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Kyndryl expands AI and generative AI services and skills to accelerate mainframe modernization

Kyndryl, the world’s largest IT infrastructure services provider, today unveiled new artificial intelligence (AI) and generative AI-based consulting and managed delivery services to help organizations exploit their mainframe application and data assets as part of their hybrid cloud transformation.

Kyndryl is committed to furthering the adoption of AI and generative AI by educating more than 5,000 of its mainframe professionals with new AI skills on top of their tens of thousands of certifications in mainframe and cloud technologies. With this initiative, Kyndryl will be poised to help mainframe customers accelerate development and deployment of AI and generative AI-enabled solutions in their hybrid cloud environments.

At 60 years of age, the mainframe remains a strategic IT foundation for thousands of organizations worldwide. According to research from Kyndryl, 90% of organizations state that the mainframe remains essential to their business operations, and 95% are modernizing and running it as part of a hybrid cloud environment. To speed mainframe transformation projects, customers are seeking to embrace AI and generative AI technology, but often lack the skills and expertise needed to effectively deploy this in their infrastructure and applications.

To help mainframe customers explore, kick-start or scale the deployment of responsible AI, Kyndryl is tapping into AI and generative AI tooling as well as the operational insights from its AI-infused Kyndryl Bridge open integration platform. In addition, the expanded AI capabilities for mainframe are supported by Kyndryl Consult and complement the company’s AI and data advisory and implementation services which include assessments, innovation workshops and proofs of concept. Kyndryl is also continuing its collaboration with the hyperscalers and other partners to assist customers in defining and achieving their AI goals to drive critical business outcomes.

“Kyndryl is taking a bold next step to enable and accelerate our customers’ mainframe modernization journey by unlocking their applications and data through deployment of AI and generative AI,” said Petra Goude, Global Practice Leader for Core Enterprise & zCloud at Kyndryl. “Our innovative approach will help customers reduce IT transformation efforts and costs, enhance agility and resiliency, mitigate skills challenges, and drive better outcomes by optimizing their mainframe estates to thrive in hybrid environments.”

“The success of AI depends heavily on an organization’s ability to fully leverage all enterprise information assets regardless of data format or application platform. Mission-critical information created and managed by mainframes needs to be fully integrated into enterprise generative AI and other data-intensive use cases in order to fully enable data-driven decision making and digital innovation,” explains Mary Johnston Turner, IDC Research Vice President for the Future of Digital Infrastructure. “Kyndryl’s commitment to mainframe modernization for generative AI and other AI use cases targets this set of important enterprise infrastructure requirements.”

Kyndryl’s new comprehensive services can help mainframe customers optimize the right workload on the right platform using AI and generative AI for the following scenarios and potential use cases:

Moving workloads off the mainframe to the cloud:
• Deliver generative AI-produced and Kyndryl-enhanced application documentation
• Enable fast and efficient automated conversion of mainframe application code to modern languages (e.g. COBOL to Java)
• Provide AI-based insights into business logic and data relationships to help customers quickly address new business opportunities.
Integrating mainframe applications and data with cloud or distributed environments:
• Assist in enabling secure access to mainframe data used in cloud-based AI solutions and secure interoperability between mainframe and cloud solutions
• Drive improved asset financial usage through AI-infused, dynamic workload placement.
Modernizing workloads on the mainframe:
• Advise on application code modernization to avoid potential production issues
• Integrate real-time AI solutions into mainframe applications for better business insights
• Enable quantum-safe data encryption to enhance customers’ compliance with their security and regulatory requirements.
Optimizing application developer agility:
• Implement DevSecOps tools and processes, including AI-based recommendations, for real-time insights into programming best practices and faster time-to-market for customers
• Enable data scientists to build new AI models and integrate these LLMOps tools with existing DevSecOps tools and processes.