A Large Action Model for Enterprise Automation

In recent years, large language models (LLMs) have transformed how we interact with technology. Tools like ChatGPT, Claude, and Gemini have demonstrated how AI can understand and generate human-like language to support content creation, communication, and analysis at scale.

LLMs are remarkable pattern recognizers and language generators, but they remain passive. They wait for prompts and produce output, but they can’t independently complete tasks inside software applications. They can tell you how to process invoices, but they can’t do it for you.

These AI models were never built to automate work inside real-world business tools, so frustrated development teams often need to step in and provide costly intervention. The result is low user adoption and failure to deliver ROI.

That’s why a new generation of enterprise AI models is emerging: Large Action Models (LAMs).

While LLMs and LAMs share some architectural DNA, their core purposes diverge significantly:

LLMs are trained on massive datasets of human-written material. Based on what they’ve learned, they can create text, answer questions, write code, and summarize content.

LAMs are trained to take action. They can perceive software interfaces, plan multi-step processes, interact with applications directly, and provide explanations for the steps they’ve taken. That means they can complete tasks autonomously, with minimal human intervention.

Think of a large language model as an expert advisor — someone you consult for answers or explanations. In contrast, a large action model is a reliable teammate who takes your instructions and meticulously executes them.

This transition from knowing to doing represents a fundamental leap in AI’s real-world usefulness.

Enterprise workflows are complex. They span multiple apps, require contextual understanding, and often demand real-time decision-making. LLMs excel at generating content and parsing language, but they fall short in executing actions across digital interfaces. And traditional training methods are expensive and time consuming, taking months to deploy a single automation project.

To close this gap, businesses need AI models that don't just understand intent but act on it. That’s the core promise of agentic AI — intelligent agents that can perceive, plan, and complete multi-step tasks autonomously.

Unlike LLMs retrofitted with agent-like capabilities, ActIO was designed from the ground up as a LAM. When comparing LAM vs. LLM capabilities, this architectural distinction is the key to success with AI agents.

LAMs are trained not just on language, but on action data. They learn how users interact with software, and use this to power real-time AI task automation in business-critical systems. These tools can unlock a new level of speed, scale, and reliability for digital transformation initiatives.

Orby’s Large Action Model, ActIO, represents a new class of enterprise AI models. This LAM is purpose-built to move beyond language to enterprise-grade automation. Using computer vision, language, planning, and action modeling together, it powers AI agents for business that can:

Visually navigate interfaces, including buttons, fields, and dropdowns, using a system called UGround, developed in collaboration with Ohio State University

Interpret user intent from natural language prompts

Adapt to new applications without custom coding, thanks to Orby’s proprietary SAIL (Self-Adaptive Interface Learning) framework

Plan and execute tasks across software environments using a hierarchical agent architecture — a planner agent interprets the goals and delegates tasks, while a grounder agent completes the tasks and reports progress

In short, this system allows ActIO to think like a strategist and act like a skilled assistant, making it ideal for automating complex enterprise tasks that involve unstructured data, evolving GUIs, and human-like judgment.

Rather than simply adding automation to LLMs, Orby designed ActIO from the ground up as a Large Action Model. It was created with action, rather than language, as its native domain.

ActIO’s architecture reflects this vision:

Visual Grounding (UGround): Locates and interacts with UI elements based on visual input rather than brittle code, making it resilient to layout changes

Planner-Grounder Framework: Separates high-level task planning from execution, improving accuracy for multi-step processes

Task Modeling: Learns workflows from user demonstrations or instructions, enabling low-code automation across platforms

Performance Benchmarks: ActIO outperforms GPT-4o, Gemini 1.5 Pro, and other top models on key GUI benchmarks like MiniWoB, WebArena, and ScreenSpot

In short, Orby’s ActIO doesn’t just understand a task. It can actually do the work.

AI has come a long way, from generating content to executing real-world tasks. While Large Language Models changed how we communicate with information, Large Action Models like ActIO are changing how we get work done.

And this kind of powerful, agentic automation is no longer just for technical teams. The future of AI is accessible automation for complex tasks. Business users can describe what they want, and AI takes care of the rest, freeing up time and money for humans to do what matters most.

WANT TO UNDERSTAND THE SHIFT FROM CONTENT GENERATION TO INTELLIGENT ACTION? DOWNLOAD THE ORBY WHITE PAPER TO EXPLORE HOW LARGE ACTION MODELS ARE REDEFINING ENTERPRISE AI.