Modernizing Legacy Systems

Translating code like #COBOL or #RPG is not a modernization solution in itself, because although AI-powered tools like Ozgar.ai , #IBMBob and X-Analysis offer huge promise, it is only a starting point, not the destination. Moreover, moving to the cloud may be detrimental to modernization, as much of what makes #IBMi and #IBMz systems so special is embedded within the platform. Peerless levels of reliability, security and performance have been built and maintained through decades of optimization, performance tuning and tight coupling between software and hardware. This kind of intel is hard to replace.     Modernization of IBM platforms is a multi-dimensional problem that requires resolving disparate challenges such as on-premises dependencies, encryption, transactional integrity, security, system-level engineering, data residency, database architecture, scaling and disaster recovery. Translating code is only part of the story. How #IBM customers solve these issues whilst successfully integrating with everything around their mission-critical, core system is where the real modernization journey begins. 

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.

AI in Telco Won’t Scale if Legacy Stays Telcos continue to announce AI transformation roadmaps. From GenAI in customer service to AI-RAN and self-optimizing networks, the ambitions are clear. Yet across the industry, most of these initiatives remain trapped in pilot mode. The reason is not model maturity or lack of talent. It is legacy infrastructure. A recent survey by Fierce Telecom found that 32% of operators cite legacy systems as the primary barrier to AI adoption. In parallel, Accenture reports that 66% of service providers identify technical debt as the top constraint to modernization. Over half of IT Telco teams spend more than 800 hours annually maintaining aging platforms. That is time diverted from deploying automated pipelines, training models, or integrating intelligent agents into production systems. Legacy showstoppers are happening every day. In 2024, a large Telco group partnered with a top vendor to implement its cognitive SON platform. The objective was to use AI to optimize power consumption, reduce interference, and improve network efficiency by up to 30%. But the project initially failed to scale. The AI system required real-time telemetry, dynamic network configuration access, and external data streams such as energy pricing. Core telemetry data was locked inside proprietary EMS platforms that did not support open interfaces. External data integration was blocked by outdated middleware layers. Configuration workflows still require manual validation due to rigid OSS processes. The model was fully functional, but the infrastructure was not. Only after the Telco replaced key legacy OSS components and re-engineered its data architecture did the AI deployment deliver measurable impact. Across the telecom industry, legacy systems dominate BSS, OSS, provisioning, and assurance layers. These platforms were not designed to support AI inference, real-time feedback loops, or autonomous operations. They were built to enforce transactional integrity, compliance, and control. As a result, they constrain AI deployments in both speed and scope. Enterprise-wide benchmarks reinforce this structural problem. 64% of large organizations still run over a quarter of their operations on legacy systems. In telecom, that percentage is likely higher and far more critical to daily network functionality. AI in telecom cannot scale on infrastructure that was never meant to support it. Until the underlying systems are modernized, even the best-designed models will remain boxed into isolated pilots. The path forward is not just about choosing the right algorithms. It begins with the architectural will to replace what no longer supports execution.

Banks’ biggest tech challenge isn’t upgrading legacy systems- it’s integrating an entirely new (Gen)AI layer with orchestration in the lead. And making it work across functions. Too many banks often start with the wrong focus. Whereas dealing with legacy infrastructure is inevitable, it can become a blind spot without the right understanding of what it needs to achieve. Delivering agile, intelligent services that anticipate customer needs should be the goal. Here is a high-level overview of how the back end can be adjusted: 𝟭. 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗲𝗻𝗴𝗶𝗻𝗲: -   An orchestration layer sits atop core systems, routing everything - from customer questions to fraud alerts - to the right AI service. -   Modern APIs abstract legacy systems into modular services, so AI features can be added or swapped without changing existing workflows. 𝟮. 𝗥𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲: -   Real-time data feeds stream transactions, balance changes and logins as they happen. -   A unified data hub brings together customer details, activity patterns and risk ratings so every AI tool works from the same information. 𝟯. 𝗗𝗮𝘁𝗮-𝗱𝗿𝗶𝘃𝗲𝗻 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀: -   Requests are automatically enriched with live account balances, recent transactions and open support tickets - ensuring the AI’s output reflects up-to-date information. -   Data is fetched on demand from indexed records, so the AI stays current without the expense of retraining the entire model for every update. 𝟰. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 & 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲: -   Data stays encrypted end-to-end, from intake to AI output. -   Automated audits flag bias and log every decision. -   Failure simulations uncover hidden risks before they impact customers. 𝟱. 𝗠𝗼𝗱𝘂𝗹𝗮𝗿 𝘀𝗲𝘁-𝘂𝗽: -   Modern interfaces turn core banking, payment and CRM systems into plug-and-play modules. -   Behind the scenes, back-end services can be updated piece by piece without interrupting the AI layer. 𝟲. 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲𝗱 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝘆 𝘁𝗲𝗮𝗺𝘀: -   Small, cross-functional teams manage everything from data ingestion to model deployment and monitoring. -   Defined roles and fast feedback loops keep projects compliant and focused on real customer needs. The GenAI layer doesn’t just sit on top of the existing setup – it’s a complete overhaul of the tech architecture and the business logic behind it. Opinions: my own, Graphic source: BCG 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐦𝐲 𝐧𝐞𝐰𝐬𝐥𝐞𝐭𝐭𝐞𝐫: https://lnkd.in/dkqhnxdg

Let's take a look at how Popeyes is doing. Essentially, and this has been the story for some time, as RBI chairman Patrick Doyle recently explained, the food quality and innovation is tough to beat. Few brands in the category put as much time into the process. But, Doyle said, the chain could do better at operational execution and some competitors (like Chick-fil-A) have it beat on that front, at least the way things stand now. "Chick-fil-A has set the standard for running at scale—really, really great service restaurants, good-looking restaurants, and we’ve got to be as good as that,” Doyle said. In 2023, Popeyes first introduced its "Easy to Love "strategy, which calls for simplifying restaurant operations for franchisees and making kitchens easier to run. By the end of 2026, Popeyes wants all U.S. locations to have cloud-based POS systems, digital drop charts, sticky label printers, order-ready boards, kiosks, and upgraded back-of-house equipment (auto batter makers and improved hot holding units). At the same time, Popeyes began increasing national advertising spend in April. This step-up in spend will continue for the next three years if performance targets are met. Additionally, RBI is encouraging franchisees to remodel their stores, backed by evidence that refreshed restaurants achieving an A-grade generate 30 percent higher profitability than the system average. The brand hopes to reach a consistent modern image by 2030. More on what's going on and what's ahead here: https://lnkd.in/gWSpYzh5

You can not modernise what you can not see. Most organisations have no idea what is actually running. We were brought in to uplift a monitoring estate for a critical government programme. The assumption was straightforward. Document the existing infrastructure and then rebuild it. The reality was different. Nobody knew exactly what was connected to the network. Years of mergers and upgrades had created an environment where the asset register bore little resemblance to reality. We found servers nobody remembered provisioning. We found network devices configured by contractors who left years ago. This is the discovery challenge that kills modernisation programmes. You can not upgrade systems you do not know exist. Successful discovery requires a few key things → Automated scripts that actively scan and identify every device. → Network traffic analysis to find communicating systems. → Reconciliation between documentation and reality. Our automated scripts found approximately 400 devices per day during the audit phase. Modernisation programmes that skip proper discovery build on assumptions rather than reality. What percentage of your infrastructure would discovery scripts find that is not in your asset register? #DigitalTransformation #ITInfrastructure #ShadowIT

The largest published cUAS enterprise contract just dropped. $20 billion. 10 years. One vehicle. A nine-year-old startup just got the streamlined corporate contract treatment that Lockheed and Raytheon built over decades. The Army handed Anduril an enterprise contract (W9128Z-26-D-A001) that consolidates 120+ separate procurement actions into a single pipeline. Aberdeen Proving Ground issued it. Five-year base plus five-year option through March 2036. This isn't about Anduril's valuation. It's about what the Pentagon is replacing. Northrop Grumman's legacy FAAD C2 system. The backbone of Army counter-drone command and control. Gone. Brig. Gen. Matt Ross, director of Joint Interagency Task Force 401, the rapid-tech-transfer outfit that visited Ukraine operations, called it directly: "This enterprise contract is a critical step in establishing a common framework for counter-UAS interoperability. It provides a foundational command-and-control capability." The timing isn't coincidental. Drone attrition is spiking in current operations. Ukraine proved that whoever controls the C2 layer controls the fight. The Pentagon watched thousands of drones get neutralized not by better hardware, but by better software integration. Lattice is the answer they're buying. One operating system fusing thousands of sensors and effectors. One operator controlling swarms. Runs in degraded comms and contested EW environments. Battle-tested on Barracuda and Bolt-M systems in Ukraine. The contract structure tells you how urgent this is. Pre-negotiated pricing. Range discounts. Annual spend-volume discounts. No more weeks of negotiations per order. The Army explicitly said this "slashes admin costs and procurement timelines dramatically." Three implications for defense contractors. 1. C2 integration is the new battleground. If your cUAS solution can't integrate with open-architecture C2 systems like Lattice, you're building for yesterday's fight. 2. Battle-tested beats paper-tested. Anduril's hardware was battle-tested and underwent rapid improvements. That operational data won this contract. 3. Speed compounds. Enterprise vehicles eliminate procurement friction. Contractors inside the architecture get faster access. Those outside watch from the sidelines. The counter-UAS race just got a unified command structure. Can your solution integrate with it? ---------- Like this content? Join our newsletter. Link below my name 👆

In an era of peak ambiguity, shifting geopolitical tensions, and asymmetrical warfare, our defense posture won’t just rely on the quality of software we can build; it will rely on the speed in which we can adapt, iterate, and deploy new innovation. As my partners Paul and Alexa recently wrote in their piece on the rise of applied software for hardware-intensive industries, we're entering a new era where software is no longer layered on top of the physical world—it’s integral to development from the start. We believe deeply in software-defined hardware as a driver of future resilience. That belief underpins our investments in defense companies like Helsing, Anduril Industries, Applied Intuition, and Saronic Technologies. But software-defined hardware is just one piece of the equation. There’s a broader ecosystem that is needed to scale innovative hardware. A key part of this is software for testing and deployment. One of the most persistent bottlenecks in innovation is proving real-world viability. In many sectors, a few hacker-engineers can scrape together an MVP, ship a small batch of prototypes, and iterate from there. But for the defense companies, the high standard for validation in the real world is costly and complex. As an example, autonomous aircraft can be built in months, but take years to certify. Companies like PhysicsX and Nominal are tackling testing and simulation to reduce waste and shorten development timelines, while enabling superior product design. PhysicsX delivers deep learning–based simulation software, embedding intelligence across the entire product lifecycle, from concepting and design to manufacturing and operations. And in the field, Nominal collects, structures, and activates raw field signals to give operators and engineers real-time visibility into how complex systems are performing. These systems aren’t just for tech-native startups. They can support mechanical, electrical, and aerospace engineers at legacy firms, streamlining processes and unlocking innovation across incumbents and emerging players alike. We’ve embraced “build, test, learn, repeat” in the software world. Now we need to bring this to the hardware world with purpose-built platforms that understand the nuance of development in critical industries. We need tools that make fast, continuous iteration possible. Resilience begins with rethinking how we build, test, and deploy in the physical world. Read more from Paul and Alexa here: https://lnkd.in/gih37kyY

𝗚𝗲𝗿𝗺𝗮𝗻𝘆 𝗜𝘀 𝗡𝗼 𝗟𝗼𝗻𝗴𝗲𝗿 𝗧𝗮𝗹𝗸𝗶𝗻𝗴 𝗔𝗯𝗼𝘂𝘁 𝗢𝗻𝗲 𝗡𝗲𝘄 𝗪𝗲𝗮𝗽𝗼𝗻 Lieutenant General Christian Freuding, Germany’s Inspekteur des Heeres and former head of the Ukraine coordination staff inside the Defence Ministry, has described five priorities for the German Army: long-range strike, air defence against drones, electromagnetic warfare, integration of unmanned systems and AI-enabled command and control. Freuding’s central point is that the battlefield is no longer suffering from too little information, but from too much information arriving too quickly from sensors in the air, on the ground and in space. The military problem is therefore shifting from collecting data to processing it, fusing it and turning it into decisions before the enemy can do the same. For #Germany and #NATO, this is a significant conceptual turn. The Bundeswehr does not only need more tanks, artillery, air defence and ammunition, although it clearly needs all of those. It also needs the digital backbone that connects sensors to commanders, commanders to effects and effects to the tempo of modern combat. ⚙️ The five priorities make sense only as a system. Deep strike without sensors is blind. Counter-drone air defence without electromagnetic warfare is incomplete. Unmanned systems without command integration become isolated gadgets. AI-enabled C2 without disciplined data architecture becomes another software promise that fails under battlefield pressure. Freuding’s comments on the tank are equally important. He does not describe the tank as obsolete, but as a command node and mothership for ground robots and unmanned systems while still retaining direct-fire capability for close combat. That is a much better framing than the tired “tank is dead” debate, because the real question is not whether the tank survives unchanged, but whether it can become part of a distributed combat architecture. For #ModernWarfare, this is the Ukraine lesson in one sentence: the future battlefield will include AI-supported drones, robots, long-range fires and electromagnetic contestation, but it will still include soldiers, rifles, close combat and human responsibility for the use of force. 🧠 The deeper lesson is that modernisation is no longer about adding drones to an old army. It is about rebuilding the army around data, depth, protection, autonomy, electromagnetic resilience and human command inside a faster kill chain. That is the hard part for every European army. Not buying one impressive system, but making the whole force think, move, sense and strike faster without removing the human decision from the centre of military responsibility. 𝘛𝘩𝘦 𝘧𝘶𝘵𝘶𝘳𝘦 𝘭𝘢𝘯𝘥 𝘧𝘰𝘳𝘤𝘦 𝘪𝘴 𝘯𝘰𝘵 𝘮𝘢𝘯𝘯𝘦𝘥 𝘰𝘳 𝘶𝘯𝘮𝘢𝘯𝘯𝘦𝘥, 𝘣𝘶𝘵 𝘢 𝘩𝘶𝘮𝘢𝘯-𝘭𝘦𝘥 𝘴𝘺𝘴𝘵𝘦𝘮 𝘵𝘩𝘢𝘵 𝘮𝘶𝘴𝘵 𝘰𝘶𝘵𝘱𝘳𝘰𝘤𝘦𝘴𝘴, 𝘰𝘶𝘵𝘢𝘥𝘢𝘱𝘵 𝘢𝘯𝘥 𝘰𝘶𝘵𝘴𝘵𝘳𝘪𝘬𝘦 𝘵𝘩𝘦 𝘦𝘯𝘦𝘮𝘺.

Revitalizing a Legacy Product: A Strategic Approach Taking on an older product with outdated code and limited documentation can be daunting. Where do you start when institutional knowledge is sparse? My approach is to go back to the source - connect directly with long-time employees and current users. Interview employees to reconstruct original motivations and use cases. Conduct usability testing and discovery interviews with customers to see how they leverage the product today. This field research uncovers valuable insights: ►Key workflows the product enables ►Functionality that customers actually use ►Opportunities to improve and solve new problems Don't just replicate the past. Leverage customer context to reimagine what's possible. New technologies may now enable you to build a far superior product. Watch users directly to identify core functionality versus unnecessary bloat. Collaborating closely with engineers is crucial too. Review the codebase for optimization opportunities. Determine if replatforming is needed to support modern architecture and experiences. In summary, let customer needs guide your vision. The problems users face today are likely different than when it was first built. Maintain a beginner's mindset. Then, architect creative solutions to exceed customer expectations. By coupling curiosity with technology, legacy products can transform into an organization's most impactful assets. There are hidden opportunities in the old - you just have to dig them out. What approaches have you found effective for modernizing legacy products? I'd love to hear your experiences! Listen now 🎧 Youtube: https://lnkd.in/eD3ABYH3 Website: https://lnkd.in/eU6RfMFe Do you have a burning product-related question for me? Feel free to submit one here DearMelissa.com and you might find your question featured and answered in an upcoming episode. #ProductThinking #DearMelissa #LegacyProducts #ProductModernization #CustomerCentric #TechInnovation