Quick Takeaways:
- Legacy systems (like mainframes) are still the backbone of global business - handling 90% of credit card transactions worldwide
- You don't need to replace your trusted legacy systems to use AI - there are multiple ways to integrate them
- Major tech companies (IBM, Microsoft, Google, AWS) offer different approaches to bringing AI to legacy systems
- Organizations are already seeing major benefits: faster fraud detection, improved customer service, and automated code modernization
Introduction: The Old and The New Can Work Together
Remember that 20-year-old mainframe system that processes all your company's core transactions? The one written in programming languages your new hires have never heard of. What if I told you, it could be at the center of your AI strategy?
You might be thinking: "Our legacy systems can barely connect to the internet—how could they possibly handle something as cutting-edge as artificial intelligence?"
The good news is that today's technology landscape isn't about choosing between keeping your reliable legacy systems OR embracing AI. You can have both. In fact, those trusted workhorses might be the perfect foundation for your AI initiatives.
This blog will explore how legacy systems and AI can work together, what approaches are available, what vendors are offering, and how real companies are already making it happen. Whether you're a C-suite executive trying to chart a technology strategy, a college student curious about how the business world really works, or an operations professional keeping the lights on, you'll find practical insights here.
Legacy Systems: Still Running the World (Seriously!)
When we talk about "legacy systems," we are usually referring to mainframes (those room-sized computers from IBM), midrange servers (like the IBM AS/400), and custom-built applications running on older hardware. You might be surprised to learn just how prevalent these systems still are:
- 71% of Fortune 500 companies still rely on mainframes
- Mainframes process 90% of all credit card transactions worldwide
- These systems handle 68% of global production IT workloads
- Yet they account for only 6% of IT costs (talk about being efficient!)
- 92 of the top 100 banks and all top 10 insurers worldwide use IBM mainframes
- The mainframe market is projected to grow to USD 6.2 billion by 2032 with a CAGR of 7.9%
- The median mainframe programmer salary has increased significantly to $111,116, reflecting the continued demand for this expertise
That trusty ATM you used this morning. Mainframe. Your airline reservation? It is again a mainframe. Insurance claims? Yep, mainframe again.
Real Example:
When you swipe your credit card at a store, that transaction travels through multiple systems, but its critical processing—checking your balance, authorizing the payment, and recording the transaction—likely happens on a mainframe somewhere. In milliseconds, this 60-year-old technology determines if you're going home with your groceries.
Why haven't these dinosaurs been replaced? Because they're incredibly reliable (99.999% uptime, which means just minutes of downtime per year), secure, and capable of processing thousands of transactions per second. For many organizations, "if it isn't broken, don't fix it" applies—especially when the system handles mission-critical operations.
Past Approaches to Modernization: What We've Learned
Over the past decade, companies have tried various approaches to modernize their legacy systems:
- Big Data Integration
Connecting mainframes to analytics platforms
To unlock insights from legacy data without disrupting core systems
Mixed results
A bank implemented Hadoop to analyze mainframe transaction data but faced data freshness and security issues - API Wrapping
Putting modern interfaces around old systems
To extend access to legacy functions for web/mobile users
Often successful
An airline wrapped its mainframe reservation system with APIs, enabling modern mobile apps while preserving core logic - Virtualization
Running legacy software on modern hardware
To reduce dependence on aging physical hardware
Good for hardware costs
A manufacturer virtualized their inventory system to run on modern servers, extending its life while reducing maintenance costs - Complete Rewrites
Rebuilding everything from scratch
Belief that clean-slate approaches would be simpler than adaptation
74% failure rate
A government agency's $50M benefits system replacement ballooned to $80M after 3 years, delivering only partial functionality - Partial Modernization
Updating specific components while keeping the core intact
To balance innovation with risk management
Most practical approach
A retailer kept their mainframe inventory system but modernized customer-facing applications with real-time integration
The key lesson? Total replacement is risky. Successful modernization usually takes an incremental approach that preserves what works while carefully adding new capabilities.
Four Ways to Bring AI to Your Legacy Systems
There isn't just one way to marry AI with your legacy systems. Depending on your goals, timeline, and resources, you might choose any of these approaches:
- AI ON the Legacy Platform
This approach brings AI capabilities directly to your existing hardware.
Real Example: IBM's new z17 mainframe unveiled in April 2025 includes plans for dedicated "Spyre" AI accelerator chips, with full availability expected in Q4 2025. A North American bank using similar on-board AI technology was able to analyze 100% of transactions in real-time (15,000 per second!). This cut fraud detection time from 80 milliseconds to just 2 milliseconds, saving $20 million annually in fraud losses.
Benefits: Super-fast processing, no data movement needed, maximum security.
Challenges: Requires newer hardware, may have capacity limitations - AI ALONGSIDE the Legacy System
With this hybrid approach, your AI runs on separate modern systems but connects tightly with your legacy data and applications.
Real Example: A regional insurance company kept its customer policy data on an AS/400 midrange system but added an AI-powered chatbot in Microsoft Azure. When customers ask questions about their coverage, the chatbot pulls data in real-time from the legacy system through secure APIs, giving accurate answers without moving all the data to the cloud.
Benefits: Leverages cloud scale for AI while keeping core data secure.
Challenges: Requires careful integration and data management - AI TO MODERNIZE the Legacy
Here, AI tools help transform the legacy code itself, making it more modern and maintainable.
Real Example: A government agency used Google's AI-based Mainframe Assessment Tool (MAT) enhanced with Gemini models to analyze their 40-year-old COBOL codebase. The AI created detailed documentation of what each program does and identified dependencies that even their senior developers weren't aware of. This mapping made it possible to safely modernize critical sections without breaking the system.
Benefits: Reduces reliance on scarce legacy expertise, makes systems more maintainable.
Challenges: Still requires human oversight to validate AI's work - AI FOR Legacy Operations
This approach uses AI to better manage and monitor your legacy systems.
Real Example: A retail company applied IBM's AIOps tools to monitor their mainframe performance. The AI learned normal patterns and could predict potential outages before they happened, reducing system downtime by 87% and saving millions in lost revenue.
Benefits: Improves stability, reduces operational overhead.
Challenges: Requires good historical data to train the AI effectively
What the Big Tech Players Are Offering
Each major technology vendor has their own approach to bringing AI to legacy systems:
- IBM: Modernize in Place
IBM's strategy is to enhance mainframes with built-in AI capabilities. Their z17 mainframe includes plans for dedicated AI chips that can process 450 billion AI operations daily.
Key offerings:- IBM z16/z17 Mainframes with Telum II processors and planned Spyre AI accelerators (scheduled for Q4 2025)
- Simple explanation: These are next-gen mainframes with special AI chips built right in, so you can run AI directly where your data lives.
- Example: A bank uses this to check every credit card transaction for fraud in real-time as happens, without having to move sensitive data elsewhere.
- IBM Watsonx Code Assistant for Z
- Simple explanation: This is like having an AI assistant that understands old mainframe programming languages and helps modernize them.
- Example: When a company's only COBOL expert retired, they used this tool to help new developers understand and update the 30-year-old code for their inventory system.
- IBM Z Digital Integration Hub
- Simple explanation: This creates secure bridges between your mainframe data and other systems.
- Example: A healthcare provider uses this to safely connect patient data from their mainframe to a new mobile app for doctors, without copying sensitive information.
- Microsoft Azure: Hybrid Integration
Microsoft focuses on connecting legacy systems to cloud-based AI through flexible integration.
Key offerings:- Azure Logic Apps with mainframe connectors
- Simple explanation: These are visual tools that let you connect mainframe systems to cloud services without complex coding.
- Example: An insurance company uses Logic Apps to automatically pull customer data from their mainframe whenever someone fills out a form on their website.
- Azure AI Services that work with legacy data
- Simple explanation: These are ready-to-use AI capabilities like language processing, image recognition, and chatbots that can work with your mainframe data.
- Example: A government agency connected their decades-old citizen records system to Azure AI translation services to automatically provide documents in multiple languages.
- Mainframe migration tools
- Simple explanation: These help you move specific mainframe workloads to the cloud when you're ready.
- Example: A retailer moved their seasonal sales analysis from their mainframe to Azure, freeing up mainframe capacity for transaction processing while gaining better reporting capabilities.
- Amazon Web Services: Cloud Migration
AWS emphasizes moving legacy workloads to the cloud to leverage their AI services.
Key offerings:- AWS Mainframe Modernization service with code conversion tools
- Simple explanation: This helps translate your old mainframe programs into modern code that runs in the cloud.
- Example: A manufacturing company converted their parts inventory system from COBOL to Java, making it easier to find developers who could maintain and enhance it.
- Integration solutions to gradually shift workloads
- Simple explanation: These let you move one piece at a time instead of all at once.
- Example: An airline kept their core reservation system on the mainframe but moved customer loyalty analytics to AWS, creating a hybrid system that combines strengths of both.
- Full suite of AI/ML services once data is in AWS
- Simple explanation: These are powerful AI tools for predictions, recommendations, and analysis that become available once your data moves to AWS.
- Example: After moving their customer data to AWS, a telecom company used Amazon SageMaker to predict which customers might cancel service, allowing for targeted retention offers.
- Google Cloud: AI-Powered Transformation
Google uses its AI expertise to help analyze and convert legacy applications with significant new offerings launched in April 2025.
Key offerings:- Google Cloud Mainframe Assessment Tool (MAT) enhanced with Gemini models
- Simple explanation: This AI-powered tool examines your entire mainframe system and creates maps and documentation of how everything works.
- Example: A bank with 3,000+ mainframe programs used this to automatically document their systems when they discovered their technical documentation was 15 years out of date.
- Google Cloud Mainframe Rewrite
- Simple explanation: This uses AI to convert old mainframe code into modern programming languages.
- Example: A state government used this to convert their 40-year-old tax processing system from COBOL to Java, reducing maintenance costs by 60%.
- Dual Run
- Simple explanation: This runs your old and new systems side-by-side to make sure they produce identical results before you switch over.
- Example: An insurance company processed all new claims through both their mainframe and their new cloud system for three months, comparing results to ensure the new system was 100% accurate before going live.
- Kyndryl Mainframe GenAI Accelerator
- Simple explanation: This is a partnership program that combines Google's AI with Kyndryl's mainframe expertise to create customized modernization plans.
- Example: A global bank used this service to create a 3-year roadmap for modernizing their core banking system, identifying which parts to keep on the mainframe and which to move to the cloud.
Real Success Stories: AI + Legacy in Action
- Banking: Lightning-Fast Fraud Detection
A large North American bank integrated AI fraud detection directly on their mainframe. Results:- 100% of transactions analyzed in real-time (vs. 20% before)
- Processing time cut from 80ms to 2ms
- $20 million annual savings from reduced fraud
- Insurance: Smarter Underwriting
Global insurer AIG applied generative AI to their legacy underwriting process:- Data accuracy improved from 75% to 90%
- Processing time significantly reduced
- Better risk assessment with fewer errors
- Government: Enhanced Citizen Services
A state DMV connected an AI chatbot to their mainframe-based vehicle registration system:- Citizens could use natural language to request services
- Wait times reduced by 65%
- Staff freed up to handle complex cases
- No need to replace the reliable core system
Testing and Validation: Ensuring Success
- Parallel Processing: Google's Dual Run solution allows applications to run simultaneously on both mainframe and cloud environments, comparing outputs to verify application correctness before cutover
- Risk Reduction: This parallel approach significantly reduces migration risk by ensuring modernized applications function correctly before decommissioning legacy systems
- Confidence Building: Teams can gain confidence in AI-enhanced or AI-transformed applications by seeing them work alongside trusted legacy operations
With these validation approaches, organizations can adopt AI technologies with greater confidence and lower risk.
Getting Started: Your Legacy-to-AI Roadmap
Ready to bring AI to your legacy systems? Here's a simple roadmap with practical examples:
- Assessment: Identify which legacy systems would benefit most from AI.
Example: A retail bank examined their systems and found their credit card approval process was the best candidate—it contained valuable customer data but had slow decision times that frustrated applicants. - Choose your approach: Will you run AI on your legacy platform, alongside it, use AI to modernize it, or apply AI to operations?
Example: After evaluating options, the bank decided on a hybrid approach—keeping account data secure on the mainframe while connecting it to an Azure-based AI for faster credit decisions. - Start small: Pick a focused use case with measurable benefits
Example: Instead of overhauling their entire credit system, they started with just the initial application screening process, affecting 200 applications per day with a clear metric: reduction in decision time. - Partner wisely: Select vendors who understand both your legacy environment and AI.
Example: The bank chose a partner who had worked with both IBM mainframes and Azure AI services, bringing in specialists for a 2-day workshop to map integration points between systems. - Build skills: Train your team to work across both technologies.
Example: They sent two mainframe developers to Azure AI training and had an AI specialist shadow the mainframe team for a month, creating documentation for how the systems would interact. - Scale gradually: Apply lessons from early successes to broader initiatives.
Example: After reducing credit decision time by 70% in the initial project, they applied the same integration pattern to fraud detection, then to personalized product recommendations, building on each success.
Conclusion: The Best of Both Worlds
The future isn't about choosing between legacy reliability and AI innovation, it's about combining them. Your trusted legacy systems contain decades of business logic and valuable data. AI can help you extract more value from that foundation.
Whether it's running AI directly on modernized mainframes, connecting legacy systems to cloud AI, using AI to help transform old code, or applying AI to better manage operations, there's a path forward that builds on your existing investments rather than discarding them.
