Home Page Title Put AI to work with IBM Z Put AI to work with IBM Z
Create new value, from application modernization to fraud protection
Overhead view of a desk with one person working on a schematic of an IBM z16 process
New value, existing workloads

If AI isn't your newest business partner, it should be. The transformative technology of AI is already helping enterprises tackle business challenges. In fact, references to AI on earnings calls were up 77% year-over-year in early 2023.¹

However, organizations have still been slow and selective in AI adoption. Why is it that although innovative generative AI models have captured consumers’ imagination, many businesses are still hesitant to take action?

Top AI-related business concerns can include:

  • Trusted data and foundations
  • Governance capabilities
  • AI’s impact on service levels
  • AI’s impact on existing workloads
  • AI and sustainability
  • The entire AI lifecycle’s impact on IT operations

To address these opportunity areas, businesses require a strategic approach to AI—one that handles each workload individually, designed to offer the flexible speed and scale capabilities as needed‚ all within a hybrid cloud environment. Let’s explore the potential of such an approach, and how AI for IBM Z® technology can impact your organization.

Many organizations divide AI into two use cases:
AI capabilities that replace manual activities

These can range from AIOps for IT operations to bots engineered to elevate customer service—focusing on productivity, cost reduction and user experience.

AI capabilities that augment existing operations

These can include applications and workflows—focusing on creating new value within an organization’s services.

The second category of use cases is paramount as businesses move forward into the AI era. Decision-makers are looking to use AI to enhance or improve operations already in place within their organizations.

Integrating AI into the preexisting supporting application logic is designed to achieve these improvements. Built for IT infrastructures to use AI capabilities without having to start from scratch, this approach can help improve cost, efficiency, time and production—while driving trust and governance.

The potential AI brings to business is why IBM is laser-focused on making this technology more accessible than ever for use in day-to-day work. Let’s explore how IBM Z is engineered to play a key role as you deploy AI for use cases within your business by combining AI inferencing with the power and resilience of a hybrid cloud environment.

Delivering value at speed and scale with AI

Worldwide spending on AI-centric systems is expected to hit USD 154 billion in 2023—up 27% over 2022.2 Businesses have reprioritized their goals because of the value, importance and opportunities that clients identify within AI-driven projects.

As many clients already use IBM Z technology to run their core operations and store their mission-critical data, it becomes a logical place to run those AI capabilities they need such as AI inferencing.

Worldwide spending on AI-centric systems is expected to hit USD 154 billion in 2023—up 27% over 2022.

With IBM Z, businesses can deliver AI inferencing at the speed and scale needed to embed AI capabilities into existing applications. Often mission-critical and time-sensitive workloads, these applications can better influence almost every business transaction without adversely impacting service levels. Users can accelerate modernization of these important workloads and integrate them seamlessly across the IBM Z platform, adding even more speed and resilience to your approach to AI.

The ability to deliver AI with IBM z16™ is not based on any single technology; it is the result of a highly differentiated total system design. IBM z16 is designed to bring together massive transaction volumes, huge data volumes and highly scalable AI in one system. This combination is the differentiating factor to other solutions.

IBM z16 is a commercial enterprise-class server with real-time, latency-optimized AI acceleration integrated into the IBM Telum® microprocessor chip. This technology is designed to execute up to 300 billion inference requests per day with only 1 ms latency.3

With this kind of reliability and resilience, it’s clear to see how new technologies such as AI can empower and extend existing capabilities—and a hybrid infrastructure can play a key role in these results.

Watch how AI can lessen credit card fraud (04:48)

While businesses further integrate AI into their strategies, they should be open to a hybrid approach to AI. That’s why we’ve made it so you can build and train your AI models on any platform and then take advantage of IBM Z technology for AI deployment, using the same open-source tools and methodologies for AI models across your business.

Let’s look at an example of the potential that AI for IBM Z delivers. Today, large banks and payment processors who use AI models often run them on only a fraction of transactions due to throughput and latency constraints with their fraud detection systems. As a result, many fraudulent transactions go unmonitored and undetected. Using IBM z16 Integrated Accelerator for AI can help systems inference at unprecedented speed and scale, enabling up to 100% of transactions to be scored for fraud.³

By keeping all of this financial data on IBM Z, you inherit its leading security and resiliency capabilities for data protection and privacy.

Read how IBM Z is built to help stop fraud
Welcome to the new era of generative AI

Since one of AI’s most profound benefits is efficiency, modernizing your already-existing applications is critical in the era of AI and digital-first IT.

IBM watsonx™ is a new generative AI platform that can help you unlock new opportunities around code conversion and code re-architecting. This next-generation platform is designed to have an end-to-end DevOps pipeline that is enterprise-ready and built for business.

Nearly 7 in 10 IT executives say mainframe-based applications are central to their business and technology strategies.4 Modernizing applications can help reduce technical debt, freeing up resources and improving process efficiencies.

The IBM watsonx portfolio of AI technology includes the IBM watsonx Code Assistant for Z, a new solution for mainframe application modernization built to increase developer productivity and agility. 5

With watsonx Code Assistant for Z, developers can:

Better understand their COBOL code and its dependencies


Refactor selected elements of an application and continue modernizing in COBOL

Selectively transform COBOL code to high-quality Java through generative AI

Help client developers validate COBOL code through generative AI

Users achieve these capabilities while keeping control and governance in their hands at lower cost and risk than today’s alternatives. In fact, it’s estimated that users can experience a 30% reduction in time to complete coding tasks through the combination of human and AI assistants working in tandem by 2028.5

With these features, IBM Z users are now able to advance mainframe application modernization goals for mission-critical workloads with automated and AI-assisted capabilities. By opting into these more intentional modernization choices, leaders can maximize ROI while accelerating the impact of generative AI.

Designed to accelerate development while maintaining the principles of trust, security and compliance, the product leverages generative AI based on the IBM Granite foundation models for code running on IBM's watsonx platform. 

Every day, AI continues to profoundly change the way the world does business. As organizations continue to transform and innovate with this technology, it’s crucial to use solutions built for business, with the transparency, trust, governance and efficiency needed for today’s IT landscape.

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1 A.I. is the star of earnings calls as mentions skyrocket 77% with companies saying they’ll use for everything from medicine to cybersecurity (Link resides outside ibm.com), Fortune, 1 March 2023.

2 “Worldwide Spending on AI-Centric Systems Forecast to Reach USD 154 Billion in 2023, According to IDC,” IDC, 7 March 2023. https://www.idc.com/getdoc.jsp?containerId=prUS50454123 (Link resides outside ibm.com)

3 Performance result is extrapolated from IBM internal tests running local inference operations in an IBM z16  LPAR with 48 IFLs and 128 GB memory on Ubuntu 20.04 (SMT mode) using a synthetic credit card fraud detection (Link resides outside ibm.com) model exploiting the IBM Integrated Accelerator for AI. The benchmark was running with 8 parallel threads each pinned to the first core of a different chip. The lscpu command was used to identify the core-chip topology. A batch size of 128 inference operations was used. Results were also reproduced using an IBM z/OS  V2R4 LPAR with 24 CPs and 256 GB memory on an IBM z16 mainframe. The same credit card fraud detection model was used. The benchmark was executed with a single thread performing inference operations. A batch size of 128 inference operations was used. Results may vary.

4 Application modernization on the mainframe, IBM Institute for Business Value, July 2021.
https://www.ibm.com/downloads/cas/7BJPNGND (PDF, 410 KB)

5 Emerging Tech: Generative AI Code Assistants Are Becoming Essential to Developer Experience (Link resides outside ibm.com), Gartner, 11 May 2023.