Kyndryl Unveils Advanced AI Tool to Boost Enterprise Innovation

Kyndryl formally introduced the Agentic AI Framework in Singapore. As a global IT service provider, the goal of the framework is to improve the ability for businesses to operationalize through self-learning AI agents. In addition, the Agentic framework allows enterprises to scale AI solutions across on-premises and/or cloud and/or hybrid environments.

The framework includes sophisticated algorithms and is purposefully secure so as to empower a comprehensive IT workflow while maintaining human governance.

• Kyndryl launches Agentic AI Framework to scale secure enterprise AI
• New AI tool by Kyndryl bridges trust gap with 'human-in-the-loop' design
• Kyndryl's AI framework targets public service and financial sector use

The framework is being considered in multiple sectors of the economy. A national government is looking at the framework to enhance public services and compliance. A financial institution is examining the framework as a means to automate compliance and optimize operational tasks in IT.

Kyndryl’s announcement aims to directly address the scepticism in implementing AI, which continues to restrict the impact on the business.

Also Read: How finance organizations deal with AI/digital transformation challenges

Based on Kyndryl's own research, only 4 in 10 leaders are using AI-powered insights in decision-making today, highlighting the gap between AI's promise and business use A trust gap exists for a number of reasons, such as AI's "black box" quality that makes understanding decision-making processes difficult for users - also the quality of the data impacts the considerations AI has for making choices.

Organizations in particular struggle with challenges associated with AI integration with existing systems that create technical blockers to adoption, even when leaders support the AI adoption effort.

Kyndryl's prioritization of the concept of "humans in the loop", traceability and "secure-by-design" speaks to the documented trust barriers empirical studies have identified limiting AI deployment by enterprise.