Intelligent Process Automation (IPA): How It Works and Why It Matters

For mid-market and enterprise leaders, the question isn’t whether to automate—it’s how to do it in a way that drives real impact. Companies implementing intelligent automation have reported annual savings of up to 40% and process times reduced by as much as 70%. These numbers highlight why more organizations are turning to automation solutions that go beyond traditional business process automation.

In this guide, we’ll break down what intelligent process automation is, how it differs from other process automation methods, and whether it’s the right fit for your business operations.

What is Intelligent Process Automation (IPA)?

‍

Intelligent Process Automation (IPA) combines robotic process automation (RPA), artificial intelligence (AI), and machine learning (ML) to automate processes while making them smarter and more adaptive. Unlike traditional automation, IPA doesn't just follow pre-set rules; it learns and improves over time to allow businesses to:

• Handle unstructured data
• Make decisions
• Optimize workflows dynamically.

Some people refer to IPA as RPA 2.0—a more advanced form of RPA, which automates repetitive, rule-based tasks without human intervention. Unlike RPA, which strictly follows predefined rules, IPA integrates AI and natural language processing (NLP) to automate repetitive tasks while continuously adapting to new scenarios. As automation evolves, IPA is becoming a critical part of digital process automation, which enables companies to automate processes that require more than just rule-based execution.

How It Works

Unlike traditional business process automation, IPA operates by integrating multiple automation technologies to create a smarter, more adaptive system:

Example: IPA in Insurance Claims Processing

In the insurance sector, IPA improves claims processing efficiency by automating various stages:

1. Data Extraction - RPA bots automatically gather information from claim forms, emails, and other documents to reduce manual data entry.
2. Claims Processing - Machine learning models assess claims against policy terms and historical data to make informed decisions on approvals or denials.
3. Fraud Detection - AI algorithms analyze data patterns to identify anomalies indicative of fraudulent claims and enable early detection and prevention.
4. Communication - NLP tools generate and send personalized communications to claimants to keep them informed throughout the process.

By implementing IPA, insurers can improve claims handling, accuracy, and customer satisfaction. For instance, it has been reported that claims process automation can assist workflows throughout claims operations, from processing and investigation to management and customer experience.

IPA vs. Other Types of Automation

With the rise of AI-powered automation solutions, businesses have many options to optimize their business processes. However, this abundance of choices can be overwhelming. Is IPA the right fit, or would another type of automation better suit your needs?

To clarify, let's compare IPA with other automation approaches, including RPA, traditional workflow automation, and AI agents. Understanding these differences will help you determine which solution aligns best with your organization's goals.

IPA vs. RPA

• Robotic Process Automation (RPA): Automates repetitive, rule-based tasks by mimicking human interactions with digital systems. It's effective for tasks like data extraction, transaction processing, and managing interactions across multiple applications.
• IPA: integrates AI and ML with RPA to handle variability, learn from data, and improve over time.

Example:  An RPA bot extracts data from invoices, while an IPA analyzes the data to detect anomalies and predict payment trends.  

IPA vs. Traditional Workflow Automation

Traditional workflow automation follows static rules and predefined workflows that require manual updates; it lacks AI-driven adaptability.

IPA adapts dynamically to changes, making workflows more intelligent and efficient without constant reprogramming.

Example - Traditional workflow automation routes customer service tickets based on predefined rules, while IPA analyzes incoming requests in real-time and prioritizes them based on urgency and sentiment analysis. 

IPA vs. AI Agents

AI Agents are components of IPA that focus on specific decision-making or learning tasks.

The key difference from AI Agents is that IPA is the overall system tht manages workflows, while AI agents handle specific tasks or decisions within that system.

Example - IPA automates the entire customer onboarding process, while an AI agent evaluates customer data to personalize recommendations during onboarding.

As organizations scale, intelligent automation like IPA can optimize business processes, reduce costs, and enable faster decision-making.

Intelligent Process Automation Benefits

IPA is still an emerging technology, but businesses already see its potential to optimize business operations and reduce inefficiencies. Here are the key benefits that IPA offers across industries, from finance and healthcare to logistics and customer service:

Improved Efficiency and Productivity

IPA automates repetitive tasks, which frees up employees to focus on high-value work that requires critical thinking and problem-solving. Instead of spending hours on manual data entry, approvals, and reporting, employees can redirect their time higher-level tasks like strategy and customer engagement.

• To reduce administrative overhead, IPA extracts data from invoices or customer forms, automatically inputs the information into internal systems, and triggers approval workflows.
• In the insurance industry, IPA extracts key data from claims forms and inputs it into CRM systems. This eliminates manual data entry (and human errors) and accelerates approvals.
• In human resources, IPA can automate employee onboarding by processing paperwork, setting up accounts, and scheduling orientation sessions.

Cost Reduction

Labor-intensive tasks are expensive, especially when large teams handle manual processes. IPA reduces operational costs by automating these tasks, reducing hiring needs, and optimizing workflows.

• In customer service, deploying IPA-powered chatbots and AI-driven agents to handle routine inquiries has resulted in cost savings ranging from 40% to 75%.
• In logistics, IPA analyzes shipping data to optimize delivery routes and schedules to reduce fuel costs and improve efficiency.
• In retail, IPA predicts demand fluctuations and automates inventory management to prevent overstocking and shortages.

Enhanced Accuracy & Compliance

Human errors in data processing, reporting, and compliance management can lead to costly mistakes. IPA ensures accuracy by eliminating manual intervention, reducing inconsistencies, and ensuring compliance with industry regulations.

• For instance, in the financial sector, a credit card service company achieved a 99% accuracy rate in handling 100,000 vendor invoices each month using IPA solutions.
• In healthcare, IPA ensures that patient records are consistently updated by pulling data from multiple sources, such as emails, medical reports, and appointment scheduling systems.
• Industries such as banking, healthcare, and insurance rely on IPA to automate compliance checks and risk assessments to reduce legal risks.

Scalability and Flexibility

One of the biggest advantages of IPA is its ability to scale. Businesses can expand their automation across multiple departments without overhauling their entire infrastructure.

• E-commerce platforms use IPA to scale up automated workflows during peak seasons (e.g., Black Friday) without hiring additional staff.
• A company that starts with IPA in finance automation can later extend it to HR, customer service, and logistics without needing separate automation systems.

Better Customer Experience

Customers today expect quick responses and personalized interactions. IPA enables businesses to provide real-time customer support, faster service delivery, and accurate responses through AI-powered automation.

• AI-powered chatbots resolve customer inquiries instantly.
• IPA uses NLP and computer vision to analyze customer preferences, feedback, and behavior so businesses can tailor marketing campaigns and service recommendations.
• In e-commerce, IPA automates order tracking, returns, and customer queries.

While intelligent automation is still evolving, these benefits demonstrate why businesses are increasingly adopting IPA.

Should Your Organization Implement IPA or RPA?

Choosing between Intelligent Process Automation (IPA) and Robotic Process Automation (RPA) depends on your organization's business processes and automation needs. Ask yourself the following questions to determine the right fit:

1. Are you looking to automate repetitive tasks that follow a clear process but require handling both structured and unstructured data?
2. Does your automation need AI-driven capabilities, such as natural language processing or machine learning, to handle variability in tasks?
3. Do you need automation that improves over time by learning from data and adapting to new patterns?
4. Are you aiming to reduce manual intervention in workflows while maintaining compliance and accuracy in decision-making?

If you answered "Yes" to all questions, IPA might be the right solution for your automation needs.

If you answered "No" to any, consider whether AI agents or a hybrid AI + automation approach might be more appropriate. There is no single approach that works for every company. Make sure to consider your team, resources, and specific challenges that you want to solve. 

Real-World Example

For many Departments of Motor Vehicles (DMVs) locations, long wait times, inefficient paperwork processing, and manual data entry have been long-standing challenges. Customers often experience delays in vehicle renewals, registrations, and payments–while employees struggle with high workloads and repetitive administrative tasks. These inefficiencies not only slow down operations but also lead to frustration for both staff and the public.

To address these challenges, a U.S. state agency’s DMV implemented IPA to automate critical services. This included:

• Automating vehicle renewals and registrations to reduce in-person visits.
• Streamlining payment processing to eliminate manual errors and delays.
• Digitizing paperwork management to allow AI-driven systems to handle document validation and approvals.

The results were transformative:

• 300,000 employee hours saved.
• Over $14 million in cost savings.
• Faster service delivery and improved customer experience.

With automation and AI, public sector agencies can significantly improve both internal operations and citizen engagement.

FAQ: Common Questions About Intelligent Process Automation

What are intelligent automation examples?

IPA is used across industries for customer support automation, data processing, and workflow optimization. Examples include automated invoice processing, fraud detection, insurance claims handling, and shipping route optimization.

What is the difference between RPA and IPA?

Robotic Process Automation (RPA) automates rule-based, repetitive tasks, while Intelligent Process Automation (IPA) integrates AI and machine learning to handle unstructured data, make decisions, and continuously improve workflows.

Which companies are using IPA?

Major industries, including finance, healthcare, retail, and logistics, are implementing IPA. Banks use it for fraud detection and risk analysis, insurers for claims processing, and e-commerce platforms for customer service automation.

How does IPA improve business operations?

IPA reduces manual work, minimizes errors, and speeds up processes for higher efficiency and cost savings. It can extract and analyze data, automate workflows, and optimize decision-making across departments.

Is IPA suitable for small businesses, or is it only for enterprises?

While large enterprises benefit from IPA at scale, mid-market companies can also implement IPA in customer service, HR automation, and financial reporting to improve efficiency without large infrastructure changes.