February 27

Large Action Models (LAMs): The End of the Dashboard?

Driving data-driven decisions through dashboards have long been the pinnacle of a successful modern data strategy, providing a visual interface for complex data. Their primary role is to aggregate and distill data into understandable and actionable information presented in an easily digestible format. This simplification is crucial in aiding decision-makers to grasp patterns and trends quickly. 

However, as I explored in my book “Delivering Data Analytics,” dashboards are more than just platforms for displaying information; they are vehicles for action. The traditional process involves using these visualizations to interpret data and subsequently make decisions. While effective, this process has inherent limitations, including potential time delays and the risk of not taking the requisite action. And, more often than not, dashboard adoption and utilization is low.

This leads us to an intriguing question: what if we could bypass the traditional dashboard, moving directly to action? Such a shift could dramatically streamline decision-making processes, reducing time delays and leveraging the full potential of real-time data analytics. And, going even further, what if it is not us taking the action?

Enter the Large Action Model.

Introduction to Large Action Models (LAMs) and Why Your Data Strategy Needs to Include Them

In the landscape of artificial intelligence, Large Action Models (LAMs) are emerging as groundbreaking tools, especially in their application to business intelligence and analytics. To understand LAMs, it’s helpful to start with a familiar concept: Large Language Models (LLMs), like ChatGPT.

LLMs specialize in understanding and generating human-like text, interpreting queries, and providing relevant, contextually aware responses. However, they primarily focus on information processing and generation, rather than direct action.

LAMs, on the other hand, take this a step further. They’re not just about understanding or generating language; they’re about translating understanding into concrete actions. This involves a combination of advanced neural network capabilities with symbolic reasoning. The outcome is a system that can not only process and interpret data but can also autonomously execute tasks based on this data.

This capability marks a significant evolution in AI. While LLMs like ChatGPT excel at providing information or suggestions, LAMs actively engage in performing tasks – from simple actions like navigating a webpage to more complex operations like managing data across various applications.

Example in a Cellphone Context: The Rabbit R1

A prime example of a LAM in action is the Rabbit R1, a device that exemplifies the practical application of LAM technology. Unlike traditional smartphone apps that require user navigation and input, the Rabbit R1 simplifies tasks through direct action.

For instance, instead of manually searching and booking a flight through an app, Rabbit R1 can understand a user’s request to book a flight and autonomously navigate through the booking process, considering user preferences and requirements. This not only saves time but also minimizes the need for human interaction with the application, streamlining the entire process.

The Rabbit R1, and LAMs in general, represent a significant shift in how we interact with technology. They offer a glimpse into a future where the line between command and action is increasingly blurred, making our interactions with digital services more efficient and intuitive.

Learn more about LAMs over at Dataconomy.

LAMs and Their Importance in Your Data Strategy

The example of the Rabbit R1 demonstrates the practicality of Large Action Models (LAMs) in everyday tasks, such as booking flights through a cell phone. This transition from traditional methods to LAM-enhanced approaches in personal technology is a precursor to similar transformations in the realm of business intelligence and analytics.

Potential of LAMs in Business Intelligence and Analytics

  1. Automated Decision-Making: Just as the Rabbit R1 can autonomously perform tasks based on user requests, LAMs can be applied to the business to automate decision-making processes. They can analyze real-time data, identify critical insights, and make informed decisions without human intervention.
  2. Real-Time Action Based on Data Insights: In a business context, LAMs can dynamically respond to changing data. For example, in response to market trends or customer behavior analytics, a LAM could automatically adjust marketing strategies or product pricing, effectively acting on the insights derived from the data.
  3. Enhanced Efficiency and Accuracy: LAMs offer the potential to greatly enhance both the efficiency and accuracy of business operations. By eliminating the time lag inherent in human analysis and decision-making, LAMs ensure that businesses can respond to market changes swiftly and effectively.
  4. Customization and Learning: Similar to how the Rabbit R1 learns from user preferences and behaviors, LAMs in business dashboards can be customized to align with specific business goals and strategies. Over time, they can learn from outcomes and refine their decision-making algorithms.
  5. Broader Application Scope: The application of LAMs extends beyond simple data interpretation to complex operations management, customer relationship management, and even predictive analytics. This broad scope highlights the potential of LAMs to revolutionize various facets of business intelligence.
  6. Challenges and Considerations: While LAMs promise significant advancements, their integration into existing business systems poses challenges, including data privacy concerns, the need for robust training data, and ensuring alignment with business ethics and regulations.

By automating decision-making processes and directly acting upon data insights, LAMs stand to revolutionize the way businesses utilize data, leading to more responsive, efficient, and intelligent operations.

Applying Large Action Models (LAMs) to Dashboards: Example Use Case

To illustrate how Large Action Models (LAMs) can revolutionize the traditional dashboard, let’s consider an example use case in a business context. This example will demonstrate how LAMs can reimagine the way businesses interact with data and execute decisions.

Example Use Case: Sales Performance Management

Scenario: A company uses a dashboard to monitor its sales performance, which includes metrics like sales volume, customer engagement, and market trends.

Traditional Approach: In the traditional setup, the sales team regularly reviews the dashboard to understand current performance, identify trends, and make decisions based on this data. For instance, if the dashboard indicates a decline in sales in a particular region, the team might devise strategies to boost sales, such as launching a marketing campaign or offering discounts. This process involves several steps – from data interpretation to strategy formulation and execution, all requiring significant human intervention and time.

LAM-Enhanced Approach: With a LAM integrated into the system, the process becomes more streamlined and proactive.

  1. Automated Analysis and Decision Making: The LAM continuously analyzes the sales data in real-time. When it identifies a significant trend, such as the decline in sales in a specific region, it doesn’t just highlight this trend; it also starts formulating potential responses.
  2. Direct Action Execution: Based on pre-set parameters and learned patterns, the LAM can initiate actions without waiting for manual intervention. For instance, it might automatically start a targeted promotional campaign in the region experiencing the sales dip or adjust pricing dynamically.
  3. Feedback and Learning Loop: The actions taken by the LAM and their outcomes are fed back into the system, allowing the LAM to learn and refine its decision-making process for future scenarios.

Modern Data Strategy for Large Action Models

As we move to automated actions, the need for trusted data is even more imperative. A modern data strategy forms the foundation for successful LAM implementation, emphasizing the importance of data governance, integration, and accessibility:

  • Data Governance: Establish policies, standards, and processes to ensure data quality, privacy, and security throughout the LAM lifecycle.
  • Data Integration: Integrate diverse data sources and formats to provide comprehensive inputs for LAMs, enabling holistic analysis and action.
  • Data Accessibility: Facilitate seamless access to data across organizational silos, empowering LAMs to derive insights from disparate sources and drive informed decisions.

To help craft your custom data strategy that integrates AI, LLMs, and LAMs, consider our Data Strategy Workshop.

Large Action Models and the Fate of Dashboards

As LAM technology continues to advance, the role of traditional dashboards may indeed evolve. While dashboards provide valuable insights and visualizations, LAMs offer a more proactive and autonomous approach to decision-making. However, rather than rendering dashboards obsolete, LAMs may complement existing dashboard functionalities, enhancing real-time responsiveness and actionable intelligence.

Evolution of the BI Developer’s Role

The adoption of LAMs heralds a paradigm shift in the role of business intelligence (BI) developers, necessitating a focus on human factors and storytelling:

  • Human Factors: BI developers must understand user needs, preferences, and behaviors to design intuitive interfaces and interaction mechanisms for LAM-driven systems.
  • Decision Analysis: BI developers need to deeply understand not just the data but the decision making processes they support. They need to move higher up in the chain to both drive actions and understand out to provide the oversight required.
  • Storytelling: Beyond presenting data, BI developers should craft compelling narratives that contextualize LAM insights, guiding users through actionable insights and facilitating decision-making.

Make telling data stories easier with the Data Storytelling Cards.


In conclusion, the integration of Large Action Models (LAMs) represents a transformative leap in the field of business intelligence and analytics. By automating decision-making processes and directly acting upon data insights, LAMs enable businesses to unlock new levels of efficiency, agility, and intelligence. However, their successful implementation hinges on robust requirements gathering, meticulous planning, and a modern data strategy. As LAM technology continues to mature, businesses must adapt their workflows and roles to harness their full potential, ensuring continued innovation and competitive advantage in the digital age.


data strategy, data visualization

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