline is also becoming a regulatory expectation. Supervisory frameworks have signaled that AI systems operating in regulated workflows need explainable governance, documented performance over time, and evidence that human accountability has been preserved. Institutions that cannot produce those artifacts on request are exposed, regardless of how well their models are performing.\n\nFor institutions operating under frameworks like DORA, SR 11-7, or the EU AI Act, the ability to document AI performance over time, demonstrate explainable governance, and evidence human accountability is both a competitive advantage and a supervisory expectation. Organizations that build that capability proactively are better positioned for the next board presentation and the next regulatory examination.\n\n## The foundation that makes scaling possible\n\nMeasurement discipline and governance frameworks matter, but they need something to operate on. In a regulated financial institution, where development, security review, compliance sign-off, and deployment touch different teams across different systems, the orchestration layer is what determines whether AI generates isolated productivity gains or institution-wide value.\n\nBringing human teams and AI agents together across the entire software development lifecycle is what converts AI investment into measurable AI value. Successfully scaling AI requires [intelligent orchestration](https://about.gitlab.com/the-source/ai/software-development-enters-the-orchestration-era/) that spans DevOps, security, and compliance workflows on a unified platform. Point solutions stitched together after the fact cannot deliver it.\n\nThe [$750 billion opportunity](https://about.gitlab.com/the-source/ai/to-maximize-the-750b-ai-opportunity-human-innovation-is-key/) AI represents in financial services is real. Realizing it requires something the industry already knows how to do: measure what matters.",[461,464,467,470,473],{"header":462,"content":463},"Why can't most financial institutions measure their AI ROI?","Only 31% of financial services institutions can track returns on AI spending, according to a 2025 Harris Poll of 506 executives. Most organizations measure AI adoption (seats licensed, prompts sent, suggestions accepted) rather than AI impact, leaving them unable to distinguish underperforming initiatives from successful ones missing unrealistic expectations.",{"header":465,"content":466},"What separates the 12% of financial institutions successfully scaling AI?","The 12% with enterprise-wide AI strategies share three traits: robust data governance (including data lineage and reproducibility), end-to-end platform engineering across the software lifecycle, and systematic measurement capabilities. The differentiator is execution discipline, not budget or board support.",{"header":468,"content":469},"Which metrics actually measure AI impact in financial services?","Meaningful metrics include cycle time from commit to production, defect escape rate, time to remediate critical vulnerabilities, and deployment frequency. Tracked consistently over several quarters, these connect AI activity to outcomes leadership cares about, unlike vendor-default usage metrics that only show tool adoption.",{"header":471,"content":472},"Why do AI revenue forecasts fail in regulated financial environments?","AI projections often rely on vendor case studies, best-case pilot results, and assumptions of optimal execution. They overlook integration complexity with production systems, change approval cycles required in regulated environments, and varying staff adoption curves across business lines, causing original projections to diverge from actual implementation.",{"header":474,"content":475},"How does AI measurement support regulatory compliance?","For institutions under DORA, SR 11-7, or the EU AI Act, supervisory frameworks require explainable governance, documented AI performance over time, and evidence of preserved human accountability. Measurement discipline serves as both competitive advantage and regulatory posture, preparing organizations for board presentations and supervisory examinations.",{},"/en-us/the-source/ai/the-framework-financial-institutions-need-to-scale-ai-in-2026",{"config":479,"title":480,"description":452},{"noIndex":25},"How financial institutions can scale AI in 2026","the-framework-financial-institutions-need-to-scale-ai-in-2026","en-us/the-source/ai/the-framework-financial-institutions-need-to-scale-ai-in-2026","article","2nCVWM1-RGaoUeoeRODIzFfEhp9HSCzlwMg97LBWWwg",1777404606329]