Autonomous AI systems are advancing past chat, tackling complex workflows and addressing trillion-dollar challenges in industries worldwide.
15%
By 2028, agentic AI will autonomously create, marking a significant jump from nearly none today.
$8T
The projected worldwide waste expenses in industries such as manufacturing and logistics that agentic AI seeks to address.
>40%
The predicted failure rate of agentic AI projects by 2027 stems from steep costs and outdated system hurdles.
Agentic AI isn’t simply a chatbot. It’s a platform capable of autonomous decision-making. perceive its environment, reason to create a plan, and act on that plan by integrating with APIs and other tools. It manages complete workflows.
Imagine it as the gap between an AI that can write an email (Generative AI) and an AI that can determine if an email is needed, draft it, locate the contact, send it, and plan a follow-up (Agentic AI).
Collects real-time data from APIs, databases, and sensors.
Uses LLMs to understand goals and create a multi-step plan.
Executes the plan by calling external tools and software.
Evaluates outcomes to refine strategies for future tasks.
Across sectors like finance and healthcare, 26 pivotal agentic applications are rising to address targeted, high-impact challenges. Here's how these agents are distributed across key industries.
Increased autonomy requires heightened oversight. Building an agentic enterprise is fraught with hurdles, including high project failure rates and the pressing need for strong governance to address risks like liability and algorithmic bias.
Elevated costs and challenges with legacy system integration are key factors behind this high failure rate.
AI systems may reinforce and escalate biases in their data, resulting in inequitable effects.
Vague laws on liability when autonomous systems cause expensive mistakes.
Challenges in clarifying AI decisions, a vital requirement in regulated sectors.