In 1995, two e-commerce giants were born that would forever change the way we shop: Amazon and eBay. These platforms introduced a faster, more convenient way for consumers to purchase everything from everyday essentials to items they didn’t even know they needed. A new marketplace was born, and companies that once relied solely on brick-and-mortar stores suddenly had to imagine what selling online could mean for their businesses and bottom lines.
As the online shopping footprint grew, many retail leaders started taking notice. When asked about it, Dan Nordstrom, former co-president of Nordstrom said “We are convinced that online shopping will be a major part of our future.” And sure enough, by the early 2000s, major retail brands like Macy’s, Nordstrom, and Gap began to dip their toes into e-commerce, though not without hesitation. They slowly built online infrastructures and started selling their products on the web.
James Zimmerman, former CEO of Macy’s, captured the industry sentiment perfectly when he said, “While the sales volume for department store-type goods on the Internet is relatively small today, we believe it is a phenomenon that will continue to grow.”
And grow it did. In 1998, Gap’s online sales generated a respectable $146 million in their first year, and by 2001, they had almost doubled that. It was clear that online sales had serious potential. But while many retailers were embracing the change, one industry was reluctant to jump on board: luxury fashion.
As Maureen Chiquet, former CEO of Chanel, famously declared, “Chanel will never sell its products online. The image of our brand is too important to expose it to the impersonal nature of the internet.” Many luxury brands held this belief. They saw the internet’s growing power but thought, “That’s for everyone else—not us. We’re different.”
Fast forward to 2010, and the reality had shifted. The writing was on the wall: consumers were shopping online, and even the most prestigious luxury brands, like Chanel, were forced to adapt and build their online presences. Today, you can buy a brand-new Hermès bag, which retailing price starts at $10,000, all from the comfort of your couch.
The story of online shopping is more than just a retail revolution—it’s a blueprint for how industries evolve and possibly a cautionary tale for mainframes. Today, we’re standing at a similar crossroads with artificial intelligence and the mainframe. Mainframe users are in danger of adopting the same mindset that luxury brands once held. The idea that AI doesn’t belong on Z systems, that mainframes were never designed with AI in mind, is a mindset we need to move past. Because the truth is, up until now, no system was designed with AI in mind, but that is what modernization is about. With the new z16 and z17 machines and Telum and Telum II processing chips, the mainframe is now equipped and built for AI integration. But just like luxury fashion once hesitated to go digital for fear of losing its identity, some users are still reluctant and believe the mainframe’s legacy status makes it incompatible with modern AI. But history shows us that resistance to innovation only delays the inevitable.
Think of it like the Nordstroms, Macys, and Gaps of the world. They didn’t dive headfirst into online sales—they phased it in, step by step, and the benefits became clear year over year. The same gradual, strategic approach can be applied to AI—and many organizations are already doing just that. Let’s explore how forward-thinking companies are utilizing IBM’s z16, z17, Telum and Telum II to integrate AI into their mainframes and seeing real, measurable benefits by staying at the forefront of innovation.
Bringing AI to the Mainframe
Just as retail giants once hesitated to sell online, many organizations today are still exploring how best to bring AI to their mainframes. But the tide is turning, and fast. Across industries, forward-thinking companies are proving that not only can AI thrive on the mainframe—it can unlock unprecedented value when it does. From securing mission-critical data to automating operations and sparking innovation, AI on the mainframe is rewriting the rules of what’s possible.
Mainframes have always been data powerhouses. Now, they’re becoming insight engines. With AI integrated directly into mainframe workloads, organizations are tapping into real-time analytics that drive smarter, faster decisions—without moving data off-platform. A 2024 IBM study found that nearly 80% of IT executives view mainframes as key to AI-driven innovation. Why? Because mainframes can process millions of transactions per second, enabling AI to analyze data exactly where it lives—reducing latency and boosting speed.
It’s not just theoretical. A 2023 Forrester report revealed that companies using AI on mainframes saw a 30% increase in decision-making speed for mission-critical use cases like fraud detection and customer personalization. In one case, banks leveraging AI reported a 25% drop in fraudulent transactions—millions saved, thanks to smarter, faster insights.
Efficiency doesn’t stop at insights. Intelligent automation on mainframes is reshaping how enterprises run. These systems are already optimized for high-volume workloads, but when infused with AI, they unlock even more value by automating complex tasks with speed and accuracy. A 2024 Deloitte study showed businesses reducing operational costs by up to 20% and improving process accuracy by 15% through AI-driven automation. For example, a global retailer that used AI to automate supply chain logistics saw a 35% jump in inventory turnover and saved $50 million annually. These aren’t just process improvements—they’re strategic advantages that ripple across the business.
Compliance is another space where AI on the mainframe is making a real difference. With automated monitoring and regulatory reporting, organizations are simplifying what was once a complex, labor-intensive process. A 2025 IDC study found that companies using AI-enhanced mainframes reduced compliance costs by 22% and audit preparation time by 30%. Unisys shared a case study where a government agency using their solution achieved 100% audit success while saving 500 labor hours annually. With AI doing the heavy lifting, teams can focus less on red tape and more on progress.
As AI transforms the way we work, security remains top of mind—especially with quantum computing on the horizon. Mainframes are already answering the call with quantum-safe encryption that protects critical data today while preparing for tomorrow’s threats. A 2024 Gartner report noted that 85% of organizations plan to adopt quantum-safe technologies by 2027, with mainframes leading due to their robust security architecture. Fujitsu’s whitepaper showed that their quantum-inspired solutions on mainframes reduced cybersecurity risk by 50% for a European bank—saving €10 million in potential fines. It’s not just about avoiding losses—it’s about building digital trust at scale.
The synergy between AI and the mainframe isn’t a trend—it’s a transformation. With 70% of global transactions by value still flowing through these systems, their relevance is as strong as ever. Modernization isn’t about abandoning the past—it’s about building on it. The mainframe has evolved before, and it can do it again. Let’s be the ones who lead the transformation, not the ones left scrambling to catch up.
Sources:
- IBM Institute for Business Value, 2024
- Forrester, "AI and Mainframes: Real-Time Analytics," 2023
- Deloitte, "Intelligent Automation in Enterprises," 2024
- CIO.com, "AI on the Mainframe," 2025
- Gartner, "Quantum-Safe Security Trends," 2024
- Fujitsu, "Mainframe Modernization Whitepaper," 2024
- IDC, "AI and Compliance in Enterprises," 2025
- Unisys, "Government Compliance Case Study," 2024
- Accenture, "Innovation Through AI," 2024
- Hitachi, "Annual Report," 2024
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