Large Action Models (LAM)

What is a Large Action Model?

A Large Action Model (LAM) is a next-generation enterprise AI system designed not just to process language, but to take action inside real software environments. Unlike large language models (LLMs), which specialize in generating text, LAMs are built to perceive, plan, and execute tasks autonomously.

Think of it this way: where an LLM is like an expert consultant providing detailed instructions, a LAM acts as a digital teammate that actually performs the work. It can log into applications, navigate interfaces, reconcile data, generate reports, and more — all without human micromanagement.

Large Action Models vs. Large Language Models

The Role of Large Language Models (LLMs)

LLMs such as ChatGPT and Claude excel at understanding and generating human-like text. They’re great at brainstorming, writing, summarizing, and answering questions. However, they remain passive tools — they produce instructions but cannot directly execute them in enterprise systems

The leap forward with LAMs

LAMs move AI from language to action. They don’t just explain how to complete a task — they complete it. Trained on both action data and user interactions, LAMs are capable of multi-step task automation, visual navigation across interfaces, and adaptive execution.

This shift represents the difference between:

How Large Action Models work

Large Action Models follow an adaptive process that can learn from interaction and contextual awareness and make decisions autonomously. The basic steps are:

Planning and execution framework

LAMs rely on a planner-grounder architecture. A planner agent interprets the user’s intent, creates a structured plan, and delegates execution to a grounder agent. The grounder then carries out actions step by step, reporting progress and handling exceptions

Visual grounding with computer vision

LAMs like Uniphore’s ActIO use visual grounding (UGround), enabling them to interpret graphical user interfaces (GUIs) like a human. Rather than relying on brittle backend code, they identify and interact with on-screen buttons, dropdowns, and fields. This makes them resilient to layout changes

Adaptive learning with SAIL

A key innovation is Self-Adaptive Interface Learning (SAIL), which allows LAMs to adapt to entirely new applications without manual coding. They can learn from user demonstrations or natural language instructions, accelerating automation from idea to execution

Contextual awareness

Beyond raw action-taking, LAMs bring contextual understanding. They can interpret documents, analyze screen layouts, and make real-time decisions. This contextual awareness ensures that automation is not just fast, but also intelligent and compliant.

Key capabilities of Large Action Models

LAMs unlock enterprise automation capabilities that go far beyond what LLMs or traditional RPA (Robotic Process Automation) can deliver:

Autonomous task execution – Completing work across applications from start to finish.
Multi-step process planning – Breaking down goals into structured, trackable subtasks.
Visual navigation – Interpreting and interacting with dynamic GUIs using computer vision.
Self-adaptation – Learning new systems without brittle, hard-coded scripts.
Explainability – Providing clear reports on actions taken, supporting compliance and transparency.

These features make LAMs foundational to the future of intelligent process automation.

Enterprise use cases for Large Action Models

With the power to act , Large Action Models can intelligently drive multiple enterprise AI applications. Popular LAM use cases include:

Invoice processing and reconciliation

LAMs can log into financial systems, extract invoice data, reconcile it with purchase orders, and flag discrepancies. Enterprises report up to 90% accuracy improvement and 60% time reduction.

Report generation and data entry

Instead of employees manually creating weekly sales reports or entering repetitive data, LAMs generate and distribute reports automatically. Companies save 50–80% of time on routine reporting.

Employee onboarding and HR automation

LAMs streamline onboarding by updating records, validating credentials, and ensuring compliance across multiple systems, reducing manual workload by up to 60%.

Cross-application workflows

From IT provisioning to procurement, LAMs orchestrate workflows across evolving digital tools, providing resilience and adaptability where traditional RPA often fails.

Benefits of Large Action Models for businesses

Adopting LAMs delivers measurable benefits for enterprise AI strategies:

Productivity gains: Automations cut task time by up to 80%
ROI impact: Enterprises report $2–4M in average ROI within the first year of deployment
Scalability: Automations adapt as systems evolve, without re-coding
Accuracy: LAMs reduce errors by 40–90% in data-heavy workflows
Employee experience: Teams are freed from repetitive tasks to focus on strategy, creativity, and customer engagement

The future of Large Action Models

LAMs represent the next leap in enterprise AI. As organizations seek agentic AI that can not only think but act, LAMs will become central to digital transformation strategies. Future developments will focus on:

This evolution marks the shift from AI as a tool to AI as a collaborator — one that scales enterprise productivity without sacrificing control.

Conclusion

Large Action Models are redefining what’s possible in enterprise automation. By combining planning, perception, and execution, LAMs transform AI from passive content generators into active digital teammates that deliver real business value.

As organizations face mounting pressure to increase efficiency and reduce costs, LAMs stand out as the foundation for scalable, intelligent automation. They’re not just the next step in AI — they’re the future of how work gets done.

Ready to explore how enterprise AI can transform your workflows? Learn more about Uniphore.

Interested in expanding your knowledge? Browse our more glossary terms.