How To Use Business Process Automation To Streamline Your Business Operations?

Business process automation (BPA) automates multistep and repetitive business operations so that employees can focus on mission-critical strategic tasks. Unlike Robotic process automation (RPA) which mimic simple and repetitive human inputs, BPA is more complex and deeply integrated with the existing tech stack such as APIs, databases, ERMS, CRMs, and knowledge management systems. 

Plus, some business process automation platforms use artificial intelligence (AI) to process information extracted from emails, audio files, videos, and images. 

Let’s take an example, a lending company uses document processing automation to process loan applications for faster approvals. The software retrieves information like income and expenditures from customer’s financial statements. An analysis automation system identifies spending patterns and repayment capability. The decision-making software provides provisional approval based on the risk assessment done by the previous systems. 

In the above example, the company uses the 3 major types of BPA to improve the efficiency and speed of their workflow. That said, these processes improve your workflow when used independently. 

Throughout this article, we cover the different types of business process automation and help you choose the best BPA for your business.  

What Are Different Types of Business Process Automation?

Here are some different types of business process automation:

A. Document processing automation

Document processing automation software, also known as intelligent document processing systems (IDP) combines traditional OCR with AI to transform structured, semi-structured, and unstructured data into a machine-readable format. IDP platforms capture, classify, and categorize information in easy-to-navigate databases.

The IDP platform can be customized with verification and validation functionality to assess the authenticity of the extracted information. 

Importance of document processing automation 

Document processing automation solutions are effective for reducing manual workloads and eliminating inefficiencies from paper-based workflows. The document processing workflow is divided into multiple steps-

• Pre-processing: Pre-processing prepares the scanned documents for ingestion by cleaning the document using techniques, such as deskewing, binarization, denoising, and zoning to prepare it for the next step. 
• Data extraction: The platform uses data extraction techniques, such as optical character recognition (OCR), pattern matching, or natural language processing (NLP) to capture information. Captured information includes tables, key-value pairs, keywords, dates, and other structured elements from the unstructured documents. 
• Intelligent document classification: Machine learning (ML) models classify the documents based on keywords and organize documents based on the content. This detailed categorization improves searchability and document storage. 
• Verification and validation: The verification process compares the extracted information with data from external databases and glossaries to check for authenticity. Once the information is validated, it is sent for approval or storage. When the software identifies anomalies the data is sent for human evaluation. 
• Handling exceptions: In case of discrepancies, employees are automatically alerted to check the documents manually. The IDP platform learns from the corrections made by the employees and adapts its ML algorithm to automatically correct such errors in the future.  

Document processing automation is becoming critical for industries that process documents and information at scale. That’s because manual document processing and handling has limitations that lead to inefficiencies throughout the process. 

Challenges of manual document handling 

Manual document processing is plagued with challenges such as: 

• The process is inefficient and time-consuming as employees have to manually read through the documents to verify the information.
• Manual document processing has an error rate of 40%, creating operational bottlenecks. 
• High risk of data breaches and leaks of confidential information as sensitive documents pass through multiple employees. 
• Manual storage of documents creates compliance risks and exposes the company to penalties and litigations.
• As the documents are physically stored, they lack searchability which hinders the information retrieval process leading to lower customer satisfaction. 

Document processing automation tools 

To achieve true document processing automation, an organization needs a data capture technology coupled with a document management system.

• Document data capture technology: Document data capture technology such as IDP, OCR, and NLP extracts data from the documents and sends it to an integrated downstream application for further processing.
• Intelligent document processing (IDP): IDP platforms ensure end-to-end automation of document processing workflows as seen earlier.
• Plus, extracted data is achieved according to GDPR and CCPA regulations and stored in the cloud. 
• Optical character recognition (OCR): OCR technology extracts texts and numbers from documents using template-based matching. The captured text is converted into a machine-readable language which increases its searchability. 
• Natural language processing (NLP): NLP, a branch of AI, adds contextual understanding to the data extraction process and improves categorization features based on contexts. Document management systems: While advanced data capture solutions store the extracted data over the cloud, you can use document management systems for the same purpose of archiving data as per regulations.

Benefits of document processing automation

The three major advantages of using document processing automation are: 

• Reduces errors with 99% accuracy: Unlike manual document processing with variable data accuracy, IDP platforms consistently offer 99% accuracy for multiple document formats with a straight-through-processing rate of 95%.
• Increases cost savings: Manual document processing software removes the need for a dedicated manual data extraction team reducing costs by 70%. 
• Faster document processing: Automated platforms using AI solutions increase data extraction speed by 10X compared to manual processes. 

b. Analysis automation

Data analysis automation refers to real-time information analysis to gain insights using advanced computer programs, powerful business intelligence (BI) tools, and simulations. Organizations use automated analytics software to focus on critical business metrics that move across multiple dimensions. One of the best examples of such metrics is banks monitoring risk for their bigger client loans. 

To calculate the default risk, they consider the normal income and expenditure of the company along with other environmental and economic factors. Such complex analysis is manually challenging. Data analysis automation makes complex data and trends easy to understand through visualization. Plus, it allows self-service report generation and dashboard creation. 

Significance of analyzing extracted data

Automated data analytics is confused as only real-time monitoring of the metrics. However, the analysis automation is a 4-step process, with monitoring being the first one.

• Monitoring: Monitoring is the process of observing the system to ensure it is functioning within the parameters. The monitoring systems automatically communicate whenever the metrics or values go above or below the threshold. Monitoring systems store historical data, manage alert notifications, and perform capacity planning, among other things. 
• Analytics: Data analytics involves converting complex data points into comprehensible insights. Analytics programs rely on visualizations, like charts, reports, and live dashboards, to communicate their findings to the user. But, the process doesn’t end here. 
• Observability: Data observability helps analysts understand the quality of data in the systems. What’s more, it provides enough context to identify the problems and offer recommendations to fix them. 
• Diagnostics: Diagnostics automation helps identify the reason behind the issues, trends, and patterns and explains the correlation between the variables. Like all the other steps, this, too, can be done manually. However, it is time-consuming and labor-intensive. The system runs hypothesis testing to prove and disprove assumptions entered by the users. 

Challenges in manual data analysis 

Manually monitoring and identifying irregularities in data trends is an exhausting task. Challenges in manual data analysis include: 

• Employees spend hours sifting through cluttered data to gain insights which makes this process error-prone.
• Misrepresentation of the insights gathered from the data. 
• Employees need a strong technical understanding of data analytics to operate multiple disjointed systems.
• Difficulty in consolidating data from multiple sources and presenting it meaningfully 
• Low-quality and inaccurate data distorts the findings and affects the decision-making process. 

Automation tools and technologies

Automating business operations eases the workload on the analysts and helps present complex data structures in easy-to-understand reports to the leadership. 

Data analytics software

Data scientists and analysts rely on data analytics software to automate-

• Data segmentation 
• Segregating data based on different time-frames
• Increase business value 

Moreover, the predictive analysis features highlight potential problems and pitfalls using pattern recognition. Lastly, automation enables data scientists to focus on important business and decision-making activities. 

Business intelligence (BI) tools 

BI tools use BPA to quickly extract data from multiple sources and eliminate manual data entry. The platform understands the data input and converts it into a live metric into a visual report on the dashboard. 

Benefits of analysis automation

Here are the two major benefits of automated data analytics:

• Real-time insights: Automate the data stream for the dashboards to keep the reports updated and save time in collecting reliable information. 
• Enhanced decision-making: The data models generated by BI tools and data analytics software enable the leadership to make data-driven decisions. Finance personnel use analytics tools to identify anomalies and prevent unauthorized transactions and potential fraud. 

c. Decisioning automation

Making an informed decision is not always about choosing between good and bad options. More often than not, the decision-makers have to make tough choices that increase profitability while minimizing the damages. 

Decision-making automation software emulates the leadership’s decision-making pattern to evaluate the situation and identify the correct course of action using data analytics, ML models, and AI. 

Let’s understand the actual role of decision-making BPAs in a hierarchical organization. 

The role of automated decision-making in business processes

Decision automation software processes data and makes a decision based on specific criteria and parameters. Consequently, these automation are used to make daily decisions regarding routine and repetitive tasks that affect smaller parts of the operation strategy of the organization. 

They need human input as the embedded AI learns from the successes and failures of similar decisions. The system implements the observe-orient-decide-act loop, or the OODA model to make informed decisions. The OODA model presents 3 types of decision automation:

Complete decision automation

The automated decision-making system leverages AI, and contextual data to offer fast, scalable, and consistent decisions. It uses prescriptive and predictive analysis to automate the observing, orienting, and deciding phases. The acting phase is semi-automated which means it only alerts the user to implement the decision. 

Advanced decision support

In advanced decision support, the orientation and observation phases are automated. Instead of deciding on the user’s behalf, it only offers recommendations.

Decision support

Lastly, in regular decision support, the observation phase is automated. The user needs to draw their own decisions after using insights gained from the orientation phase. 

Challenges in manual decision-making

Apart from uncertainty, let’s look at the manual decision-making challenges that lead to the adoption of automated resources: 

• The process is hampered when the manager is overloaded with information which prevents them from focusing on the bigger picture. 
• The fear of making wrong choices thwarts the manager’s ability to make a decision. 
•  Personal beliefs and biases influence their decisions. 
• Pressure from the  C-suite executives influences decision-making 
• The decision-making process becomes increasingly difficult as the complexity of the problem increases. 

Automation tools and technologies 

Streamlining with business automation tools includes combining mathematical simulations with AI. 

Monte Carlo simulations

The decision support systems can run the Monte Carlo simulation that analyses historical data and the present scenario to predict the future outcome of uncertain decisions. The uncertain scenario is simulated multiple times until it generates optimal results with the provided resources. This reduces the fear of the unknown and enables the manager to take quick action. 

AI for decision-making

AI programs can be trained to mimic the operating style and the thinking capacity of the CXO employees. Eventually, these programs start similarly making decisions, reducing the workload on the managers. 

Benefits of decision-making automation

The two critical benefits of decision-making automation are:

Consistency in decision-making

The AI within the system self-regulates and takes preventive measures without significant supervision and decisions are consistent and aligned with the vision of the organization. 

Increased efficiency through process automation 

The decision-making workflows constantly adapt as new information is passed through the system. In the long run, it increases the efficiency of the managers. 

Here’s a real-life use case of a document processing BPA and how it improved the organization's workflows. 

‍BiagiBros, a 3PL warehousing company, struggled with:

• Manually processing 3000+ documents per month
• Assigning barcodes to their shipments as it took more than 20 minutes to assign one. 
• Capturing data from 50+ inbound leads on a daily process
• Validating the captured data
• Manually scanning data from 10+ bills of lading 

Docsumo, is an IDP platform, which helped BiagiBros set up document processing automation to tackle these problems and optimize their workflow. After Docsumo’s implementation, BiagiBros

• Automatically captured data from unstructured documents 
• Assigned barcodes within 2 minutes and saved 500+ hours every month 
• Validated captured data in real time using custom data
• Reduced human input with a 95% STP rate
• Reduced their processing cost by 70%
• Saved $10,000+ per month

BiagiBros’ tremendous increase in profitability and resource savings should be enough to consider you to start using streamlined workflows with automation using advanced BPAs.

Start automating your document processing workflows with Docsumo’s 14-day free trial.