Enterprise AI has moved past the assistant phase. The next wave is Agentic or Autonomous AI that doesn’t wait to be prompted. These systems sense signals, reason through context, and act with minimal human input, making them fundamentally di erent from the chatbots and co-pilots that preceded them.
For IT operations, the shift is less about capability and more about consequence: what an agent does wrong at 2am, at scale, without a human in the loop, matters in ways that a chatbot response never did.. For IT leaders, agentic AI is not just better automation. It’s a move from reactive monitoring to autonomous investigation and resolution, fundamentally reshaping enterprise operations. Getting there requires more than the right model. It requires clean knowledge foundations, deliberate integration design, and a clear model for where humans stay in control. This piece covers what that looks like in practice: what works, what consistently fails, and where to focus first.
Traditional automation, a predefined rule determines how a system reacts whenever an event occurs. This means there is a direct association between the event occurring and the system executing a reaction. The entire decision-making process is explicitly programmed beforehand, with every single outcome being predictable.
Devanathan Desikan, AVP & AI Architect of Digital Services at Movate, shares vital perspectives from his experience in leading AI-driven offerings for the software delivery lifecycle.