What are Large Action Models (LAMs)?

Large Language Models (LLMs) have been referred to as ‘one of the most disruptive technologies of the century,’ yet Large Action Models (LAMs) represent the next evolution. 

LAMs don’t just process data—they act. They control devices, retrieve information, and automate decisions, making AI a proactive force ideal for enterprise use. 

In this article, we’ll explore what a Large Action Model is, its benefits, and what to consider before implementation. 

What are large action models (LAMs)? 

A large action model (LAM) is an AI system that understands queries and executes actions. It builds on large language models (LLMs), which generate text using Natural Language Processing (NLP). 

Unlike LLMs, which are primarily limited to content generation, LAMs extend beyond text-based outputs to actively perform tasks. These tasks include dynamically adjusting responses based on new inputs, automating workflows, and optimizing decision-making processes.

At the core of every advanced AI agent is a well-designed LAM—capable of planning, reasoning, and executing actions efficiently.

How do large action models work? 

LAMs combine neural networks, which detect patterns in data, with symbolic AI, which follows logical rules to make decisions. This hybrid approach enables them to analyze and act, following three core steps that distinguish them from traditional AI models.

Step 1: Understand the goal 

Unlike traditional language models that primarily generate text or provide information, LAMs are designed to perform actions. This action-oriented structure allows them to interact with and influence their environment in ways standard AI models cannot.

To develop this capability, LAMs learn from large datasets, analyzing how humans complete multi-step tasks. For example, a customer service automation LAM might study thousands of agent interactions before autonomously handling requests, mimicking human decision-making.

Step 2: Apply contextual understanding

LAMs are equipped with context awareness, allowing them to take relevant and adaptive actions based on real-time conditions.

For example, a rule-based AI system might automatically lower prices when stock levels are high, but it lacks adaptability. A LAM-powered pricing system, however, could detect sudden demand surges, analyze competitor prices, and dynamically adjust pricing in real-time to optimize revenue.

Step 3: Reach a goal or objective

LAMs are goal-driven, meaning they take proactive steps to complete tasks, solve problems, or optimize processes.

In healthcare, for instance, a LAM designed for patient monitoring could detect early signs of deterioration, such as patterns consistent with respiratory failure. It could then cross-reference a patient’s medical history, current medications, and similar past cases to recommend the most effective intervention.

LAMs vs. LLMs: What are the differences?

Large Action Models (LAMs) and Large Language Models (LLMs) share foundational AI principles, but they serve distinct roles. LLMs specialize in language generation and understanding, while LAMs extend beyond text to automate and execute tasks, making them a critical advancement in enterprise AI. Here’s how they compare:

Understanding 

Large Language Models (LLMs) excel at processing and generating human-like text using Natural Language Processing (NLP). However, they are inherently passive AI systems—limited to responding with words rather than taking direct action.

LLMs are highly effective for tasks such as:

• Content creation (e.g., writing articles, summarizing reports)
• Sentiment analysis (e.g., analyzing customer feedback)
• Conversational AI (e.g., chatbots, virtual assistants)

While LLMs provide insights, predictions, and structured responses, they do not act independently—they rely on human intervention to apply their outputs.

Action 

LAMs are designed to bridge the gap between AI-driven insights and real-world execution, enabling enterprises to automate complex, multi-step workflows without human intervention.

LAMs build upon LLMs by adding action-oriented capabilities. While LLMs interpret and generate text, LAMs translate that understanding into decision-making and task execution. They incorporate additional layers, such as neuro-symbolic AI, to handle reasoning, planning, and automation. 

LAMs move beyond language generation and actively perform complex digital tasks, such as autonomous supply chain adjustments, dynamic pricing strategies, and AI-driven customer interactions.

Applications of LAMs in the enterprise 

The potential applications of LAMs are vast, spanning multiple industries and redefining how businesses operate. By combining advanced language understanding with the ability to take concrete actions, LAMs are driving automation, enhancing decision-making, and optimizing workflows across various sectors.

Automating enterprise workflows

One of the most impactful applications of LAMs is automating repetitive and complex tasks with precision and efficiency. Here are some key examples:

• Marketing automation: LAMs enable real-time personalization by dynamically adjusting campaigns. For instance, an eCommerce company could use LAMs to track customer engagement, modify email sequences, and apply targeted discounts based on live buying activity—enhancing customer experiences and maximizing sales.
• Customer service optimization: In telecom, an LAM could autonomously resolve service disruptions, schedule technician visits, and adjust billing plans, eliminating the need for manual intervention while improving response times.
• Sales and lead management: LAMs streamline sales processes by automating lead qualification and follow-ups. A real estate agency could deploy one to analyze inquiries, assess buyer preferences, and schedule property viewings, ensuring seamless customer engagement.
• Supply chain efficiency: Retailers can leverage LAMs to analyze demand trends, optimize inventory orders, and automate supplier shipments. By anticipating demand surges, they can help prevent costly stockouts and ensure timely restocking.

Enhancing decision-making and risk management

LAMs go beyond automation by improving data-driven decision-making, particularly in industries where accuracy and speed are critical:

• Financial operations: A finance-trained LAM could review expense reports, flag inconsistencies, and automate reimbursements—reducing human error and expediting financial approvals. In hedge funds, LAMs can monitor stock movements, assess risk levels, and adjust investment portfolios based on pre-set parameters.
• Healthcare insights: LAMs can revolutionize patient care by tracking vitals, detecting early signs of deterioration, and proactively adjusting medication dosages or alerting medical staff to intervene—helping prevent critical health incidents.
• Manufacturing and industrial automation: In smart factories, LAMs analyze machine performance, detect anomalies, and schedule predictive maintenance—preventing costly breakdowns and downtime.

What are the challenges of LAMs? 

While the potential of LAMs is immense, their development and deployment also come with significant challenges that need to be addressed:

• Training limitations and cost: LAMs require extensive exposure to real-world data to achieve contextual accuracy. This demands significant computational resources, making their development costly and time-intensive.
• Security and compliance risks: Despite rigorous training, LAMs introduce security vulnerabilities when taking action. A Deloitte report found that 55% of organizations avoid certain AI use cases due to concerns around data privacy and security. Misuse or compliance violations can lead to regulatory breaches and reputational damage.
• High-stakes consequences: In industries such as finance and healthcare, a single miscalculation by a LAM could cause severe financial losses, regulatory violations, or even life-threatening errors. Enterprises must implement strict governance frameworks to ensure human oversight and mitigate risks.
• Potential for misuse: Even in general enterprise applications, LAMs can lead to unintended negative consequences if not properly managed. To address these challenges, organizations must adopt strong policies, enforce compliance measures, and prioritize responsible AI development.

How will LAMs impact the enterprise?

The future is poised for LAMs to collaborate with humans and other AI systems to create fully autonomous AI-driven operations that handle entire processes. 

For enterprises seeking advanced improvements in output and efficiency, LAMs represent a fundamental shift—moving beyond traditional automation and LLMs to create AI agents capable of continuous learning, adaptation, and independent action. 

For enterprises, LAMs could redefine AI from being used as isolated assistants to collaborative teams, where each LAM would specialize in a specific task as a central LAM oversees workflows. 

Realizing this vision presents many challenges. LAM adoption isn’t a plug-and-play process, and strategic adoption requires a substantial digital transformation and associated challenges, which won’t be attainable for everyone. 

However, enterprises that move early may gain a competitive edge, leveraging LAMs to drive efficiency, automation, and customer value and allowing human teams to focus on creativity and strategy.

LAMs: Pioneering the Future of AI-Driven Execution

The vision of accurate machine intelligence is rapidly transitioning from theory to reality, with LAMs at the forefront of this transformation. 

Unlike previous AI advancements focused on advanced data analysis and content generation, LAMs introduce a new era of AI-driven execution, where systems autonomously complete tasks, optimize workflows, and make complex decisions in real-time.

This evolution will redefine how industries approach operational efficiency, scaling AI from a supportive tool to a strategic driver of innovation.

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