Strategies to Improve Operations Through Modernization

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

Exactly a year ago, we embarked on a transformative journey in application modernization, specifically harnessing generative AI to overhaul one of our client’s legacy systems. This initiative was challenging yet crucial for staying competitive: - Migrating outdated codebases - Mitigating high manual coding costs - Integrating legacy systems with cutting-edge platforms - Aligning technological upgrades with strategic business objectives Reflecting on this journey, here are the key lessons and outcomes we achieved through Gen AI in application modernization: [1] Assess Application Portfolio. We started by analyzing which applications were both outdated and critical, identifying those with the highest ROI for modernization.  This targeted approach helped prioritize efforts effectively. [2] Prioritize Practical Use Cases for Generative AI. For instance, automating code conversion from COBOL to Java reduced the overall manual coding time by 60%, significantly decreasing costs and increasing efficiency. [3] Pilot Gen AI Projects. We piloted a well-defined module, leading to a 30% reduction in time-to-market for new features, translating into faster responses to market demands and improved customer satisfaction. [4] Communicate Success and Scale Gradually. Post-pilot, we tracked key metrics such as code review time, deployment bugs, and overall time saved, demonstrating substantial business impacts to stakeholders and securing buy-in for wider implementation. [5] Embrace Change Management. We treated AI integration as a critical change in the operational model, aligning processes and stakeholder expectations with new technological capabilities. [6] Utilize Automation to Drive Innovation. Leveraging AI for routine coding tasks not only freed up developer time for strategic projects but also improved code quality by over 40%, reducing bugs and vulnerabilities significantly. [7] Opt for Managed Services When Appropriate. Managed services for routine maintenance allowed us to reallocate resources towards innovative projects, further driving our strategic objectives. Bonus Point: Establish a Center of Excellence (CoE). We have established CoE within our organization. It spearheaded AI implementations and established governance models, setting a benchmark for best practices that accelerated our learning curve and minimized pitfalls. You could modernize your legacy app by following similar steps! #modernization #appmodernization #legacysystem #genai #simform — PS. Visit my profile, Hiren Dhaduk, & subscribe to my weekly newsletter: - Get product engineering insights. - Catch up on the latest software trends. - Discover successful development strategies.

Legacy systems often stick around longer than anyone plans. At first, they do the job, but over time, they start holding your business back. Many of our clients come to us facing this exact issue — old systems that can't keep up with their growing needs. The big question: how do you modernize without risking major disruptions? The first step is understanding what your legacy system still does well and where it’s holding you back. Not everything needs replacing right away. Focusing on the areas that are creating the most friction in your day-to-day operations will help you target your efforts. We often advise clients against ripping out an entire system all at once. Instead, we help them modernize in manageable steps. This approach spreads the investment over time and allows you to gradually replace outdated components while keeping your core business running smoothly. Moving data from a legacy system to a new platform can be one of the most complex parts of the process. We’ve helped companies navigate this challenge by developing clear migration plans that focus on data accuracy and integrity. Your data is the lifeblood of your operations, and ensuring it transfers correctly — without loss or corruption — is key to a successful modernization. One mistake we’ve seen businesses make is forgetting to prioritize security when modernizing legacy systems. Older systems tend to have vulnerabilities that modern threats can exploit, but simply moving to a new platform isn’t enough. Every upgrade needs to be paired with an evaluation of your security posture. Implementing new encryption methods, improving access controls and conducting regular security audits to protect your data and operations should be a priority in your modernization plan. Legacy system modernization is a journey, but when done thoughtfully, it can unlock new opportunities for growth, efficiency, and innovation. #flexmade #softwaredevelopment #legacysystems #digitaltransformation

How to tackle legacy system modernization at scale: How Booking(.)com tackled a legacy API that had gotten completely out of hand: The situation: A 14-year-old API in their Perl monolith had grown from handling simple app updates to managing 21 different features across 7 teams. Instead of a quick migration to Java, the team took a thoughtful approach to breaking down this complex system. Key insights from their successful modernization: 1. Map before you migrate. The team created visual diagrams to understand how 1,500 lines of code connected to various parts of their system. 2. Know your stakeholders. Using repository history, they identified every team dependent on the API and included them in the planning process. 3. Split strategically. They separated the system into focused services based on functionality and platform requirements, making it more maintainable. 4. Test thoroughly. When they encountered unexpected issues with marketing metrics, they used A/B testing to identify and fix problems without disrupting service. The biggest lesson? Modernizing legacy systems isn't just rushing to new technology. It's about understanding what you have and carefully restructuring it into something better. Follow Pratik Daga for daily informative posts on software engineering.

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