Benefits of Mainframe Modernization

AI field note: Modernization is one of the most underappreciated forces for innovation (Southwest Airlines shows us why). When legacy systems finally get updated, two big things happen: 1️⃣ You can start improving services that were effectively frozen in time. 2️⃣ The cost and complexity of running those services drops—freeing up time, money, and focus for what’s next. But for a long time, modernization just wasn’t worth it. The juice wasn’t worth the squeeze. Projects kicked off with long planning cycles, manual analysis, and a lot of upfront investment—often without a clear path to value. That’s starting to change. AI is shifting what’s possible. It can help teams understand legacy code faster, accelerate planning, and reduce the rework that usually slows things down. With that, modernization becomes more viable, more targeted, and more focused on outcomes. It’s not just about updating systems—it’s about unlocking capacity, reducing friction, and making space for the next wave of innovation. Take Southwest Airlines. They needed to modernize their crew leave management system—a critical platform for scheduling, time off, and operations. Over time, the system had become harder to update. Technical debt made it difficult to plan changes, and documentation was limited. Each update required hours of manual analysis just to understand what the system was doing—slowing delivery and tying up valuable resources. But the pressure to modernize was growing. As operations evolved and employee needs changed, the system needed to be more flexible, more reliable, and easier to maintain. PwC partnered with Southwest to take a different approach. Using GenAI, we analyzed the legacy code and generated user stories—effectively mapping the system’s behavior and identifying what needed to change. That work: ⚡️ Cut backlog creation time by 50% 🌟 Produced user stories accepted 90% of the time without major rework 💫 Freed up 200+ hours across teams More importantly, it gave the team clarity and momentum—turning a slow, manual planning process into a faster, more focused path forward. Less time untangling the past. More time building what’s next—for their teams and their travelers. There’s never been a better time to modernize.

𝐌𝐨𝐬𝐭 𝐛𝐚𝐧𝐤𝐬 𝐚𝐫𝐞𝐧’𝐭 𝐬𝐭𝐫𝐮𝐠𝐠𝐥𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐀𝐈. 𝐓𝐡𝐞𝐲’𝐫𝐞 𝐬𝐭𝐫𝐮𝐠𝐠𝐥𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐂𝐎𝐁𝐎𝐋. This year, Morgan Stanley quietly did something bold. They built DevGen(.)AI a GPT-based tool trained not on GitHub, but on their own legacy code: COBOL, JCL, SAS, in-house Perl scripts. And in just a few months: ✔ 9 million lines of legacy code processed ✔ 280,000 developer hours saved ✔ 15,000+ engineers using it globally This isn’t about generating new code. It’s about making old code readable, documenting logic buried in 40-year-old systems so modern developers can rewrite it in Python or Java. Why it matters: Most AI copilots can’t help here. Legacy logic doesn’t live on the internet. It lives in ancient batch jobs, undocumented macros, and formats no modern LLM was trained on. Morgan Stanley’s edge? They fine-tuned the model on proprietary systems. Now they’re getting cleaner outputs, faster onboarding, and tighter governance, with no hallucinations. Meanwhile, off-the-shelf tools struggle with context, privacy, and legacy syntax. 𝐓𝐡𝐢𝐬 𝐢𝐬 𝐰𝐡𝐞𝐫𝐞 𝐆𝐞𝐧𝐀𝐈 𝐦𝐨𝐯𝐞𝐬 𝐟𝐫𝐨𝐦 𝐚𝐬𝐬𝐢𝐬𝐭𝐚𝐧𝐭 𝐭𝐨 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐚𝐫𝐜𝐡𝐚𝐞𝐨𝐥𝐨𝐠𝐢𝐬𝐭. The economics are compelling too. At ~$100/hour, those 280,000 saved hours equal $28 million unlocked. The payback period? Less than 24 months. Here’s the real strategy shift: Modernization is not a side project. It’s been a board-level priority for years now. But now, something meaningful can be done. 60 - 80% of IT budgets go to maintenance And COBOL talent is vanishing (if not already) If you’re not using AI to decode your own systems by 2025, your risk isn’t just technical. It’s institutional memory loss. Your codebase is your architecture. Your constraints. Your truth. Modernization isn't about speed. It's about clarity. 𝐍𝐨𝐭 𝐚 𝐌𝐨𝐫𝐠𝐚𝐧 𝐒𝐭𝐚𝐧𝐥𝐞𝐲? You don’t need to be. Here’s what smaller banks and tech teams can do: → Start with documentation, not translation. Use AI to generate English summaries of your core legacy apps first. Focus on clarity, not code conversion. Tools like GPT-4 can already help here without full custom training. → Fine-tune with what you own. If your codebase is too proprietary for public copilots, use small internal LLMs or embeddings over your repos. Even a basic RAG setup over COBOL comments can lift onboarding speed. → Prioritize by exposure, not convenience. Don’t modernize what’s easiest. Modernize what’s riskiest. Start with systems that touch audit, risk, or customer data. → Invest in “translators,” not just devs. Your most critical hires aren’t Python experts. They’re the ones who can bridge legacy logic and modern architecture. Pair them with AI and scale their impact. The goal isn’t to match Morgan Stanley’s throughput. The goal is to stop bleeding institutional knowledge with every retirement. If your AI roadmap doesn’t include your oldest systems, you’re modernizing the front while the foundation crumbles

#Cloud + #AI: The CxO Playbook for Mainframe Modernization Despite all the innovation buzz, IBM reports that ~70% of global business transactions (by value) still run on mainframes. Mainframes are the dependable backbone in banking, insurance, healthcare, government, and logistics, to name a few. But now dependability isn’t enough in an era of macroeconomic volatility, accelerated tech disruptions and everchanging customer expectations— it’s time to modernize mainframes. As competition increases, new ecosystem collaborations form and businesses evolve, mainframe modernization needs to be become a C-suite imperative. Three Options to Mainframe Modernization 1) Review Contracts: to identify cost-savings, negotiate terms, reduce OpEx 2) Optimize on the Mainframe: continue to drive efficiency on legacy 3) Modernize to the Cloud: enhance flexibility, scalability and innovation Let’s focus on the boldest move, 3) Modernize to the cloud. So why can’t you afford to wait any longer? 💣 Talent Crisis = Talent Debt Most COBOL experts are 50–70 years old and retiring fast according to AFCEA. New developers aren't learning legacy programming languages, creating a dangerous skills gap that threatens mission-critical operations. 💸 Mainframes OpEx Costs Rising SW licenses, HW costs, and support contracts. These costs bleed budgets and block reinvestment in innovation. 🐘 Lack of Agility Creates a Speed-to-Value Issue Mainframes lack the agility to respond quickly to market shifts or regulations, making speed-to-value a major challenge. Cloud + AI: The Modernization Power Duo ☁️ Cloud provides the ecosystem partner community, flexibility, scale and economics. 🤖 AI (including GenAI + Agentic AI) decodes legacy systems, reverse engineers COBOL, and finds smarter migration paths. 🔐 Together, they enable continuous delivery, improve security, and unlock trapped mainframe data - helping businesses shift from survival to reinvention. Modernizing isn’t just a tech decision. It’s a CxO mandate. CEOs, CFOs, and CROs are realizing this isn’t just an IT problem - it’s about resilience, agility, and future growth. So mainframe modernization isn’t optional. With Cloud + AI, it’s finally feasible, safe to deliver and strategic. Stay tuned for our next post / video with my good friend Sridhar (Sri) Narasimhan who talks to us about The Urgency of Mainframe Modernization from #SanFrancisco Accenture #CloudFirst #MainframeModernization #Innovation Andy Tay | David Parker | Michael Abbott | Rob Pinkham | Ram Ramalingam | Jeff Emerson | Seeju Kumar | Steve Murphy | Jon Hart | Herman Eggink | Joel Rosenberger | Robin Wooley | Christine Disco | Valerio Romano | Duncan Eadie | Sid Nair | Chris Howarth | Sanjay Mehta | Jennifer Jackson | Chris Wegmann | Scott Alfieri | Chetna Sehgal | Shivani Vora | Max Furmanov | Melissa Besse | Susan Whitehouse

Legacy banking infrastructure costs global markets $400B annually in operational inefficiency Major banks spend $135M on average to maintain COBOL systems built in the 1960s. Engineering teams manage billions of lines of outdated code while new digital challengers build on modern stacks. Core Banking Systems Status: - 43% run on COBOL - 55% operate on mainframes - 72% require overnight batch processing - 85% face critical skill shortages - 92% report rising maintenance costs Traditional infrastructure limits innovation severely. International transfers take 5 days to settle. System changes require 18-24 months to implement. Basic feature updates cost millions in testing. Current Infrastructure Challenges: - Average COBOL programmer age: 55 years - System update cost: $200M-$350M - Batch processing delay: 12-24 hours - Integration time: 6-12 months - Technical debt ratio: 35% Google Cloud and major banks now invest in modernization. Santander's Gravity platform enables parallel cloud processing. Goldman Sachs rebuilt their transaction banking stack completely digital. Modernization Results: - Settlement time: Minutes vs days - Operating costs: -65% - Integration speed: 4x faster - System uptime: 99.99% - Feature deployment: Daily vs quarterly J.P. Morgan demonstrates the impact through numbers. Their new platform processes $1T annually. Transaction costs dropped 90%. Settlement times decreased from days to seconds. Want our complete banking infrastructure report? Drop a "report" below

Modernizing a legacy system doesn’t have to take months. Southwest Airlines and PwC proved it can be done in five weeks. Together, they transformed Southwest’s crew attendance and leave management system using GenAI—turning complex source code into clear, actionable requirements. ✅ 50% reduction in backlog creation time ✅ 600+ high-quality user stories generated by AI ✅ 200+ hours saved across engineering, tech, and business teams This isn’t just a systems upgrade. It’s a new model for how GenAI can transform planning, boost team alignment, and fast-track modernization at scale. As Marty Garza, VP of Air Operations Technology at Southwest, shared: "PwC’s approach gave us a smarter, faster, more accurate way to modernize. Their use of GenAI helped free up our teams to focus on what matters most—solving problems and driving innovation." Read the full story here: https://lnkd.in/eq_sfkSD Tim Mattix, Raymond Hearrell #GenAI #LegacyModernization #AITransformation #PwC #SouthwestAirlines

GenAI offers financial services leaders a transformative approach to mainframe modernization by automating the rebuilding of outdated codebases. This enhances system maintainability and proactively ensures integration with agile technologies like the cloud, preparing institutions for the next wave of digital infrastructure solutions. When it comes to data migration, often a complex task, GenAI can save hundreds of hours. It can automate the cleansing, transformation, and normalization of legacy data, ensuring high-quality, formatted data migration with minimal errors –– an extremely resource-effective approach. Equally important, GenAI’s predictive maintenance capabilities also provide real-time monitoring and anomaly detection, allowing institutions to proactively address potential issues and maintain operational efficiency. These abilities can be lifesavers, especially when legacy tech presents vulnerabilities that require swift remediation. For a closer look at updating financial services infrastructure with GenAI, check out this article (with great case studies) from my EY colleagues Paul Sussex, Alim Williams, and James Hwang: https://lnkd.in/eEfYPaB6

The takeaway: IBM #Mainframe modernization has evolved from a necessary risk to a strategic accelerator for digital transformation. Key insights in this article from Becky Etheridge: 1. 𝐌𝐚𝐢𝐧𝐟𝐫𝐚𝐦𝐞 𝐌𝐨𝐝𝐞𝐫𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐌𝐢𝐧𝐝𝐬𝐞𝐭 𝐒𝐡𝐢𝐟𝐭 CIOs now view mainframe transformation as a strategic enabler of innovation and AI readiness, rather than just a cost-cutting exercise. 2. 𝐇𝐲𝐛𝐫𝐢𝐝 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡 𝐄𝐥𝐢𝐦𝐢𝐧𝐚𝐭𝐞𝐬 𝐅𝐚𝐥𝐬𝐞 𝐂𝐡𝐨𝐢𝐜𝐞𝐬 Organizations no longer need to choose between safe replatforming or risky refactoring—modern platforms [such as Heirloom®] integrate both approaches based on business needs. 3. 𝐋𝐞𝐠𝐚𝐜𝐲 𝐄𝐱𝐩𝐞𝐫𝐭𝐢𝐬𝐞 𝐑𝐞𝐦𝐚𝐢𝐧𝐬 𝐂𝐫𝐢𝐭𝐢𝐜𝐚𝐥 Successful migrations preserve the original code (COBOL, PL/1) alongside its modern equivalents, ensuring existing expertise remains valuable rather than becoming obsolete. 4. 𝐀𝐈 𝐀𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐞𝐬 𝐌𝐢𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐒𝐩𝐞𝐞𝐝 Generative AI now automates documentation, testing, and reengineering, accelerating these tasks by orders of magnitude. 5. 𝐏𝐫𝐨𝐯𝐞𝐧 𝐅𝐚𝐬𝐭 𝐑𝐎𝐈 Heirloom's client, Arek, realized an 80% OpEx reduction and performance improvements of 152%. https://lnkd.in/gqqTGBtM #replatformRefactorReimagine #realCustomersRealResults #exitTheDataCenter #agilityMatters #humbleBrag #mainframeMigration #mainframeModernization #COBOL #PL1 #cloudAgnostic #multiCloud #cloudNative ISG (Information Services Group) Kyndryl Heirloom Computing Microsoft Cloud Google Cloud Oracle Cloud

70% of enterprise IT budgets are drained by maintaining outdated legacy systems. The result? Slowed innovation, increased risk, and rising costs. 🚀 But there's a smarter path forward — and it starts with AI-powered modernization. Here’s what SoftServe’s latest white paper reveals about COBOL-to-Python migration: - Speed – AI accelerates code translation, reducing delays. - Flexibility – Python enables rapid development and integration. - Savings – Automation reduces maintenance and vulnerability costs. - Scalability – Future-proof systems ready to grow and adapt. 🔍 Still stuck with COBOL? Then you know the pain of rigid architecture and talent scarcity. One enterprise in the energy sector used AI to shift 82 modules to the cloud — not in months, but weeks. The gains weren’t just technical — they unlocked time, budget, and innovation potential. This isn’t just modernization — it’s reinvention. If you’re leading digital change or guiding tech strategy, this read is your next move.