Kyndryl launches Agentic Service Management for enterprise AI readiness evaluations

Kyndryl has launched its Agentic Service Management, a maturity assessment designed to evaluate an organisation’s current state of AI and prioritises gaps across service management, AI governance, security and operations.

The ASM offering combines a maturity model with structured assessments and implementation blueprints, with the aim of helping enterprises transition from traditional service operations to autonomous, intelligent workflows at scale.

It assesses alignment with emerging industry standards and governance frameworks for AI-native environments, which allows customers to adopt agentic IT service management with security and reliability as design principles, the company said.

ASM is offered through Kyndryl Consult, with an assessment designed to help customers review existing policies, controls and workflows against relevant standards and frameworks to determine readiness for agentic operations, pursuant to ISO 42001.

Once the assessment is done, the company delivers a tailored gap analysis and phased roadmap to help customers adopt agentic IT service management responsibly, with guardrails and human oversight to support autonomous capabilities across cloud-native and AI-native environments.

Also available as a standalone service is Kyndryl Agentic AI Digital Trust, which supports Agentic Service Management and aims to help enterprises govern, reduce risk and scale agentic AI deployments across hybrid and multi-cloud environments. It provides a security-first framework for managing how AI agents operate.

“Most enterprise environments were built for people running tickets and tools, not for fleets of autonomous agents executing tasks across hybrid and multi-cloud estates - and this mismatch is limiting AI from moving out of pilots to outcomes,” said Kris Lovejoy, global head of strategy, Kyndryl.

“You can’t scale agentic workflows on top of operating models that were designed for manual work. Organisations need clear controls, repeatable practices and measurable stages of adoption so AI agents can act autonomously where appropriate—while people remain accountable for governance, risk and service outcomes.”