Whitepapers & eBooks
Mar 11, 2026
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Unlike traditional AI models that rely on explicit prompting, agentic AI operates with autonomy, making independent decisions and optimizing workflows dynamically. Predictions indicate that in a few years, one-third of enterprise software will rely on agentic AI, and these agents will make up to 15% of daily work decisions.
The rise of agentic AI is poised to surpass even the impressive advancements of generative AI, driving enterprises to explore how this technology can revolutionize their operations. The true potential of agentic AI can only be realized through the lens of process intelligence. Without a granular understanding of operational processes, AI-driven decision-making may lead to inefficiencies rather than improvements.
Agentic AI enhances human roles rather than replacing them. AI agents will autonomously manage workflows in claims processing, risk assessment, and beyond. Training data is crucial, with Large Action Models (LAMs) needing high-fidelity visual data based on enterprise workflows to train effectively. Skan AI’s Process Intelligence platform bridges the gap by observing work across applications, capturing decision points, and mapping workflows to create a Digital Twin of Operations, essential for training LAMs.
Key Takeaways:
"The key to unlocking agentic AI’s full potential lies in comprehensive process observation. By meticulously capturing every worker’s activities—whether manual or automated—organizations can create AI systems that understand context, recognize inefficiencies, and proactively recommend optimizations."
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Autonomous Decision-Making
Agentic AI operates independently, making decisions and optimizing workflows without the need for explicit human prompting. -
Enhanced Human Roles
Rather than replacing human roles, agentic AI enhances them by managing repetitive and complex tasks autonomously. -
The Role of Process Intelligence
The success of agentic AI relies on a deep understanding of operational processes, captured through comprehensive process observation. -
Training with High-Fidelity Data
Effective training of AI models requires high-fidelity visual data based on real enterprise workflows. -
Digital Twin of Operations
Creating a Digital Twin of Operations is crucial for training AI models and ensuring they understand context, recognize inefficiencies, and recommend optimizations.
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