Automated Action Execution: Business Automation in Intelligent Enterprises

Unleashing Action: The Rise of Automated Execution

In the relentless pursuit of efficiency, businesses have long embraced automation. But today’s leaders face a stark reality: traditional automation is no longer enough. Intelligent Business Automation offers significant advancement by integrating Data Fabric Technology, Agentic AI, and Generative AI through an Intelligent Apps Platform. This evolution enables organizations to shift from reactive operations to more proactive and adaptive systems. Rather than being a simple enhancement, it represents a rethinking of how businesses process information, learn from outcomes, and adjust to changing demands. As we stand at this inflection point, the question for executives isn’t whether to adopt these technologies, but how quickly they can harness them to stay ahead. The age of the truly intelligent enterprise is here – and it’s rewriting the rules of business as we know them.

Large Language Models (LLMs) are a key component of this shift, processing and generating language by interpreting vast amounts of data and producing human-like responses. However, while LLMs excel at interpreting and communicating insights, they face limitations when comes to translating their understanding into action. 

Large Action Models (LAMs) expand on LLMs’ capabilities by converting insights into automated actions. While LLMs excel at data interpretation, LAMs execute specific tasks based on that understanding. These systems require minimal human input once configured, as they can drive real-time actions tailored to specific user contexts. When combined with Agentic AI, they can analyse situations and implement appropriate responses. For example, a LAM might not just identify a supply chain bottleneck but also initiate corrective measures based on predefined parameters, adjusting its response based on the specific facility, time of day, and available resources. This combination of understanding and action helps organizations respond to challenges more quickly and consistently.

The journey of Automation has gone through three major revolutions. Enterprises have progressed from basic task automation to Decision Intelligence enabled automation of cognitive workflows. Now, we are entering the era of fully intelligent enterprises that use LLMs, LAMs, and Agentic AI in Intelligent Business Automation. The ultimate goal of the evolution is to create enterprises that don’t just react to data but proactively shapes their future. 

So, what defines Intelligent Business Automation? It’s more than just advanced automation – it’s the convergence of cutting-edge technologies that drive Automated Action Execution for Intelligent Enterprises. At the heart of this transformation are four key enablers that form the foundation for Automated Action Execution driving Intelligent Enterprises to operate with precision, speed, and minimal human intervention.

The Key Enablers Driving Automated Action Execution

Understanding Intelligent Business Automation requires examining its core components and how they work together. Like systems in the human body, these technological enablers each serve specific functions while operating as part of a cohesive whole. Several key elements such as – Agentic AI, Decision Intelligence, and Self-Aware Data – form the foundation of Automated Action Execution.

• Agentic AI: Imagine the human body, where limbs execute actions guided by signals from the brain, nerves, and spinal cord. In this analogy, LLMs (Large Language Models) act like the vocal cords, enabling communication by interpreting and generating human-like language. However, they lack the ability to act autonomously. LAMs (Large Action Models), akin to the spinal cord, complement LLMs by not only understanding data but also executing real-time actions that drive business outcomes. Together, they form Agentic AI, which integrates LLMs for communication and LAMs for action. This enables enterprises to take hyper-contextualized, autonomous actions. Agentic AI acts independently, learning from its environment and making decisions autonomously to manage workflows from start to finish. With its combined foundation of LAMs and LLMs, Agentic AI can initiate and execute actions using real-time data and a deep contextual understanding.

• Decision Intelligence: Decision Intelligence serves as the brain, synthesizing data, AI, and human judgment to make informed decisions. By combining machine learning with structured decision frameworks, Decision Intelligence ensures that data-driven decisions are not only swift but also precise. In business scenarios such as supply chain management or customer service, it brings together the strategic thinking of humans with the scale and speed of AI. It can evaluate the complexity of tasks, adjust decision-making strategies dynamically, and execute decisions with minimal intervention.

• Self-Aware Data: Self-Aware Data, is like the nervous system, actively participating in workflows by contextualizing data and providing relevant insights. It refers to data enhanced with metadata, context, and intelligence, allowing it to actively participate in decision-making processes. Unlike traditional data, which is passive and requires manual manipulation for insights, Self-Aware Data is enriched with real-time contextual layers that empower it to become an active component in workflows. For instance, in claims processing, Self-Aware Data can recognize the type of incident, assess relevant historical data, and provide insights into potential outcomes. This data doesn’t just sit idle but interacts with automation systems, informing them of the best course of action.

• Other Intelligent Automation Technologies: Technologies like Intelligent Virtual Assistants (IVA), Intelligent Document Processing (IDP), Robotic Process Automation (RPA), and Low-Code Platforms operate as the muscles, carrying out repetitive tasks to enable seamless automation. They work in tandem with the nerves (Self-Aware Data) and brain (Decision Intelligence) to ensure a smooth and automated process. These tools take on the executional heavy-lifting—whether it’s processing large volumes of unstructured data through IDP, automating routine workflows with RPA, or enabling rapid prototyping of automation through Low-Code platforms. For example, in claims processing, RPA can be deployed to gather data, while IDP extracts and organizes information from scanned documents, creating a streamlined pipeline for faster, error-free decision-making.

Together, these enablers work holistically as the Intelligent Apps Platform with each enabler plays a specific role in automating tasks like communication, scheduling, and decision-making, transforming traditional workflows into intelligent, action-driven processes. The synergy between these technologies facilitates a fully autonomous action-driven ecosystem, where decision-making and task execution are tightly integrated. As a result, Intelligent Business Automation doesn’t just react to events but anticipates and acts upon them, moving businesses from a reactive to a proactive operational model.

By looking at these enablers working in tandem in a real-life workflow, we can better appreciate how they transform traditional workflows into intelligent workflows that self-execute flawlessly.

Automated Action Execution in Practice

Adopting Automated Action Execution presents a set of modern challenges, including ensuring system adaptability to rapidly changing data environments, managing the cross-functional integration of diverse automation tools, and addressing AI ethics in decision-making. Successfully overcoming these obstacles requires dynamic platforms that evolve with business needs, real-time data synchronization, and well-defined AI governance frameworks. These elements work together to streamline the adoption process, making it smoother and more effective.

To illustrate the significance and impact of this technology, consider the following use case from the auto-insurance sector, specifically in claims processing.

In the Claim Intake and Registration phase, key technologies like Agentic AI, Self-Aware Data, and Decision Intelligence greatly enhance efficiency and accuracy.

• Agentic AI improves communication by automatically notifying the claimant via email once the claim is registered, boosting customer experience.
• Self-Aware Data adds contextual awareness, providing real-time information on claim details such as claimant history and the nature of the claim. It also uses active metadata management to extract details from the incident report, including the vehicle type and make involved.
• Decision Intelligence combines AI and data analytics to guide accurate initial claim evaluations, ensuring swift and precise processing from the start.

Together, these technologies streamline the claim intake process, laying a strong foundation for the next steps.

Similarly, in the Initial Assessment stage of claims processing, the trio of Agentic AI, Decision Intelligence, and other Intelligent Automation technologies plays a crucial role in streamlining the evaluation process.

• Agentic AI facilitates nuanced decision-making by analyzing complex factors such as vehicle damage, verification results, and incident reports, ensuring that assessments are accurate and aligned with the specifics of each case.
• Decision Intelligence further enhances this process by integrating advanced analytics and decision-making frameworks, which help automate the determination of claim complexity and appropriate actions.
• Additional Intelligent Automation technologies, such as Intelligent Document Processing (IDP) and Robotic Process Automation (RPA), support these capabilities by efficiently managing data and executing repetitive tasks, ensuring that the assessment process is both rapid and error-free.

In the Investigation and Documentation phase,

• Agentic AI and Intelligent Document Processing (IDP) collaborate to gather critical evidence autonomously. Agentic AI analyzes incident reports, images, and witness statements, ensuring that all relevant information is captured without human intervention.
• Intelligent Document Processing (IDP) further enhances this by extracting data from scanned documents and structuring it for analysis.

This integrated approach accelerates the documentation process, reducing delays and manual errors, allowing claims to progress seamlessly to the next stage.

During Loss Evaluation and Estimation,

• Agentic AI takes charge of evaluating losses by analyzing real-time data from various sources, including repair estimates and market trends. It leverages advanced decision-making frameworks to produce accurate loss estimates, ensuring transparency and fairness.
• Decision Intelligence supports this process by offering predictive insights based on historical data, refining estimations, and recommending optimal settlement amounts.

The combined power of these technologies allows enterprises to reduce time spent on evaluations while maintaining precision.

As the claim moves into the Reserve Setting and Negotiation stage,

• Agentic AI autonomously sets reserves by analyzing the complexity of each claim, factoring in potential costs and legal considerations. During negotiations, Agentic AI employs predefined business rules to initiate discussions and settlements with claimants or third parties.
• Decision Intelligence complements this by adjusting strategies dynamically based on negotiation outcomes, ensuring that settlements are both fair and cost-effective.

In the final stage, Payment and Closure,

• Agentic AI automates the entire payment process. It verifies all required documentation and triggers payments, ensuring accuracy and compliance with organizational policies.
• Self-Aware Data adds another layer of security by cross-referencing real-time data to confirm all conditions are met before the release of funds.

• Self-Aware Data adds another layer of security by cross-referencing real-time data to confirm all conditions are met before the release of funds.

This streamlined process not only improves payment speed but also enhances claimant satisfaction by closing claims efficiently.

Intelligent Business Automation is powered by the key enablers mentioned earlier, ensuring accuracy, speed, and adaptability in business operations. It is crucial that enterprises explore and adopt solutions that provide all these enablers in a cohesive manner, such as the Intelligent Apps Platform.

The Road Ahead: Intelligent Enterprises

As enterprises continue to evolve, more businesses will adopt Automated Action Execution across various areas. By leveraging the key enablers within the comprehensive framework of the Intelligent Apps Platform, organizations can automate entire workflows, from marketing strategies to customer service.

The future lies in autonomic systems that not only think but also act as self-sustaining, adaptive systems that drive growth and success autonomously. As these technologies mature, Automated Action Execution will become a cornerstone of the Intelligent Enterprise. Imagine thought-based interactions and purchases via metaverse platforms, with AI advisors offering real-time, hyper-personalized recommendations. Now is the time for businesses to embrace these advancements and achieve new levels of efficiency and innovation, gaining a crucial competitive edge in today’s market.