What is intelligent process automation? A complete guide.

As generative AI becomes a standard enterprise tool, organizations are focused on integrating it in a scalable, compliant way. It's no longer about if you should use AI, but how to connect it to your core business and whether your existing automation is smart enough to handle AI?

This is precisely where intelligent process automation (IPA) provides the answer. IPA bridges the gap by embedding artificial intelligence directly into your workflows. It supercharges existing automation, enabling far greater efficiency and building more reliable, compliant checks and balances into your core processes.

Intelligent process automation combines business process automation, task automation, and artificial intelligence. These parts work together to improve processes across the whole company. With intelligent process automation in place, organizations will see improved efficiency, greater ability to identify and eliminate process bottlenecks. They will also improve risk management against errors by human operators. 

McKinsey called IPA “the engine at the core of the next-generation operating model.” It lets organizations work faster, reduce costs, and deliver higher quality customer and employee experiences.

Intelligent process automation involves three main components.

Business process management (BPM). Business process management is the foundation of IPA. It provides a structured framework for identifying, analyzing, and redesigning workflows. By mapping out processes, identifying bottlenecks, and defining rules, BPM enables organizations to streamline their operations and ensure that the right tasks are automated.

Robotic process automation (RPA). Robotic process automation plays a pivotal role in automating routine, repetitive tasks, thus freeing up human resources to focus on more complex and creative activities. By integrating RPA into workflows, organizations can achieve significant time and cost savings, minimize errors, and improve process consistency.

Artificial intelligence (AI). AI technologies like machine learning (ML) and natural language processing (NLP) let you automate tasks that need advanced thinking. These tasks include data analysis, decision-making, and understanding language. ML algorithms can analyze large datasets to identify patterns and make predictions, while NLP empowers machines to understand and interact with human language. By leveraging AI, businesses can apply digital process automation to more complex, end-to-end workflows, gain valuable insights, and provide more personalized and efficient services to customers.

Intelligent automation technology improves traditional process automation methods. It helps create smoother business processes. AI harnesses data to enable systems to learn, adapt, and make decisions, so that automation can do even more to save employees time. 

For example, see how these two tasks related to intelligent document processing (IDP) and RPA below change when AI is added into the mix:

• Document classification. IDP was revolutionary when it was introduced, saving employees hours on manual document processing. But AI has enhanced this technology even more. For example, Appian’s AI Skills allows customers to securely train private AI models with their own data. These models can tackle things like email and document classification and document information extraction. 

• Workflow generation. Some robotic process automation providers already enable developers to use a recorder to track their tasks so RPA can follow along and create its own workflow design, but AI will help make it even more seamless. In the future, expect to see RPA bots enhanced with the ability to auto-generate workflows. In fact, RPA trends indicate AI is already influencing both software robot development and the way bots carry out task automation to equip them to handle more complex tasks, rather than just repetitive, simple tasks.
   

The easiest way to think about it is: RPA is the "hands," and IPA is the "brain" that directs the hands.

RPA: The digital hands

A robotic process automation bot serves as a digital helper that you teach to mimic repetitive human actions. It's incredibly fast and accurate, but it can't think for itself.

It's perfect for tasks like data entry when it's copying and pasting data from a spreadsheet into an app. It follows strict, pre-defined rules, and works best with structured data that's clean, like from spreadsheets and forms. If a rule changes or the data is too messy, like in a scanned PDF, the bot gets stuck and stops.

IPA: The digital brain (with hands)

Intelligent process automation is the next evolution. It starts with RPA (the hands) and adds a "digital brain" using technologies like AI, ML, and NLP.

It can read and understand unstructured data, like emails, scanned vendor invoices, or customer inquiries. It can make decisions and learn over time. Instead of just following a rule, it can analyze data to decide the next step. It manages the entire, end-to-end process, not just one task.

So, it’s not really IPA vs. RPA. Think of it this way: You can use an RPA bot on its own to automate a simple task. But IPA is the larger system that uses RPA bots as one of its components, adding an AI brain to manage the entire workflow, handle exceptions, and get smarter over time.

Intelligent process automation is designed for large-scale digital transformations. So if you're looking to make small changes at the margins, like automating simple tasks, IPA probably isn't for you. IPA is better suited to large organizations with lots of data that want to streamline complex, enterprise-wide processes—to digitally transform their workflows, top to bottom.

But 70% of digital transformation initiatives fail. So if you’re struggling, you’re in good company. Intelligent process automation might be the solution you’ve been searching for.

Business processes are messy. They involve lots of people working with large volumes of data stored in all different formats and systems, from scanned invoices, emails, and spreadsheet files to niche apps, modern CRMs, and old-school databases. To keep an automated workflow moving, you need technologies that can access and interpret all your data—structured, semi-structured, and unstructured. IPA fits that bill. 

Intelligent process automation technologies can access and parse unstructured data using data connectors, RPA, optical character recognition (OCR), natural language processing, and AI. Here's an overview of how IPA tools handle unstructured data:

• Integration with systems and applications: IPA tools integrate with other systems and applications using API connectors. Where no APIs or connectors exist, RPA bots can use screen scraping techniques to extract data by simulating human interactions with web interfaces. Or with a data fabric, you can connect all your data directly to your applications, making it accessible in real time, by applying an orchestration layer over all your data sources.

• Data capture: IPA tools can capture unstructured data from scanned, printed, or even handwritten text in documents, images, emails, PDFs, and more. They employ OCR technology to convert unstructured data into machine-readable text. 

• Data interpretation: IPA tools use NLP and AI to parse text and extract meaningful information. For example, from an email, an IPA tool can extract sender details, subject lines, timestamps, message content, and sentiment. They can also categorize unstructured documents based on their content or purpose. Machine learning algorithms can be trained to automatically identify document types, such as invoices, contracts, resumes, or customer forms. This classification can trigger the next step in a workflow—like document routing or processing.
   

If you’ve done things right, your automations should run pretty autonomously. IPA technologies can make intelligent decisions or trigger appropriate workflows based on predefined rules or machine learning models. For instance, an AI tool may use previously extracted data to determine the next steps in an invoice processing workflow, such as invoice validation, approval, or exception handling.

Why can IPA operate with little to no human intervention? Because they run on automated, pre-programmed rules about how data is processed and tasks are executed. Once these rules are established, the automation tool can execute the tasks consistently and accurately. They even have cognitive AI capabilities that allow them to make decisions at inflection points in a workflow.

While IPA can operate with minimal human intervention, it's important to note that human oversight, monitoring, and exception handling are still necessary. Human involvement may be required for handling complex decisions, addressing unique situations, and managing exceptions outside the defined rules. It's also needed to ensure the overall performance and compliance of the automated processes.

Robots have not yet achieved consciousness—we think. But they are faster and more accurate than human workers. One bot can process huge volumes of data or make complex calculations almost instantly, with zero risk of human error. 

And because of their rules-based programming, automations operate highly consistently. That’s great news for industries where compliance is a high priority. Automation tools can be configured to ensure compliance with specific regulations, policies, or industry standards and practices. They can enforce standardized processes, track and log activities, and generate comprehensive audit trails, providing a transparent and reliable record of operations for regulatory purposes. 

Automations also run around the clock, skyrocketing productivity and in many cases eliminating the need for shift work. For example, letting a chatbot handle customer inquiries after hours means not only improving employees’ work/life balance but also faster response times for better customer experience. Intelligent process automation is all about win-wins like these.

Intelligent process automation is highly scalable, giving businesses the flexibility to meet the ever-changing demands of their industry. IPA can be easily modified to meet the changing needs of an organization as the demand for automation rises. Two main factors that make IPA so scalable are its modular architecture and its cloud infrastructure:

Modular architecture. The modular architecture of IPA technologies allows different automation components (e.g., RPA bots, AI models) to be developed and deployed independently. This modularity lets businesses scale their automation efforts by adding new components as needed. Organizations can integrate additional RPA bots to handle more tasks or incorporate advanced AI capabilities for more sophisticated automation. Each module can be tailored to specific processes or functions, providing flexibility and adaptability to scale IPA across different departments and business units.

Cloud infrastructure. Many IPA tools are cloud-based. Cloud platforms offer virtually unlimited processing power and storage, making it easier for businesses to scale their automation initiatives. Organizations can dynamically allocate resources based on demand and quickly provision more computing capacity as workload requirements grow. This eliminates the need for significant upfront infrastructure investments and allows businesses to scale IPA rapidly and reduce costs effectively.

Thanks to the adaptability of IPA technologies, firms may automate tasks in a wide range of departments and functions, and because of its flexibility, IPA will continue to serve businesses well even as their needs change.

IPA models self-improve over time using machine learning. They analyze historical data to identify patterns, learn from experience, and adapt their behavior. This learning is further enhanced by human feedback, as users can correct errors or confirm decisions, which the system uses to refine and optimize its performance.

This powerful combination of automated learning and human refinement is now highly accessible. Modern process automation platforms like Appian are designed for ease of use, eliminating the need for large data science teams or new infrastructure.

For instance, to create a model for classifying and extracting data from documents, you simply upload samples, and the platform handles the training. You can then review and adjust the outcomes. This straightforward feedback loop allows you to build a customized AI model that enhances accuracy and significantly reduces manual work.

IPA brings benefits to nearly every industry and organization that seeks to improve workflow orchestration. Below are examples of use cases that are ideal for IPA.

• Financial operations: payroll processing, accounts receivable and payable, risk assessment, financial reporting, invoice processing

• Human resources: resume screening and candidate ranking, employee onboarding and offboarding

• Customer service: customer chatbots, customer onboarding, customer relationship management

• Healthcare: appointment scheduling, health insurance processing, medical record data entry

• Manufacturing: automated production, quality control with computer vision, predictive analytics for maintenance and inventory

Let's look at a real-life example. Every month, a financial asset management firm was collecting invoices from 22 vendors who billed on varied schedules. An employee had to visit each vendor's website to download the invoice, enter the information into an app, then wait for it to be processed.

They used Appian to build and deploy a broad invoice processing app using RPA, AI, and intelligent document processing (IDP). A bot would access and download the invoices, then AI and IDP were used to extract data from the invoices so they could be processed. Now invoices from all 22 vendors are processed automatically.

Successfully implementing IPA isn't just about buying software; it's about adopting a strategic, end-to-end approach to process transformation. Here is a 5-step guide based on proven methodologies.

You can't fix what you can't see. Instead of relying on guesswork, the best first step is to use process mining to get an objective, data-driven view of your actual operations.

Process mining tools connect to the event logs in your existing systems (like your CRM, ERP, or databases) to create a visual, real-time map of your processes. This instantly reveals what you can improve, highlighting:

• Bottlenecks: Where are tasks getting stuck?

• Deviations: Where are people using workarounds? (This is known as "conformance checking"—comparing your real process to the ideal one).

• Rework: Which steps are repeated most often?

By analyzing this data, you can move beyond automating simple tasks and instead identify the high-volume, high-friction processes where intelligent automation will deliver the greatest business impact.

Once you've identified a target process, you must define what success looks like. Vague goals like "improve efficiency" aren't enough. Your IPA platform should allow you to establish and track specific Key Performance Indicators (KPIs) for every process.

Focus on metrics that directly impact business value:

• Cycle time: What is the average time from process start to finish?

• Cost-per-process: What is the total cost (including human-in-the-loop time) for one transaction?

• Quality and error rates: What percentage of processes require manual rework or correction?

• Automation rate: What percentage of activities within a process are handled by automation versus a human?

Tracking these KPIs is how you will prove the ROI of your pilot project and justify scaling your program.

This is the most critical step. Trying to bolt together separate RPA, AI, and BPM tools creates data silos, increases technical debt, and makes orchestration a nightmare.

Your goal is to find a unified platform that combines all the core components of IPA in one place. As you evaluate options, look for a platform built with these three key capabilities:

• Easy access to all your data: True intelligent automation relies on data. Look for a platform with a built-in data fabric, a library of APIs, and RPA capabilities. This acts as an enterprise-wide layer that can connect to anything in your tech stack, giving your AI the high-quality data it needs to be effective.

• Speed to design and deploy: You shouldn't need a massive team of data scientists to build an automation. Prioritize a low-code platform that offers built-in AI capabilities and, ideally, generative AI–driven tools to help model and build your processes faster. This lets you design and deploy solutions in weeks, not years.

• An integrated, do-more foundation: Choose an integrated platform that handles the full spectrum of automation (BPM, RPA, AI, etc.) in one place. This eliminates IT silos and gives you a versatile foundation to solve many different business challenges without having to buy and bolt on new niche technologies.
   

Don't try to boil the ocean. A big bang release is risky. The best practice is to adopt a phased, agile strategy. Start with a single, well-defined pilot project based on the high-impact process you identified in the first step.

A successful pilot project accomplishes two things:

1. It delivers a quick win. By focusing on a measurable outcome (like the KPIs from step 2), you can deliver tangible business value in weeks, not years.
2. It creates evangelists. This early success is your most powerful tool for getting buy-in from other business units and leadership. A real-world example, like TELUS saving 7,200+ hours on its planning process, is far more persuasive than any slide deck.
    

Your successful pilot isn't the finish line; it's the blueprint for scaling. To move from one-off projects to enterprise-wide transformation, you need governance. This is where you eventually establish an Automation Center of Excellence (CoE). A CoE is a central team of business and IT leaders responsible for:

• Governance: Setting standards, best practices, and security protocols for all automation projects.

• Reusable assets: Creating a library of reusable components (like AI models or API connectors) that accelerate future development.

• Prioritization: Managing the automation pipeline to ensure the team is always working on the highest-value opportunities.

• Continuous improvement: Using process mining to continuously monitor your new automated processes, identify new bottlenecks, and make iterative improvements. This turns automation from a one-time project into a proactive, continuous cycle of optimization.
   

Bringing intelligent process automation to your enterprise doesn’t come without challenges. The biggest ones to watch out for are poor process selection, resistance to change, and data silos that make integration complex. 

To avoid poor process selection, start with a process that’s focused and high value. Choosing a pilot project that’s overly complex can lead to trying to do too much and not seeing the expected results. Additionally, avoid automating low-value tasks that don’t have measurable ROI. Lack of provable ROI makes it difficult to get leadership buy-in for scaling. 

Create a proactive change management plan to help employees adopt new automation efforts. Without user buy-in, employees may not adopt new systems leading to workarounds that undermine the automation. Having employees that can help serve as evangelists for the change can help push automation efforts forward. 

To overcome data silos and integration complexity, avoid trying to bolt together separate RPA, AI, and BPM tools, which creates technical debt. The most effective strategy is to select a single, unified platform that combines all the core components. Look for a platform with a built-in data fabric, which acts as an orchestration layer to connect all your data sources in real-time. This gives your AI the high-quality data it needs.

As organizations move from simply experimenting with artificial intelligence to embedding it into their core operations, the challenge becomes clear. It's no longer just about if you should use AI, but how to connect it to your business in a scalable, compliant, and reliable way. Intelligent process automation provides the essential framework, bridging the gap between powerful AI capabilities and tangible business value by embedding intelligence directly into your workflows.

A successful transformation begins not with buying software, but with a deep understanding of your current processes. Identify and prioritize which processes you want to improve where automation can deliver the greatest business impact. Process mining can help you get an objective, data-driven map of your operations that will reveal the bottlenecks, deviations, and high-friction tasks that are ideal candidates. 

By starting with a clear, objective view of your processes, you can move from a one-time project to a continuous cycle of optimization, building an intelligent and efficient enterprise from the ground up.