Importance of Upgrading Legacy Systems

𝗙𝗿𝗼𝗺 𝗡𝘂𝗰𝗹𝗲𝗮𝗿 𝗟𝗮𝘂𝗻𝗰𝗵 𝗖𝗼𝗱𝗲𝘀 𝘁𝗼 𝗧𝗮𝘅 𝗥𝗲𝘁𝘂𝗿𝗻𝘀: 𝗔𝗺𝗲𝗿𝗶𝗰𝗮'𝘀 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 𝗔𝗿𝗲 𝗦𝘁𝗶𝗹𝗹 𝗥𝘂𝗻𝗻𝗶𝗻𝗴 𝗼𝗻 𝗧𝗲𝗰𝗵 𝗙𝗿𝗼𝗺 𝗬𝗼𝘂𝗿 𝗖𝗵𝗶𝗹𝗱𝗵𝗼𝗼𝗱 When you filed your taxes this year, your return was processed on technology older than most college graduates. This isn't hyperbole, it's reality. The Pentagon was using 8-inch floppy disks (yes, the actually floppy ones) for nuclear systems until 2019. Critical government infrastructure still runs on COBOL - a programming language that debuted when Eisenhower was president. Why this matters: • Each legacy system is a ticking security vulnerability • Maintenance costs are skyrocketing as experts retire • Citizens experience delays that would be unacceptable in the private sector • Innovation becomes nearly impossible on decades-old platforms Enter DOGE (Department of Government Efficiency) - which isn't just about cost-cutting. It's about dragging government technology out of the digital Stone Age. The transformation is already beginning: • The IRS is getting a long-overdue technological overhaul • OPM is using AI to help translate ancient COBOL code • Agencies are finally migrating to cloud-based infrastructure But modernization faces real challenges: • Embedded legacy systems that "can't be touched" • Institutional resistance to change • The complexity of upgrading while maintaining operations • Navigating ethical considerations around AI integration The stakes couldn't be higher. When government technology fails, it's not just an inconvenience - it affects national security, economic stability, and basic citizen services. What outdated government systems have you encountered that desperately need modernization? What would you prioritize? #GovTech #DigitalTransformation #TechPolicy #DOGE

Every month you delay that system upgrade costs you money. Based on recent industry trends, we're seeing firms lose up to $8,500 monthly in operational efficiency alone. What you can't measure? The recruiter who quits because they're tired of fighting outdated tech. The client who switches to your competitor with the slick new portal. The candidate who ghosts you after your clunky onboarding experience. I've watched staffing firms analyze the same tech decision for 18 months. Meanwhile, their competition implemented, iterated, and captured their market share. The reality is, waiting for the "perfect time" to upgrade is like waiting for all traffic lights to turn green before starting your commute.  It's never going to happen. Here's the pattern I keep seeing: firms that postpone major tech decisions for over 6 months are 3x more likely to lose ground to competitors. Not because they made the wrong choice, but because they made no choice at all. Your legacy system isn't getting better with age. Neither is your competitive position. The best time to plant a tree was 20 years ago. The second best time? Today. If you're calculating the real cost of waiting versus moving forward, happy to share what successful firms are doing to break the analysis paralysis cycle. 

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.

The biggest threat to innovation? It’s not lack of talent. It’s not lack of funding. It’s technical debt. The reality: Every time an employee waits for a slow system to load, that’s lost productivity. Every time a business relies on outdated tools, that’s missed revenue. Every time IT has to patch instead of innovate, that’s stalled transformation. And the worst part? The longer you ignore it, the more expensive it becomes. How it happens: Enterprise leaders unknowingly accumulate technical debt when they: Delay critical system upgrades to “save costs” Patch legacy systems instead of modernizing them Ignore architectural debt while chasing short-term wins The result? A fragile, inefficient IT landscape that increases risk and makes transformation exponentially harder. The fix: ✅ Treat technical debt like financial debt → Proactively measure, manage, and reduce it. ✅ Invest in enterprise architecture → A strategic roadmap reduces redundant systems and optimizes total cost of ownership (TCO). ✅ Align IT and business strategy → Every IT dollar should drive measurable business outcomes. Real-world impact: At one of the global manufacturing companies I worked with, we faced overwhelming technical debt—multiple ERP systems, siloed applications, and legacy infrastructure slowing down operations. By implementing an enterprise-wide modernization strategy, we: ✔ Cut IT costs by 34% ✔ Eliminated redundant applications ✔ Freed up resources for true innovation Because technical debt isn’t just an IT challenge—it’s a business priority. The question isn’t whether you have technical debt—it’s whether you’re actively managing it. The sooner you address it, the less it will cost you. P.S. What’s the biggest challenge in addressing technical debt—cost, leadership buy-in, or execution? Drop your thoughts in the comments. And if you need help tackling it, let’s connect.

SCARY FAA to Replace Air Traffic Control Systems Still Running on Windows 95 and Floppy Disks Introduction: America’s Skies Still Managed by 1990s Tech The Federal Aviation Administration (FAA) has confirmed that critical components of the U.S. air traffic control system still operate using antiquated technology—including Windows 95 computers and floppy disks. In testimony before Congress, acting FAA Administrator Chris Rocheleau announced that the agency is finally seeking contractors to replace these outdated systems, marking a long-overdue modernization effort set to be completed within four years. Key Details from the FAA Modernization Plan 1. Obsolete Technology Still in Use • Windows 95, launched nearly 30 years ago, remains in active use at many air traffic facilities. • Floppy disks are still used to transfer crucial data between systems. • Paper strips continue to be employed to track aircraft movements, a manual process with built-in risk for delays and human error. 2. Federal Push to Modernize • The FAA issued a Request for Information (RFI) to solicit modernization proposals from private sector contractors. • Acting Administrator Rocheleau emphasized, “No more floppy disks or paper strips,” signaling a clean break from legacy systems. • Transportation Secretary Sean Duffy described the upgrade as “the most important infrastructure project” in decades, highlighting its bipartisan support. 3. Why These Systems Persisted • Stability and familiarity: Older systems are deeply embedded and have a long record of reliability when functioning properly. • Cost and complexity: Upgrading mission-critical systems in a 24/7 environment is expensive, risky, and logistically difficult. • Security concerns: Ironically, older systems are often isolated from the internet, limiting their vulnerability to modern cyberattacks. Why It Matters: Safety, Efficiency, and National Preparedness • Safety and Delay Reduction: Updating these systems will improve flight tracking accuracy, reduce delays, and lower the chance of operational errors. • Cybersecurity Risks: Antiquated hardware increases the risk of failure and limits defenses against evolving threats. • Global Competitiveness: Modern air traffic infrastructure is essential for keeping pace with international aviation standards and demand growth. • Contracting Opportunity: The overhaul opens the door for innovative tech firms and defense contractors to modernize one of the most vital national systems. Conclusion The FAA’s plan to replace its aging air traffic control systems signals a long-overdue leap into the 21st century. While the legacy systems have served reliably, modernization is essential for safety, efficiency, and national competitiveness in the age of digital aviation. Keith King https://lnkd.in/gHPvUttw

🐭 What Do Chuck E. Cheese, the German Navy, and San Francisco Public Transportation Have in Common? Let's talk about a blast from the past: floppy disks. For most of us, these relics of the 80s and 90s are long gone, but in the world of industrial control systems (ICS) and national infrastructure, they're still spinning. Literally. Take a moment to digest this: The German Navy, US Air Force, and until just last month, the Japanese government, have been happily relying on these ancient storage mediums. And guess what? The San Francisco Municipal Transportation Agency will keep using 5¼-inch floppies for the Muni Metro light rail system until 2030. Even Chuck E. Cheese was using them for their animatronics until 2023. Talk about retro! Now, before you laugh, let’s break down why this matters for national security and cybersecurity: Legacy Systems = Legacy Risks: Why It Matters: Old systems might seem charmingly nostalgic, but they're not exactly bulletproof. They often lack the security features we take for granted today. Takeaway: Regularly assess and update your ICS and infrastructure to mitigate vulnerabilities inherent in outdated technology. Security Through Obscurity? Not Quite: Why It Matters: Just because a system is old doesn't mean it's safe from modern threats. Hackers love finding old systems to exploit precisely because they're often overlooked. Takeaway: Conduct comprehensive security audits on all systems, old and new. Assume that if you can still use it, a hacker can exploit it. The Human Element: Why It Matters: Many industries stick with old tech because it's "tried and true." The people running these systems are experts in their field but might not be up to speed on modern cybersecurity practices. Takeaway: Invest in ongoing cybersecurity training for all staff, emphasizing the importance of updating legacy systems and the risks they pose. Operational Continuity vs. Innovation: Why It Matters: Upgrading legacy systems can be costly and disruptive. But so is a cyber attack. Takeaway: Balance the need for operational continuity with the imperative of cybersecurity. Create a phased plan to modernize systems without halting operations. Emulating Legacy Systems: Why It Matters: As seen with the German Navy working on emulating replacements for their 8-inch floppies, finding ways to emulate or virtualize legacy systems can offer a bridge to modern solutions. Takeaway: Explore emulation as a stop-gap measure to maintain functionality while planning for long-term upgrades. 🐭 Final Thought: In the realm of national security and critical infrastructure, the stakes are too high to rely on the outdated comfort of legacy technology. It's time to move from nostalgia to vigilance. Embrace the future, secure your systems, and let's leave the floppies to the history books where they belong. #cybersecurity #industrialcontrolsystems #nationalsecurity #legacysystems #techupgrade #cyberrisk #infrastructuresecurity #floppydisk

The Quantum Leap From Legacy Systems to Cloud-Native During a chilly autumn morning when I met with the leadership team of one of our longstanding clients. They spoke with a mix of pride and frustration about their legacy systems—once the engine of their success, now a heavy anchor in a fast-paced digital world. Their story resonated deeply with me. It wasn’t just about technology failing to keep pace; it was about the human struggle to adapt in an ever-changing landscape. Recent research reinforces this urgency. Gartner’s 2024 forecast predicts that by 2025, over 80% of enterprise workloads will operate on cloud-native platforms. This shift promises not only faster deployment cycles and enhanced scalability but also a reduction in operational costs by as much as 30%. For companies burdened by outdated systems, these figures offer hope and a clear path forward. At Devsinc, we embarked on our own journey towards cloud-native solutions driven by our commitment to our clients’ futures. I recall countless late nights spent brainstorming and re-engineering our processes. We embraced agile methodologies, established continuous integration and delivery pipelines, and reimagined our infrastructure to unlock new levels of efficiency. One memorable project involved a major retail client whose legacy systems were stifling innovation. After migrating to a cloud-native platform, their time-to-market was reduced by 40%, and defect rates dropped by 35% within just six months. Their renewed agility not only improved customer satisfaction but also reinvigorated their competitive edge. Yet, this transformation isn’t solely about technology; it’s about people. Shifting from legacy systems to cloud-native architectures means rethinking workflows, empowering teams, and fostering a culture of continuous improvement. I’ve seen firsthand how this change can inspire creativity and resilience, transforming operational challenges into opportunities for growth. Embracing cloud-native is more than a technical upgrade—it’s a lifeline in today’s digital era. It offers the promise of agility, responsiveness, and long-term success. At Devsinc, we are dedicated to guiding our clients through this evolution with empathy, expertise, and unwavering support. The journey from legacy to cloud-native is challenging, but every step forward builds a future where technology truly serves us all. Let’s embrace this change together and create a landscape where innovation and human ingenuity drive us to new heights.

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.

For 50 years, engineers built systems to efficiently collect data. Now we need them to efficiently manage trust processing data at AI scale. That’s something most legacy systems can't do. Legacy systems were designed to gather, process, and retain data efficiently, not to enforce dynamic privacy preferences or support responsible data usage in AI at enterprise scale. This is much more than a minor inconvenience. It's a fundamental challenge for all organizations, made even more existential by the AI innovation wave. This is also the challenge we’ve built Fides to help enterprises overcome. Consider what happens when modern AI governance requirements crash into legacy architecture: • Hard-coded data models that can't adapt to evolving AI ethics policies • Tight coupling between systems that makes selective data usage impossible • Embedded business logic that assumes permanent, unrestricted data access • Missing audit trails for AI model data lineage and usage • Limited APIs that don't support real-time consent enforcement • Undocumented data flows that create AI governance blind spots • Complex dependencies that make changes risky Building trusted data infrastructure from scratch, designing AI governance controls from the ground up, is relatively straightforward. But which enterprise has the luxury of rebuilding everything from scratch before deploying AI? Instead, they must retrofit trust into systems designed for a world where data was collected without question and used without constraint. This isn't about replacing everything. It's about building the trusted data layer that bridges legacy systems and infrastructure that’s suitable for AI innovation and ready for hyper-scale data governance. Because the real challenge isn't the technology, it's enabling data-driven innovation while evolving systems to meet trust requirements that scale with your data ambitions. Is your organization's data infrastructure built for AI-scale trust, or still optimized for unlimited collection? What's blocking your teams from using sensitive data to create value, ethically and at speed with personal data? I'd love to hear your experiences, either in the comments below or by DM.

If you're prioritizing AI use cases, look for where your teams spend significant effort in order to understand massive amounts of data. With over 800 billion lines of legacy code running in production systems globally, using AI to understand that code is a perfect use case. Legacy code modernization has always been a major headache for businesses, and has been one of the toughest nuts to crack because... ☑️ Documentation is sparse or nonexistent (developers from the 1980s didn't exactly leave comments) ☑️ Institutional knowledge has walked out the door with many who know the language best retiring ☑️ The business logic is buried in millions of lines of interconnected, undocumented code The scale of this challenge is not small. 70% of Fortune 500 software was developed 20+ years ago, not to mention... ☑️ There's roughly 800+ billion lines of COBOL running in production systems globally ☑️ There are only ~24K active COBOL developers in the U.S. to support it ☑️ 95% of financial transactions still flow through COBOL code ☑️ 43% of global banking critical systems depend legacy code If your firm depends on legacy code for critical processes, modernizing is likely a strategic priority. And that effort isn't measured in months to completion, but decades. You've probably tried rule-based tools to automatically convert COBOL to Java, only to end up with "Jobol" that you understand no better than the original code. The code converts, but the comprehension doesn't. Imagine if you could train an AI model to understand COBOL. Could it tell you how these systems work in plain English? Where you can then decide for yourself the path forward? Morgan Stanley just did just this. Faced with this exact challenge, the team at Morgan Stanley built their own AI solution. In January, they rolled out DevGen.AI, an in-house tool built on OpenAI's GPT models that translates legacy code into plain English. The results speak for themselves: ✔️ 9M lines of code processed in just 5 months ✔️ 280,000 developer hours saved ✔️ 15,000 developers now have access to legacy system insights What did they learn? Don't try to automatically replace legacy code. Instead, use AI to translate what the code does into readable specifications that anyone can understand. If you aren't looking at AI for accelerating your system modernization, now is the time. Here's where I'd start... 1️⃣ Validate on a small, known system. Try evaluating specialized tools for this purpose. There are several startups out there for this purpose 2️⃣ Categorize your systems: what needs full modernization vs. AI support-only maintenance 3️⃣ Run strategic pilots on core modules (not a big bang approach) and deploy AI translation for teams supporting legacy systems (at least for now) The question isn't whether AI can handle this challenge, it's whether your organization will be among the first to unlock this competitive advantage. Morgan Stanley story: in comments #ai #legacymodernization