Tips for IT Modernization Success

The next few years are going to be tough. Many legacy applications finally need to be modernized.  10 actions to survive. 1. Focus: Not every functionality needs to be migrated. Strict scope management based on real customer needs is crucial. What's your approach to scope prioritization? 2. Outcome-driven: Delivered functionality isn't the main success criterion - improved business value is. In my last project, we delivered 18% more revenue with just 60% of the migrated functionality. What metrics matter most in your modernization efforts? 3. Data-driven: Validate the value of each delivered feature through A/B testing. Combine quantitative data with user stories to paint the complete picture. 4. Incremental and iterative: From month one, deploy continuously to production through a robust delivery pipeline. Daily releases should be your minimum target. Agile and DevOps work. 5. Fail fast: Build and validate technically risky and commercially important functionalities first. Minimize basic functionality. Effectiveness before efficiency. 6. Experience-based: Don't reinvent the wheel. Learn from others who've succeeded. Shamelessly adopt state-of-the-art practices that work. 7. Human-centric: Your employees are critical to success. They understand customer needs, business processes, and legacy systems. Blend their experience with external expertise and invest in change management. 8. Be adaptable: We plan, God laughs. Observe, reflect, and adapt regularly at every organizational level. Stay self-critical and embrace change. 9. Cost-aware: Modernization isn't just about technology - it's about business value. Track and communicate both investment and returns. Create transparency about technical debt reduction and new revenue opportunities. 10. Future-proof: Design for change, not just today's requirements. Choose modern, maintainable architectures and build technical excellence into your culture. Microservices aren't dead. Which of these measures resonates most with your experience? What would you add to this list? Share your thoughts in the comments!

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 This initiative was challenging yet crucial for staying competitive: - Migrating outdated codebases - Mitigating high manual coding costs - Integrating legacy systems with cutting-edge platforms - Aligning technological upgrades with strategic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.

𝗠𝗼𝗱𝗲𝗿𝗻𝗶𝘇𝗶𝗻𝗴 𝗟𝗲𝗴𝗮𝗰𝘆 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝘄𝗶𝘁𝗵 𝗔𝗪𝗦: 𝗟𝗲𝘀𝘀𝗼𝗻𝘀 𝗟𝗲𝗮𝗿𝗻𝗲𝗱 Legacy applications can hold your business back: high maintenance costs, scalability challenges, and lack of agility. Modernizing with AWS offers a chance to unlock innovation, but it’s not without challenges. Here are some hard-earned lessons I’ve learned along the way: 1️⃣ 𝗕𝗿𝗲𝗮𝗸 𝗗𝗼𝘄𝗻 𝘁𝗵𝗲 𝗠𝗼𝗻𝗼𝗹𝗶𝘁𝗵 𝗦𝘁𝗲𝗽-𝗯𝘆-𝗦𝘁𝗲𝗽 Trying to refactor everything at once? That’s a recipe for disaster.  Instead, adopt an incremental approach: • Start by identifying business-critical components. • Migrate to microservices in stages using containers (ECS, EKS). • Introduce APIs gradually to reduce tight coupling. 2️⃣ 𝗖𝗵𝗼𝗼𝘀𝗲 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗔𝗪𝗦 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝘀 AWS offers countless services, but not all are the right fit. Select based on your workload needs: • 𝗖𝗼𝗺𝗽𝘂𝘁𝗲: Lambda for event-driven tasks, ECS/EKS for containerized workloads. • 𝗦𝘁𝗼𝗿𝗮𝗴𝗲: S3 for static content, RDS or Aurora for relational workloads. • 𝗠𝗲𝘀𝘀𝗮𝗴𝗶𝗻𝗴: SQS and EventBridge for decoupling components. 3️⃣ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝗘𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 Manual deployments and configurations increase complexity and risk. Use: • ���𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗮𝘀 𝗖𝗼𝗱𝗲 (𝗜𝗮𝗖): Terraform or AWS CloudFormation to define environments. • 𝗖𝗜/𝗖𝗗 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀: Automate testing and deployment with AWS CodePipeline. • 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴: CloudWatch and X-Ray to gain visibility and ensure performance. 4️⃣ 𝗕𝗮𝗹𝗮𝗻𝗰𝗲 𝗖𝗼𝘀𝘁 𝗮𝗻𝗱 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 Modernization doesn’t mean throwing money at the cloud. Optimize costs by: • Right-sizing EC2 instances or shifting to serverless where possible. • Using Savings Plans and auto-scaling to keep costs under control. • Leveraging AWS Cost Explorer to identify waste and optimize spending. 5️⃣ 𝗜𝗻𝘃𝗼𝗹𝘃𝗲 𝗦𝘁𝗮𝗸𝗲𝗵𝗼𝗹𝗱𝗲𝗿𝘀 𝗘𝗮𝗿𝗹𝘆 Modernization is not just a tech initiative; it’s a business transformation. Engage teams early to align goals and expectations across development, operations, and leadership. 6️⃣ 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝗤𝘂𝗶𝗰𝗸 𝗪𝗶𝗻𝘀 A successful modernization effort starts small, proves value, and expands. Identify low-risk, high-impact areas to deliver quick wins and build momentum. 💡 𝗣𝗿𝗼 𝗧𝗶𝗽: Modernization is an ongoing journey, not a one-time project. Continuously monitor, optimize, and adapt to stay ahead. What modernization challenges have you faced? #AWS #awscommunity

𝗧𝗵𝗲 𝗦𝗶𝗹𝗲𝗻𝘁 𝗖𝗿𝗶𝘀𝗶𝘀 𝗶𝗻 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗧𝗲𝗰𝗵 𝗪𝗵𝗮𝘁 𝗻𝗼 𝗖𝗜𝗢 𝗶𝘀 𝘁𝗮𝗹𝗸𝗶𝗻𝗴 𝗮𝗯𝗼𝘂𝘁, 𝗯𝘂𝘁 𝗲𝘃𝗲𝗿𝘆𝗼𝗻𝗲 𝗶𝘀 𝗳𝗮𝗰𝗶𝗻𝗴: 3 brutal truths about our aging tech landscape: 1. 75% of our innovation budget is swallowed by maintenance 2. Our "modern" tech stack was modern... 3 years ago 3. We're building digital skyscrapers on quicksand The real cost isn't the tech. It's the trillion-dollar opportunities slipping through our fingers. But here's the gameplan that's working: 1. Map Your Legacy Maze • Full application inventory • Identify dependencies • Calculate true maintenance costs 2. Build Your "Future Team" • Dedicated modernization squad • Clear retirement targets • Weekly wins tracking 3. Embrace the New Rules • No code/low code is the new normal • Mobile-first isn't optional • SaaS-first architecture • Smart RPA integration 4. The Secret Sauce • Zero-trust security by design • Legacy team buy-in (crucial!) • Continuous upskilling program The companies winning right now? They're not just updating tech. They're reimagining their entire approach. Question for fellow CIOs: What's your biggest modernization win so far? #TechLeadership #CIO #DigitalTransformation #FutureOfTech #ModernEnterprise Agree? Share this with a CIO/Tech Leader who needs to see it. Studies show 84% of digital transformations fail. Let's change that.

Over the weekend, I read Google's paper on how they use AI for internal code migrations—and it’s packed with insights on how to approach legacy system modernization. I’ve attached the paper for those interested, but here’s how I believe some of these strategies can help us tackle complex modernization challenges: 🔎 1. Accelerating Legacy System Modernization Google leverages Large Language Models (LLMs) to automate large-scale code migrations, significantly reducing manual effort and speeding up projects. Applying similar AI-driven approaches can streamline the modernization of legacy systems, cutting through complexity and outdated code. 🔎 2. Combining AI with Proven Engineering Tools By blending LLMs with Abstract Syntax Tree (AST)-based tools, the ensure accuracy and scalability in their code transformations. This hybrid method shows how AI and traditional engineering techniques can work together to deliver safe and reliable modernization. 🔎 3. Reusable Migration Workflows Google created modular, reusable workflows that make onboarding and executing new migration tasks faster and more efficient. Developing similar toolkits for legacy systems could simplify recurring modernization steps and adapt to complex scenarios. 🔎 4. Measuring Success by Business Impact Google focuses on measurable outcomes, like a 50% reduction in project time, rather than just the volume of AI-generated code. This business-aligned metric highlights the importance of demonstrating clear ROI in technology transformation projects. 🔎 5. Safe and Scalable Rollouts Their phased deployment strategy ensures AI-driven changes are rolled out safely, minimizing disruption. Adopting a controlled rollout approach can help manage risks and ensure stability when modernizing critical systems. 🔎 6. Strategic Use of AI Models Google balances using custom fine-tuned models and general-purpose tools depending on the task. This approach offers valuable insight into when to invest in specialized AI solutions versus using adaptable off-the-shelf models. 📌 The Big Picture: Legacy system modernization is about combining AI-driven efficiency with engineering best practices to deliver faster, safer, and more impactful business transformations. 📎 I’ve attached the paper if you’d like to explore it further! #LegacyModernization #GenAI #BusinessInnovation — Enjoyed this post? Like 👍, comment 💭, or repost ♻️ to share with others.

As a former AWS Technical Delivery Manager, I taught hundreds of customers how to migrate their workloads to AWS. Last week, I spent a few days working with individuals on a migration project, and I'm sharing a few tips below. First, 𝐀𝐖𝐒 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐃𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲 𝐒𝐞𝐫𝐯𝐢𝐜𝐞 (𝐀𝐃𝐒) removes the guesswork with EC2 recommendations to run your workloads to plan migrations with AWS Migration Hub by:  • Gathering Server and DB inventory for Database Migration Service.  • Server utilization data to generate rightsized EC2 instances.  • Map network communication patterns to understand application dependencies and group servers together.  • Export processes are running on the servers with agents installed. Second, 𝐀𝐖𝐒 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐌𝐢𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐒𝐞𝐫𝐯𝐢𝐜𝐞 (𝐃𝐌𝐒) makes it easy to securely assess, convert, and automate the migration of your databases and analytics workloads with network controls and real-time visibility. DMS minimizes operational disruptions to your applications by keeping source systems fully operational until the migration is complete. Third, 𝐀𝐖𝐒 𝐌𝐢𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐇𝐮𝐛 is a centralized platform that enables you to monitor your migration from planning to end-to-end execution, providing automated recommendations to accelerate your transformation. What I really like is these services are included in the Free and Paid plan tiers, allowing SMBs with AWS credits to evaluate their workloads for migration and modernization. 𝑾𝒆 𝒔𝒑𝒆𝒏𝒕 𝒍𝒆𝒔𝒔 𝒕𝒉𝒂𝒏 $10  to gather server information, EC2 recommendations, and test cutover. For 𝐀𝐈 𝐰𝐨𝐫𝐤𝐥𝐨𝐚𝐝𝐬 𝐚𝐧𝐝 𝐭𝐡𝐞 𝐆𝐏𝐔-𝐚𝐬-𝐚-𝐬𝐞𝐫𝐯𝐢𝐜𝐞 𝐦𝐚𝐫𝐤𝐞𝐭, analysts project that small and medium-sized businesses will allocate more than half of their technology budgets to cloud services. With the cloud migration market expected to grow from $232B to $806B by 2029 (+28%), SMBs are leading the charge, especially those investing in AI, AIOps, and DevOps to modernize faster. Starting in November, 𝐀𝐖𝐒 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦 takes things a step further as the first agentic AI service developed to accelerate enterprise modernization by deploying specialized AI agents to automate complex tasks, such as assessments, code analysis, refactoring, decomposition, dependency mapping, validation, and transformation planning, thereby dramatically reducing project timelines. The service helps reduce both modernization costs and ongoing maintenance expenses while identifying opportunities to eliminate legacy licensing costs for large enterprises. AWS Transform is the next leap bringing agentic AI into migration and modernization. If you’ve tested any of these new AI-driven migration tools, I’d love to hear your experience.

The numbers never lie. . Risk & Failure Rates Modernization (full replacement): High-profile failures: Commonwealth Bank of Australia → spent $1.3B AUD and faced major outages. TSB Bank (UK) → fined £48M after modernization failure locked out 1.9M customers. ING Netherlands → attempted replacement projects multiple times, often delayed or scrapped. Industry research (Standish Group, Gartner): over 70% of large IT modernization projects either fail, run over budget, or don’t deliver expected outcomes. Enhancement (keeping COBOL and modernizing around it): Lower risk since core logic remains untouched. Enhancements (wrapping with APIs, cloud migration, containerization, DevOps integration) are increasingly common. IBM, Micro Focus, CloudFrame, Astadia report higher success rates when companies choose incremental modernization. 2. Cost Replacement: Average cost for full rewrite of a large bank or insurance COBOL system: $500M – $2B+. Maintenance overhead during migration: dual systems must run in parallel. Extra regulatory and testing costs. Enhancement: Costs are typically 10–20% of a full replacement. Example: moving COBOL apps to Linux or cloud environments reduces mainframe costs by 50–70%. API enablement allows integration with modern apps without rewriting. 3. Talent Availability COBOL developers today: estimates range 150K–200K active worldwide (not 2M). Declining pool, but still enough for enhancement strategies. Training a new developer in COBOL for maintenance is often faster than rewriting millions of lines into a new language. 4. Time-to-Value Replacement: typically 5–10 years for a full system migration. Many projects exceed timeline. Enhancement: 6–18 months for major upgrades like API enablement, cloud hosting, DevOps, or system optimization. Conclusion (based on data, not opinion): Enhancement is significantly easier, cheaper, faster, and less risky than replacement. Replacement has value only when: The COBOL system is extremely small. Or the business process itself is being redesigned (not just the code). 👉 That’s why 70–80% of banks and governments worldwide still run COBOL, and most modernization budgets are going into incremental enhancement, not replacement. INTEROPERABILITY that's the name of the song

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

For too long, federal agencies have equated cloud migration with progress. However, true modernization isn’t about where your systems reside; it’s about how they perform, adapt, and deliver on your mission. Lift-and-shift approaches move technical debt from one environment to another. They don’t eliminate it. Modernization begins when agencies redesign the architecture and reimagine the mission workflow. It occurs when automation replaces manual approvals, when AI agents analyze patterns and trigger responses in real-time, and when legacy apps are decomposed into containerized services that scale on demand. We’re already seeing this in the field. One agency utilized AI agents to identify and re-platform over 500 legacy scripts into secure, cloud-native services, reducing migration time by 60% and eliminating redundant compute spend. Another deployed AI-driven workflow enables the detection of misconfigured cloud assets within minutes of deployment, reducing compliance risk and audit cycles from weeks to hours. At the tactical edge, defense programs are leveraging autonomous agents to filter and prioritize real-time sensor data in disconnected environments, delivering a decision advantage without human bottlenecks. These are not hypothetical use cases. They’re the future of mission execution—happening now. Cloud is not the goal. The mission is the goal. And modernization only delivers value when it fuses automation, AI, and architectural change to unlock new levels of speed, insight, and control. For CIOs, CTOs, and CISOs leading the next generation of federal transformation, the imperative is clear: Don’t just migrate, modernize with intent, intelligence, and mission alignment. #FederalIT #CloudModernization #MissionDrivenTech #AIinGovernment #GovCon #DigitalTransformation #LegacyToCloud #AIWorkflow #ZeroTrust #HAILMarketplace

The API-based progressive modernization approach to the Core Banking System Capgemini's World Retail Banking Report (WRBR) gives a great insight into planning of an API-based progressive modernization approach. The report mentions a two-pronged approach to core banking transformation and platfromification: 👉 1. Progressively modernize the core banking system via solutions such as resilience-by-design and a shift to the cloud 👉 2. In parallel, develop an API network that drives collaboration with ecosystem partners, to enable new products/services and revenue streams By becoming an ecosystem of business services, change can occur at pace. These business services are then underpinned by IT services which can operate at a Macro level (e.g. SaaS platforms) or at a micro level through the deployment of Micro Services architectures. APIs enables these services to be decoupled and exchange information through defined and secure contracts. Such a strategy does not have to happen via a big bang, it can be achieved through progressive modernization as services are split out of the monolithic systems and then operated independently, connected via APIs to the core platforms. In this context we can identify different types of APIs for different purposes, as illustrated in the Figure below. As the organization embraces such an API based model, then governance of the APIs across the organization will become critical to ensure the benefits are realized. APIs are the key ingredient for enabling such a transformation to create a modern, agile organization. Learn how thinking strategically can reduce costs, deliver business agility and drive innovation at the same time. 👉 Decomposing legacy mainframe platforms One of the key challenges when embarking on a progressive modernization journey is 'how to deal legacy mainframe platforms, which deliver core business processes. There are a number of steps: Define the target architecture at a high level so that it is clear where components can move to as they come off the mainframe, e.g. into new microservices or into other services that already exist 👉 Review the functional decomposition of the platform to define a high level business prioritization for decomposition Use tools to analyse the platform code base and database to define the code relationships, quality and opportunities for simplification. This will also enable clarity of the impact of changing or removing particular items of code Progressively simplify the platform, removing high change components and exposing APIs from the platform such that it can be presented as a utility to other services. This approach then enables the progressive modernization of the estate, whilst protecting the integrity of the service and managing the risk on the journey. 👉 Subscribe for more insights https://lnkd.in/d94JgWBU Source Mulesoft / Capgemini #fintech #payments #banking Thomas Leda Timothy Alex Ali Carlos