Why Smart Leaders Use AI Business Process Automation to Simplify Processes

Combine AI and business process automation before implementing robotic process automation (RPA) to help you simplify processes, remove unnecessary steps, and ensure consistent inputs and reliable decision-making. The end result is a process ready for standardization and AI-driven automation.

In brief:

• AI business process automation simplifies workflows. It removes unnecessary steps and ensures consistent inputs and reliable decisions.
• With simpler processes and cleaner data, RPA systems work more effectively to lower costs and reduce employee frustration.
• Simpler processes can also aid in compliance, particularly in highly regulated industries.
• Microsoft Power Automate is one solution available for integrating AI and RPA.

For years, robotic process automation (RPA) — software that uses unattended “bots” similar to programmable digital employees to automate repetitive digital tasks — has been the ultimate shortcut to increased efficiency. RPA has blossomed into a $23 billion industry as businesses such as supply chain management organizations and educational institutions now use RPA to perform routine tasks more quickly, more accurately and more efficiently.

Today, generative artificial intelligence (AI) can make RPA even more transformational.

“Every industry has those tasks where it’s easy to get bogged down with data, verification and analysis,” says automation leader UiPath. “With the combination of RPA and AI, you can streamline those processes and free your human workforce to focus on tasks where they can have a greater impact.”

So, how do you get there?

In this guide, we’ll explain why process simplification matters for any RPA project. We’ll also show how implementing AI helps simplify and automate processes. Once you streamline your processes, you’ll be ready for business process automation.

But first, let’s look more closely at simplification.

Why Simplification Matters for Business Process Automation

Simplifying processes is crucial because it enables error-free outcomes that keep organizations competitive. Smooth, efficient processes also boost morale and productivity.

Complexity leads to frequent exceptions and rework loops. It also causes handoff delays and worsens regional differences in how work gets done. This makes it harder for employees to understand and consistently follow a process. As a result, business process automation efforts will struggle because processes are a lifeblood for RPA.

When you simplify first, you give your teams clarity and consistency, which makes it easier to standardize practices, reduce variation, and ensure everyone operates under the same expectations.

Process simplification also helps eliminate or reduce:

• Workflows With Too Many Exceptions. You can often eliminate exceptions by clarifying — or even eliminating — categories. For instance, if all your employees work in one facility or city, you can eliminate exceptions for location on your HR forms.
• Redundant Steps. How many times does someone have to enter their name, address or email? Alternatively, for physical components, how often do they enter building or dock information? The golden rule should be “Only once.”
• Unnecessary Steps. Say your form requires users to specify how they prefer to be addressed — “Mr.”? “Miss”? “Ms.”? However, nothing in your process requires this data. Eliminating such steps creates efficiency, making customers happier and reducing manual reviews. Removing more substantive, unnecessary steps can even lower the frequency of department head approvals.

Business process simplification is also important for RPA’s other part of the lifeblood: data. Similar to processes, data works best when it is free of outliers, redundancies, inconsistencies or unnecessary information. For example, imagine if your data consistently spelled out the word “Street” rather than using the abbreviation “St.,” or if it was full of errors that can flow from manual inputs.

Many aspects of data cleaning are beyond the scope of this article, but the main point remains: The simpler your processes and data are, the better your RPA will work.

For example, suppose an online retailer decides to automate its order intake system to integrate with an online inventory solution. The company begins to examine its current intake system and quickly realizes that it doesn’t consistently categorize each item in its stock.

As a result, the company has no idea whether a customer buys shoes or sweatpants more often. When the company has a big shoe sale, they don’t know if they are reaching out to the right group of customers.

By turning their disorganized system into a streamlined process, the company now has valuable data to support personalized marketing that improves the customer experience — an insight that only became possible through the simplification.

Compliance: Another Benefit of Business Process Simplification

When processes and data are clean and organized, compliance improves. To meet regulations, you must understand how your systems work and be able to access the right data quickly. The more organized your processes and data, the easier it is to enforce governance and meet requirements.

Fortunately, you can also design RPA processes with governance and compliance in mind.

“Automation governance puts easily accessible and understandable steps in place, such as documentation, tracking, and communication practices, so automated processes run smoothly and integrate into your workplace seamlessly,” says Nick Rahn, Centric Consulting operation excellence architect.

Tracking and good documentation make compliance easier. For example, consider a financial services company that must handle various data types (such as Social Security numbers, tax identification numbers, transaction histories) in accordance with the Gramm-Leach-Bliley Act (GLBA).

With the proper preparation and governance, the company can build an RPA solution that automates a time-consuming process while simultaneously ensuring compliance. Any organization subject to compliance regulations may be able to score a twofer while using simplification to combat inefficiency.

Prepare Your Business for AI-Powered Automation: 5 Steps

Clearly, you have numerous decisions to make before moving forward with building an RPA solution. Before getting neck-deep in this work, it’s worth reflecting on other ways process simplification can benefit your organization — and the role it may play in an ongoing business process management and improvement program.

Below is a step-by-step guide to simplifying your business processes. As we’ll see, many of them can be augmented by AI. Together, they will help you prepare for RPA initiatives and begin thinking more broadly about business process improvement:

1. Identify Stakeholders

Document the decision-makers and employees who need to improve or will be affected by the simplification process. To earn their buy-in, outline how simplification can benefit your RPA and overall operational processes.

2. Define Business Rules and Exception Handling

Explain how current business rules will change — and benefit — from RPA. Standard rules include approval hierarchies, data validations and processing timelines.

3. Assess Your Current Processes

Document each step and decision point. Note every input and output involved. Process mapping your current workflows will highlight how work gets done now and identify costly, wasteful or low-value activities.

4. Consolidate and Streamline Tasks

When possible, simplify or consolidate by reducing handoffs between departments and eliminating manual actions.

5. Perform a Feasibility Assessment

This easy-to-miss step verifies whether simplification will negatively impact the RPA’s desired outcome. For example, a simplification that removes crucial data needed for decision-making is not feasible.

If that sounds like a lot of work, you’re not wrong. However, AI can help with these and other tasks in many ways as you prepare your processes for RPA.

How AI Can Help Streamline Intelligent Automation

By automatically scanning digital workflows, AI can detect redundant steps and inconsistent data formats, then flag them for improvement. This technique, known as process mining, can also help you visualize workflows and identify steps that you can automate or remove.

AI can also help standardize templates by analyzing existing documents and recommending uniform formats that make it easier for AI tools to ingest (read and process) and understand data.

Through its analysis, AI can reveal hidden inefficiencies and productivity issues that have existed for years. By using templates to eliminate unnecessary information and standardize data inputs, organizations enable machine learning (ML) — a type of AI that uses historical data to find patterns and make predictions.

In addition, AI’s ability to reduce manual steps can prepare organizations for hyperautomation, which is the combination of ML and RPA, to automate tasks beyond RPA alone.

AI can also identify overly complicated processes before you build them into your RPA. Even with AI incorporated into your RPA, unnecessarily complex processes may require more time to fix the outputs than they save on manual work.

For instance, when data is inconsistent, a bot may not fill every field. This can create manual work instead of reducing it. For example, a customer may enter their state or province in the “Country” field. As a result, your RPA’s data validation leaves that section blank, and employees must search for the missing data.

A consistent initial data entry process, identified by AI tools, would have clarified what needed to be input and where, preempting time-wasting activities and other issues down the line.

Conclusion: Take the Next Step Into AI Business Process Automation

Now that you’ve seen AI’s potential for simplifying business processes before creating an RPA solution, you may be wondering how to integrate those benefits into your RPA.

Microsoft Power Automate is one of many automation technology tools that can help. Created to integrate with Microsoft platforms, including Microsoft 365, Dynamics, and Azure, Power Automate uses a suite of Microsoft resources:

• AI Builder adds intelligence to workflow automation for tasks such as processing forms, extracting data from documents or predicting outcomes. Automate offers prebuilt models for these tasks, or you can build your own.
• Foundry Tools (previously Azure Cognitive Services) enable advanced AI tasks, including sentiment analysis, language translation and image recognition.
• Natural language processing (NLP) automates tasks that require text or voice comprehension, such as categorizing emails or analyzing customer feedback.
• Predictive analytics use AI models to forecast trends or detect anomalies in business processes.
• Human-in-the-loop AI automation allows employees to review, approve or reject AI-driven decisions within complex workflows.
• Microsoft Power Apps allows you to create AI-powered apps that trigger automated workflows.
• Power BI provides AI-driven insights and analytics that feed into automated decision-making.
• Dataverse is a centralized data platform for storing and managing AI model outputs and workflow data.

By putting AI to work on simplifying processes before implementing RPA and extending its capabilities to the workings of your RPA, you’ll be prepared for additional AI implementation to satisfy customers, delight employees, and prepare for whatever comes next.

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FAQs

1. What is the difference between RPA and AI business process automation?

Robotic process automation (RPA) uses software bots to automate repetitive digital tasks based on predefined rules. AI takes automation further by incorporating artificial intelligence capabilities like machine learning, natural language processing and predictive analytics.

While traditional RPA follows set instructions, AI-enhanced automation can analyze data, make intelligent decisions, detect patterns, and adapt to variations in processes. Together, RPA and AI create a more powerful solution that handles both routine tasks and complex decision-making.

2. Why should I simplify processes before implementing RPA?

Process simplification is crucial because complex processes lead to frequent exceptions, rework loops, and inconsistent outcomes that make automation difficult. When you simplify processes before implementing RPA, you:

• Eliminate redundant steps
• Reduce exceptions
• Produce cleaner data with fewer errors and inconsistencies
• Create clearer workflows that RPA can execute efficiently

Without simplification, your RPA may struggle with variations, require constant fixes, and create more manual work instead of reducing it. Simplification gives your automation efforts the clarity and consistency needed for success.

3. How can AI help identify inefficiencies in existing business processes?

AI can automatically scan digital workflows to detect redundant steps and inconsistent data formats through a technique called process mining. This technology visualizes workflows and identifies bottlenecks, unnecessary steps, and automation opportunities that humans might miss.

AI can also analyze existing documents to recommend uniform formats and standardize templates, making data easier to process. By examining patterns across your operations, AI reveals hidden inefficiencies and productivity issues that may have existed for years, providing objective insights that prepare your processes for automation.

4. What are common examples of unnecessary steps that can be eliminated through process simplification?

Common unnecessary steps include:

• Requiring users to enter the same information multiple times (name, address, email)
• Collecting data that’s never actually used in the process (like honorific titles when they’re not needed)
• Maintaining exception categories that no longer apply (such as location fields when all employees work in one facility)
• Requiring excessive approvals for routine decisions

Physical workflow redundancies include repeatedly entering building or dock information. Following the golden rule of data entry — information should only be entered once — helps eliminate these wasteful steps and improves both efficiency and user experience.

5. Can AI business process automation help with regulatory compliance?

Yes, AI business process automation significantly aids compliance efforts. When processes and data are simplified and organized, it becomes easier to enforce governance and meet regulatory requirements. RPA solutions can be designed with built-in compliance measures, including documentation, tracking, and communication practices that ensure processes run according to regulations.

For example, financial services companies can automate time-consuming tasks while simultaneously ensuring compliance with regulations like the Gramm-Leach-Bliley Act. The tracking and documentation features built into automated processes make auditing easier and help organizations quickly access the right data to demonstrate compliance.