IBM Automation Head Emphasizes New AI Operating Models for Agentic AI
- Corporate competitiveness in artificial intelligence is no longer driven solely by the underlying models.
- Speaking on industry developments, Sundaram noted that autonomous software agents are beginning to execute real operational tasks.
- Organizations must prioritize how different systems connect and operate within daily workflows, rather than focusing exclusively on model capabilities.
Corporate competitiveness in artificial intelligence is no longer driven solely by the underlying models. According to IBM Automation and AI General Manager Neil Sundaram, the rapid expansion of agentic artificial intelligence demands entirely new operational models for enterprises.
The Operational Realities of Autonomous Software
Speaking on industry developments, Sundaram noted that autonomous software agents are beginning to execute real operational tasks.
Moving Beyond Text Generation in Workflows
Organizations must prioritize how different systems connect and operate within daily workflows, rather than focusing exclusively on model capabilities.
Sundaram emphasized that autonomous agents actively handle tasks rather than simply generating text or responding to prompts.
Infrastructure Demands for Continuous Automation
This fundamental shift means corporate infrastructure must evolve to support continuous automation across business units.
Industry observers point out that managing multiple AI agents requires robust integration frameworks. Companies are increasingly looking at operational frameworks that can monitor agent behavior, streamline data flows, and maintain security across enterprise environments.