A top-rated healthcare provider began facing a critical challenge: their patient inquiry volume was skyrocketing, straining their customer service team and impacting response times.

The company needed a solution that could automate routine inquiries and free up staff for more complex issues while seamlessly integrating into their existing support environment.

What was this solution?

They adopted intelligent AI agents. These are smart AI workers built to help companies transition to Agentic Process Automation, an era-defining evolution of RPA, Robotic Process Automation.

Having said that, let’s look at what they managed to achieve with-in the first 6 weeks of adopting Beam’s AI Agents.

Suffice it to say, this is one of a number of case-studies we’ve garnered over the last year while helping startups and Fortune 500 companies begin adopting AI for process automation.

However, there is a lot about APA that business leaders need to educate themselves on before they begin contemplating possible adoption circumstances.

Agentic Process Automation: Under The Hood

In simple terms, APA means using AI-powered software agents that can autonomously perform tasks, make decisions, and adapt to changing circumstances.

Moreover, unlike traditional automation tools, these agents possess a degree of intelligence and autonomy that allows them to navigate complex scenarios, learn from experiences, and even collaborate with human workers.

At its core, an agentic system combines several key technologies:

• Artificial Intelligence in the form of Large Language Models for task execution and decision-making.

• Tools in form of API integrations for interacting with various 3rd party software systems.

These components work together, creating a system that can understand context, set goals, and take appropriate actions to achieve those goals.

What Other Automation Systems Exist?

Although APA can be seen as the most sophisticated method for process automation, it pays dividends to understand how we got here, starting with:

1. Rule-based Automation

Rule-based automation or Robotic Process Automation (RPA) follows pre-defined, static rules to perform tasks.

While this is effective for simple, repetitive processes, it has otherwise significant limitations:

• It's confined to structured data and predictable scenarios.

• Any changes in the process require manual updates to the rules.

• It struggles with exceptions or unusual situations.

Examples

For example, a rule-based chatbot might use keyword matching to provide customer support, but it would fail to understand context or handle complex queries.

In contrast, agentic automation can handle unstructured data and adapt to new scenarios without manual intervention. It can understand the intent behind a customer's query and provide more accurate, contextual responses.

2. AI-assisted Automation

AI-assisted automation represents a step forward, using artificial intelligence for specific tasks within a larger process. However, it still has limitations:

• It often requires human intervention for complex decision-making.

• While it can handle more varied situations than rule-based systems, it may struggle with highly variable or novel scenarios.

Examples

An example of AI-assisted automation might be a system that uses machine learning to categorize customer inquiries but routes complex issues to human agents.

Agentic process automation takes this a step further. It can manage end-to-end processes, making complex decisions autonomously and only involving humans when necessary. In the customer service example, an agentic system could not only categorize inquiries but also resolve complex issues, learn from these interactions, and continuously improve its performance.

What Makes Agentic Automation Better?

There are some key elements of APA that make it stand out and help it differ from RPA and other traditional systems:

• Autonomy: AI agents make decisions and take actions based on their understanding of the business context and objectives. Additionally, unlike traditional systems that require constant human oversight, agentic systems can operate independently.

• Adaptability: APA systems give agents the ability to learn from new situations and adjust their behavior accordingly. This adaptability ensures that the system remains effective even as business conditions change.

• Goal-oriented: Agentic systems understand and work towards specific objectives rather than simply following a set of predefined rules. This allows them to prioritize tasks and make decisions that align with broader business goals.

• Contextual understanding: Agentic automation can interpret and respond to the broader context of a situation, not just isolated data points. This enables more nuanced and appropriate responses to complex business scenarios.

• Proactivity: Unlike reactive traditional systems, agentic automation can anticipate needs and initiate actions. This proactive approach can help cover edge case through human feedback in uncertain scenarios.

How Does Beam Use APA To Help Optimize Business Processes?

At Beam AI, we combine the best of both worlds: The adaptability of agentic process automation with the determinism and reliability of current RPA systems. This approach focuses on developing highly adaptable, context-aware agents that can seamlessly integrate with existing business processes while continuously learning and improving.

Beam AI's agents are designed to not just automate tasks, but to become valuable, AI-powered team members that contribute to strategic decision-making and drive business growth.

To put it succinctly, here is what Beam’s clients can experience with their new AI workforce:

• AI agents learn from humans how to execute specific tasks: Our agents understand human workflows, enabling them to replicate tasks accurately and efficiently.

• AI agents choose the appropriate workflow based on the request and task: Depending on the nature of the request, our AI agents select the most suitable workflow to ensure optimal execution. It will only choose between workflows that you trained it on.

• Workflows ensure similar requests are processed consistently: By following established workflows, our AI agents maintain consistency and reliability in handling similar requests. Deterministic results can be achieved.

• Agents seek clarification or additional information when necessary: If required, our agents will ask for additional information or clarification to ensure tasks are completed correctly.

• Critical steps require human approval: For key steps in the process, our agents seek human approval to ensure accuracy and compliance.

• Humans can give feedback on task results: This will help the AI agent to continuously improve its results over time.

Potential Impact on Workforce and Job Market

With a technology as revolutionary as APA and AI agents, we do predict a drastic restructuring of the professional world:

1. Job Transformation:

   • Many roles will be redefined as routine tasks are automated, with human workers focusing more on strategic, creative, and interpersonal aspects of their jobs.

   • New roles will emerge, such as AI trainers, ethics officers, and human-AI collaboration specialists.

2. Skill Demands:

   • There will be increased demand for skills in areas like data analysis, AI management, and complex problem-solving.

   • Soft skills like emotional intelligence, creativity, and adaptability will become even more valuable.

3. Education and Training:

   • Educational systems will need to evolve to prepare workers for a world where collaboration with AI is the norm.

   • Continuous learning and re-skilling will become essential for career longevity.

At Beam AI, we think that businesses of the future will be agentic. We’re just helping them get there.