The Challenge: The Silent Aging Infrastructure Crisis

IT leaders around the world face an impossible choice today: keep running reliable but aging infrastructure that constrains organizational growth and competitive agility, or undertake costly, time-consuming, disruptive modernization projects to unlock it. 

Over 100,000 companies worldwide run their most critical operations on IBM i systems, a trusted but aging system launched 35+ years ago. Major retailers to top banks and insurers rely on this foundational platform for their inventory, financial, and customer records. 

However, the people with the institutional knowledge to maintain, modify, and modernize these systems are now retiring, and connecting the system to modern intelligence technology like cloud analytics platforms and AI-powered decision tools is challenging. 

When Manual Reporting Holds Your Business Back

McCarthy Tire Service, a 100-year-old commercial tire and retail services company, faced exactly this IT dilemma. Their IBM i AS/400 mainframe was the operational backbone of their $700 million annual revenue business. 

Each week, their IT team spent hours manually extracting sales and inventory data from the mainframe, cleaning and aggregating it across its 100+ retail locations, then compiling Excel reports for management. 

As a top 5 ranked commercial tire business in North America, they needed near-real-time insights to optimize inventory, sales, and purchasing decisions across their growing business. 

The Solution: AI Agents That Build Foundational Models in Days 

McCarthy’s IBM i system had 30+ years of crucial data, 40,000+ tables with limited schema documentation, and institutional knowledge about the system concentrated in a few veteran employees. CIO Lee Lispi needed to take a radically different approach to modernize their infrastructure.

Instead of humans teaching consultants who then document the system, he partnered with Turgon’s agentic IT modernization platform to use specialized AI agents to learn directly from the system itself. 

In just four days, the AI agents created a complete semantic model of McCarthy's IBM i system, delivering what traditionally takes 3-4 months to produce. 

The Data Dictionary Agent parsed database schemas, analyzed table structures and relationships, examined actual data patterns, and cross-referenced system documentation and historical requirements. It processed information at a scale and speed impossible for human teams, analyzing patterns across 40,000+ tables simultaneously, identifying implicit relationships even when not formally defined, and flagging inconsistencies or anomalies for human review.

Unlocking Business Insights with Modern Cloud Analytics 

With the semantic foundation in place, specialized AI agents divided the complex migration work to establish cloud-native data infrastructure built for McCarthy's future. 

A Data Integration Agent designed extraction and transformation logic to move data from IBM i into Amazon S3 using Apache Iceberg. Coding Agents generated pipeline code implementing a bronze/silver/gold medallion architecture in Snowflake for progressive data refinement. IBM i and Snowflake specialist AI agents ensured platform-specific optimization while building dozens of interactive dashboards in AWS QuickSight.

Five weeks from project kickoff to real-time dashboards in full production, McCarthy's leadership team had what they'd been manually compiling in Excel every week: current visibility into inventory, sales, and purchasing trends across their entire operation.

Interactive dashboards now provide instant visibility into inventory levels, sales performance, and purchasing patterns. Leadership can spot trends, make fast decisions, and evaluate opportunities, from geographic expansion to acquisition targets to new service lines, with complete confidence. Their data infrastructure was once a drain on company resources. Now it is a strategic advantage.

The New Standard for Legacy Modernization 

McCarthy Tire Service proved that AI-native infrastructure modernization is incrementally better across every dimension than traditional approaches. 

Speed: The complete infrastructure modernization wrapped in five weeks, three times faster than traditional approaches would have taken. 

Cost: The AI-led approach reduced costs by 70% compared to the typical $500K-$1.5M costs, with radical efficiency coming from AI agents handling up to 90% of the work (semantic modeling, pipeline code generation, validation) and human experts focusing on refinement and business alignment. 

Zero disruption: AI agents built extraction and transformation pipelines without touching production systems until validated. No downtime, no delayed transactions, no business process interruption.

Living Infrastructure: McCarthy’s infrastructure now contains documented, accessible system intelligence that was previously trapped within soon-retiring employees. 

For the companies worldwide running critical operations on legacy systems, McCarthy's five-week transformation demonstrates a path forward that doesn't require choosing between speed, quality, and cost. 

Contact us to explore how Turgon’s AI-speed infrastructure modernization can work for you.