Using Gen AI for Business Automation: Removing the Limitations of Traditional Rule-Based Automation

In today’s fast-paced business environment, automation is essential for improving efficiency, accuracy, and decision-making. Traditionally, rule-based systems like JBoss Drools have played a significant role in automating repetitive and structured tasks. However, as business processes grow more complex, these systems face limitations in handling diverse, unstructured, or evolving scenarios. This is where Generative AI (Gen AI) and Large Language Models (LLMs) come in, offering capabilities that not only complement rule-based automation but also address its inherent limitations. In this blog, we explore how Gen AI and LLMs can revolutionize business automation by overcoming the constraints of traditional rule-based systems and transforming them when challenged.

What are Rule-Based Systems?

Rule-based systems, such as JBoss Drools, operate on predefined sets of human-made rules, often developed by domain experts. These systems follow explicit “if-then” logic to reach decisions, making them ideal for environments where structured data and fixed rules dominate, such as financial services, fraud detection, and healthcare. By embedding business logic into rules, these systems ensure transparent, interpretable, and consistent decision-making.

However, rule-based systems like Drools have several key challenges:

• Limited Flexibility: They are rigid and struggle in situations that fall outside their predefined rules. If exceptions or unanticipated variables arise, these systems often fail to provide accurate results.
• Scalability Issues: As businesses grow and become more complex, the number of rules increases, making it harder to manage, maintain, and update these systems.
• Inability to Handle Unstructured Data: Rule-based systems rely heavily on structured inputs, which means they perform poorly with unstructured data like natural language documents or customer inquiries.
• Outdated or Challenged Rules: Over time, business rules may become outdated due to evolving market conditions, regulatory changes, or new customer behaviours. Maintaining these systems manually can become cumbersome.

How JBoss Drools Works: A Simple Example

JBoss Drools is a powerful tool that automates decision-making based on predefined rules. Let’s consider a simple example of a loan approval system.

Drools Rule Example: Loan Approval System

Here’s a simple Drools rule written in Drools Rule Language (DRL) to automate a loan approval process:

package com.example.rules;

rule "Approve Loan"
    when
        $applicant: Applicant(creditScore > 700, income > 50000)
        $loan: Loan(amount < 100000)
    then
        $loan.setApproved(true);
        System.out.println("Loan approved for applicant: " + $applicant.getName());
end

rule "Deny Loan"
    when
        $applicant: Applicant(creditScore < 600 || income < 40000)
        $loan: Loan(amount > 150000)
    then
        $loan.setApproved(false);
        System.out.println("Loan denied for applicant: " + $applicant.getName());
end        

In this example:

• The first rule approves loans for applicants with a credit score > 700 and income > $50,000, and where the loan amount is less than $100,000.
• The second rule denies loans for applicants with a credit score < 600 or income below $40,000, or if the loan amount exceeds $150,000.

Limitations of Rule-Based Systems

While JBoss Drools provides clear, structured decision-making, it can struggle in more nuanced or complex scenarios. Let’s consider some limitations:

1. Handling Exceptions and Edge Cases: What happens if an applicant’s credit score is 650, which falls between approval and denial? Rule-based systems might struggle to handle such borderline cases unless explicitly programmed.
2. Unstructured Data: If an applicant provides additional documentation in the form of unstructured data (e.g., letters, PDF files, or scanned documents), Drools would require custom logic to extract useful data from these sources.
3. Dynamic Rules: If a company frequently updates its policies, updating Drools rules can become time-consuming. Every change would require a domain expert to manually adjust rules and redeploy the system.
4. Outdated Rules: Over time, certain rules may become obsolete or inefficient, leading to incorrect decisions if not regularly updated. The traditional rule-based approach requires human intervention to assess and modify these rules, which can be tedious.

Generative AI and Large Language Models (LLMs)

Generative AI is a subset of artificial intelligence that uses Large Language Models (LLMs) to generate human-like responses, analyze patterns in unstructured data, and automate decision-making processes. LLMs like ChatGPT, Gemini, and open-source models such as GPT-J and LLaMA have become powerful tools in automating tasks that go beyond the capabilities of traditional rule-based systems.

ChatGPT:

ChatGPT is an AI model developed by OpenAI, designed to generate text and interact in a conversational manner. It has been widely used in applications like customer support, content generation, and decision-making assistance. ChatGPT’s ability to handle complex language tasks and generate contextually relevant responses makes it an ideal complement to rule-based systems for handling unstructured data and ambiguous cases.

Google Gemini:

Google’s Gemini is a next-generation LLM aimed at solving more complex tasks by integrating multiple modalities, such as text and images. With powerful language understanding, it’s designed for use in advanced AI applications, and its scalability makes it suitable for enterprise-level automation.

Private and Open-Source LLMs:

For organizations that prefer more control and privacy, open-source LLMs like GPT-J, LLaMA, and Bloom can be deployed as private instances within secure environments. These models offer flexibility and customization while allowing businesses to protect sensitive data. Deploying private LLMs in a self-hosted environment gives businesses the benefits of LLMs without exposing data to third-party providers.

How Generative AI and LLMs Complement Rule-Based Automation

LLMs bring several advantages to automation processes, especially when used alongside traditional rule-based systems like Drools. While rule-based systems provide deterministic, transparent decision-making, LLMs can:

• Handle Unstructured Data: LLMs excel at processing natural language inputs, making them ideal for tasks like extracting data from emails, documents, or customer inquiries.
• Adapt to Complex Scenarios: Generative AI models can evaluate situations where traditional rules fail, providing dynamic and context-aware responses.
• Analyze and Transform Rules: LLMs can help analyze existing rules, identify inefficiencies, and even generate suggestions for new or updated rules based on patterns observed in data.

Prompt Engineering for LLMs

To fully harness the power of LLMs in business automation, prompt engineering becomes critical. Prompt engineering is the process of designing and optimizing input prompts to guide LLMs to generate desired outputs.

What is Prompt Engineering?

Prompt engineering involves carefully structuring the input (prompt) to an LLM to ensure that it generates the appropriate output. The prompt acts as a set of instructions, guiding the model to produce the most relevant response. This is crucial in enterprise applications where specific results are expected, such as automating responses to customer inquiries or extracting structured data from unstructured inputs.

Example of Prompt Engineering in Business Automation:

Imagine you have a loan application process where the data is unstructured, such as emails or scanned documents that require further analysis. You can use prompt engineering with an LLM to extract critical data points.

Sample Prompt for LLM to extract structured data from an unstructured email:

You are a loan processing assistant. Extract the following details from this email: 
1. Applicant's name
2. Loan amount requested
3. Applicant's income
4. Credit score (if mentioned)

Email content:
"Dear Team, 
I would like to apply for a loan. My name is John Doe, and I am looking for a loan of $85,000. My monthly income is around $7,000, and my credit score is approximately 720."        

Expected output

{
  "Applicant Name": "John Doe",
  "Loan Amount": "$85,000",
  "Applicant Income": "$7,000",
  "Credit Score": "720"
}        

By structuring the prompt clearly, the LLM can process the unstructured email and extract the relevant information, which can then be passed to a rule-based system like Drools for further processing.

Advanced Prompt Engineering for Dynamic Rule Recommendations

To help rule-based systems evolve, you can use prompt engineering to instruct an LLM to analyze the rules and suggest improvements based on patterns it identifies in the data.

Example Prompt for Rule Analysis:

You are an AI that analyzes business rules for loan approvals. Given the following rule:
- Applicants with a credit score above 700 and income greater than $50,000 are approved for loans under $100,000.

Analyze the rule based on the latest loan approval data, and suggest any optimizations or changes if you notice any inefficiencies or exceptions that were not handled well.        

The LLM can provide recommendations like:

Based on the latest data, you might consider adjusting the credit score threshold to 680, as several applicants with scores between 680-700 were approved manually. Additionally, consider incorporating debt-to-income ratio for more accurate risk assessment.        

In this way, Generative AI assists in keeping business rules optimized and aligned with real-world data.

How JBoss Drools and LLMs Work Together

Combining JBoss Drools and LLMs can provide a robust hybrid automation solution. Here’s how:

1. Handling Ambiguous or Missing Rules: Drools excels at structured decision-making, but if a situation arises that doesn’t fit neatly into existing rules (e.g., an applicant with a borderline credit score), Generative AI can analyze historical data and suggest a recommendation based on trends, filling in the gaps left by the rule engine.
2. Processing Unstructured Data: While Drools processes structured facts, LLMs can transform unstructured data like text documents into structured information that can then be fed into Drools. For example, LLMs can extract data from loan applications, process customer inquiries, or analyze scanned documents to provide Drools with the structured facts it needs to evaluate its rules.
3. Analyzing and Updating Rules: LLMs can be used to continuously monitor the performance of rules in Drools and suggest improvements. For example, if a rule is no longer relevant due to changing market conditions or customer behavior, the LLM can analyze past decisions and recommend modifications to the rule set.

Example Use Case: Loan Approval with Drools and Gen AI

1. Drools handles structured data such as credit scores, income, and loan amounts.
2. Generative AI processes unstructured data, such as emails or scanned documents, converting them into structured facts for Drools to use.
3. If an applicant’s profile doesn’t fit neatly into predefined rules (e.g., borderline credit scores), Gen AI analyzes historical data and provides a probabilistic recommendation.
4. LLMs analyze the existing Drools rules and identify inefficiencies or outdated logic, suggesting optimized versions based on new data patterns.
5. Drools applies its rules to the structured data, supplemented by Gen AI insights, to approve or deny the loan.

This hybrid approach ensures that businesses maintain the transparency and auditability of rule-based systems while gaining the adaptability, flexibility, and dynamic decision-making that LLMs offer.