Global AI spending is projected to surpass $2.8 trillion, yet only 41% of projects reach production. Overcome this ROI paradox by shifting from automation to governed orchestration: get a roadmap for the autonomous enterprise and strategies to ensure humans remain in control.
Enterprises use many types of artificial intelligence to support automation, but generative AI and agentic AI dominate current conversations. While generative AI systems excel at creating new content from existing data, agentic AI systems are built to pursue specific goals on their own. This distinction is key to understanding the future of workload automation, moving from content generation to autonomous, real-time decision-making.
Artificial intelligence is no longer confined to predicting outcomes or generating content. A new path is emerging, one defined by agentic AI systems that reason, plan and act with intent. This shift creates automation that doesn’t just follow a script but understands goals and adjusts to meet them.
We’re moving from systems that execute commands to systems that understand purpose and can adjust plans when reality pushes back. For enterprises, this evolution bridges a critical gap between automation and intelligence. It signals the arrival of agentic AI — a design approach that allows systems to reason, plan and act with intent.
Traditional automation is like a highway sign that merely reports congestion. Agentic AI is the traffic controller that proactively predicts jams and reroutes cars in real time without waiting for instructions. Learn what this powerful shift could look like in your environment.
Most artificial intelligence systems today are reactive, responding only when you ask them to perform a task. But imagine an assistant who proactively drafts a report, schedules the meeting and shares the results without waiting for instructions. That’s the promise of agentic AI: moving from reactive to proactive and from static outputs to meaningful outcomes.
Agentic AI represents a new class of artificial intelligence — beyond generative AI and large language models (LLMs) — that plans and reasons about goals and adapts autonomously when conditions change. Rather than executing one specific task at a time, it orchestrates outcomes, adjusting strategy to maintain flow even when disruptions occur.
Global supply chains sit at the intersection of volatility and expectation, where traditional automation excels at repetition but fails to improvise when reality shifts. Agentic AI changes that, moving beyond static rules toward systems that can perceive, reason, plan and act in real time. Instead of just generating insights for a person to review, agentic AI links insight directly to execution.
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