Kyndryl Expands AI, Generative AI Services To Accelerate Mainframe Modernisation

IT infrastructure services provider Kyndryl has launched new artificial intelligence and generative AI-based consulting and managed delivery services to help organisations leverage their mainframe application and data assets as part of hybrid cloud transformation.

To help enterprise adoption of AI and generative AI, Kyndryl said it is training more than 5,000 mainframe professionals with new AI skills in addition to certifications in mainframe and cloud technologies.

According to Kyndryl research, 90% of organisations state that mainframe remains essential to their business operations, and 95% are modernising and running it as part of a hybrid cloud environment. To speed mainframe transformation projects, organisations are looking to embrace AI and generative AI technology, but often lack the expertise needed for effective deployment.

To help enterprises overcome such challenges, Kyndryl will tap into AI and generative AI tooling and insights from AI-infused Kyndryl Bridge open integration platform. In addition, the expanded AI capabilities for mainframe are supported by Kyndryl Consult and complement its AI and data advisory and implementation services, which include assessments, workshops and proofs of concept. The company said it will also continue its collaboration with hyperscalers and partners to assist enterprises in defining and achieving their AI goals.

According to Kyndryl, its services can help mainframe customers in the following scenarios and potential use cases:

Moving Workloads Off Mainframe To Cloud

• Deliver generative AI-produced and Kyndryl-enhanced application documentation.

• Enable automated conversion of mainframe application code to modern languages (e.g., COBOL to Java).

• Provide AI-based insights into business logic and data relationships.

Integrating Mainframe Applications And Data With Cloud Or Distributed Environments

• Enabling secure access to mainframe data used in cloud-based AI solutions and secure interoperability between mainframe and cloud solutions.

• Drive asset financial usage through AI-infused, dynamic workload placement.

Modernising Workloads On Mainframe

• Advise on application code modernisation to avoid potential production issues.

• Integrate real-time AI solutions into mainframe applications.

• Enable quantum-safe data encryption for compliance with security and regulatory requirements.

Optimising Application Developer Agility

• Implement tools and processes for development, security and operations, including AI-based recommendations, for real-time insights into programming best practices.

• Enable data scientists to build new AI models and integrate large language model operation tools with existing tools and processes.