Top 5 Intelligent Automation Examples for Your Organizations

 Why settle for automating mundane tasks with basic bots and macros when you can supercharge your business processes with intelligent bots fueled by AI? Intelligent automation not only saves time and money but goes beyond fixing human errors, offering a transformative edge.

Intelligent automation harnesses the power of AI alongside traditional technologies to achieve unparalleled performance. This means more than just efficiency – it opens doors to innovation and frees up resources for seizing new opportunities.

Are you curious about how this works in real business processes? Read on for specific examples where AI-backed automation, combining technologies like intelligent document processing, robotic process automation, machine learning, optical character recognition, computer vision, and business process automation, can elevate your business game.

1. Invoice Processing

   Invoice processing is a critical but time-consuming task for businesses of all sizes, considering the amount of data required to collect, analyze and process the information. Here, AI-enabled document processing can play a role in extracting documents from multiple resources and processing the information faster. Using AutomationEdge’s IDP in conjunction with RPA bots and business process automation, a major product distributor, Transformed, improved its invoice process using intelligent automation solutions.
   

   Each day, the firm receives over 1000 invoices, most of which have unique formats. Due to the enormous workload, there was a substantial risk of human error. Moreover, if the problems weren’t addressed, any errors would have damaged the vendor-client relationship, which would have resulted in substantial losses for the company.

   Using AutomationEdge’s Intelligent Document Processing solution, Transformed was able to Process 1000 invoices daily using AI bots. Having AI in place, Transformed was able to improve its employee productivity by 70%.

2. Service Desk Automation

   The service desk is the lifeline of IT support in any organization. Right from routing service tickets to extracting information for faster resolution, the IT service desk has a lot on its hands to serve. With a lot of data management and processing involved, AI-enabled bots can benefit both service desk agents and customers at the same time. Take another AutomationEdge client example.

   Wilsonart, a manufacturing company, was handling a lot of service ticket requests like account creation, sharing folders and data access. Handling multiple data violations, managing data retrieval and providing operation support 24/7 was a constant challenge. Using intelligent automation in the process, Wilsonart was able to fast-track its service desk operations by easily assigning service tickets to the right agent to offer instant resolution and create a scalable design to handle high-volume service ticket requests. With Intelligent Automation in place, Wilsonart achieved 95% manual effort reduction and 100% error-free service desk operations.

3. Data Reconciliation

   In companies dealing with a high volume of daily transactions, the reconciliation process can consume a considerable amount of time. This challenge is particularly pronounced in financial institutions, where customer transactions form the core of the business model.
   Also, managing data extraction and checking discrepancies between data is another challenge that makes data reconciliation a suitable use case for intelligent automation.

   One of our customers, HDFC Bank, used to manually reconcile a large volume of transactions daily by cross-referencing multiple spreadsheets. Their team of employees had the task of matching entries from various external banks with their internal spreadsheet of daily transactions to ensure data accuracy. This process was not only time-consuming but also prone to errors. To reduce the manual efforts and fast-track the reconciliation process, AutomationEdge built RPA bots to automate the process and layer it with AI to make sure that bots can understand the data variations and achieve a 90% TAT reduction in the reconciliation process.

4. Employee Onboarding

   Employee onboarding is a critical process that sets the tone for an employee’s experience with an organization. Intelligent automation can simplify and accelerate this process by automating administrative tasks such as document verification, background checks, and account provisioning. Automated onboarding workflows ensure consistency, compliance, and a positive onboarding experience for new hires, allowing HR teams to focus on more strategic aspects of talent management.

   One of our biggest customers, Axis bank, was struggling to handle the manual and tedious task of matching new employee details and handling 50+ employee background verification requests per day. Here AutomationEdge’s Intelligent automation solution helped create an automated system that handled 50+ requests using AI bots and reduced 1 FTE efforts.

5. Claims Processing

   Claims processing with intelligent automation involves leveraging advanced technologies such as Robotic Process Automation (RPA) and machine learning to automate and expedite the end-to-end claims workflow. From data extraction in claim forms to validation and assessment, intelligent automation ensures a streamlined and efficient process, reducing manual intervention and enhancing accuracy. This approach not only accelerates claims processing times but also helps identify and prevent fraudulent claims through data analytics.

Ready to Make Improvements with Intelligent Automation?

Intelligent automation is reshaping the way organizations operate, offering unprecedented opportunities to enhance efficiency, reduce costs, and improve accuracy. From streamlining financial processes like invoice processing and reconciliation to optimizing IT support through service desk automation, businesses across various industries are reaping the benefits of intelligent automation. As technology continues to advance, organizations that embrace and integrate intelligent automation into their workflows will undoubtedly gain a competitive edge in today’s dynamic business landscape.

These intelligent automation examples may have given you an idea of where your business could benefit from intelligent automation solutions. If you remain uncertain, AutomationEdge experts can assist you in identifying the processes that would yield the most significant benefits through automation. Contact us to know more.

Healthcare Revenue Cycle Management(RCM) Automation With RPA and AI

As per Grand View Research, the global market for revenue cycle management is projected to expand at a compound annual growth rate of 11.6% from 2022-2030. Considering the amount of increment in revenue cycle management, demand for a technology solution like AI and RPA in healthcare is also accelerating. Some reports stated that AI in healthcare is expected to expand at an annual growth rate of 38.4% from 2022-2020. As healthcare revenue management involves multiple steps, leveraging AI and automation solutions can help drive better revenue management.

In simple words, the RCM process begins with patient appointments and ends when full payment is initiated in the provider’s account. Between these two points occur five important steps that complete the whole revenue cycle. Automating these steps and processes with RPA and AI can help create better revenue management systems. These five steps are:-

1. Insurance Eligibility Verification

2. Medical Coding and Claims Preparation

3. Claims Submission

4. Dispute Management

5. Claims Reconciliation

To read more for detailed information here.

How to Choose the Right Intelligent Document Processing Software for Your Business?

 The abundance of unstructured data found in various formats such as emails, social media posts, blogs, news articles, PDFs, and physical documents presents a significant challenge for companies seeking to leverage this information effectively. 

Without digitization, valuable strategic insights remain untapped, hindering the progress toward hyperautomation and digital transformation. To address this issue, organizations and marketers are increasingly adopting Intelligent Document Processing solutions, enabling them to extract valuable business insights from diverse document sources along the entire business value chain.

What is Intelligent Document Processing?

Intelligent Document Processing (IDP) revolutionizes conventional business practices by digitizing manual customer data. By incorporating AI and ML capabilities, Intelligent Document Automation enables the extraction of data and its integration into the process workflow, enhancing document intelligence.

IDP automation empowers organizations to digitize unstructured and semi-structured data, facilitating intelligent data processing and transforming it into structured data. Achieving end-to-end automation throughout the data collection and extraction lifecycle has become indispensable for companies involved in customer data collection or managing vast amounts of unstructured information as part of their digital transformation journey.

How to Choose the Right Intelligent Document Processing Software for Your Business?

Choosing the right intelligent document processing (IDP) software for your business can significantly streamline your document-related workflows and improve efficiency. Here are some key factors to consider when selecting the appropriate IDP software:

1. Identify Your Specific Needs

Start by understanding your organization's unique document processing requirements. Determine the types of documents you regularly handle, the volume of documents, and the specific tasks you want the IDP software to automate. This will help you narrow down your options and find a solution that aligns with your needs.

2. Document processing capabilities

 Evaluate the IDP software's capabilities in handling various document formats, such as invoices, purchase orders, contracts, and forms. Ensure that the software can effectively extract relevant data from these documents and validate it against predefined rules. Look for features like optical character recognition (OCR), natural language processing (NLP), and machine learning algorithms for accurate and automated data extraction.

3. Integration with existing systems

 Consider the compatibility of the IDP software with your existing IT infrastructure and systems. Ensure that it can seamlessly integrate with your document management, content management, or enterprise resource planning (ERP) systems. This integration will facilitate smooth data exchange, reduce manual entry, and enhance overall efficiency.

4. Customization and Flexibility

Look for IDP software that allows customization to meet your specific business requirements. Different organizations have unique document layouts, industry-specific terminology, and compliance regulations. The software should provide flexibility in configuring document templates, defining extraction rules, and adapting to changing needs without significant programming efforts.

5. Accuracy and Reliability

Evaluate the accuracy and reliability of the IDP software's data extraction capabilities. Look for solutions that offer high precision and have built-in validation mechanisms to minimize errors. Check for features like confidence scoring, data verification, and exception handling to ensure data integrity and minimize manual intervention.

6. Scalability and Performance

 Consider your business's future growth and scalability requirements. Choose IDP software that can handle increasing document volumes without compromising performance. Look for options that offer cloud-based or distributed architectures to support scalability and ensure fast processing times.

7. User-Friendliness and Ease of Implementation

Assess the software's user interface and ease of use for both technical and non-technical users. Consider how intuitive the software is, the training required, and the level of technical expertise needed to configure and maintain it. Additionally, evaluate the implementation process, including deployment timelines, vendor support, and potential integration challenges.

8. Security and Compliance

 Document processing often involves sensitive and confidential information. Ensure that the IDP software follows industry-standard security protocols to protect data during extraction, storage, and transmission. Look for solutions that comply with relevant data privacy regulations, such as GDPR or HIPAA, and provide audit trails and access controls.

9. Vendor Support

Research the reputation and track record of the IDP software vendor. Consider factors such as industry experience, customer reviews, and case studies. Evaluate the vendor's commitment to ongoing support, software updates, and responsiveness to customer issues or feature requests.

10. Total Cost of Ownership

 Lastly, consider the overall cost of implementing and maintaining the IDP software. Evaluate factors such as software licensing fees, implementation costs, ongoing support charges, and potential training expenses. Additionally, consider the return on investment (ROI) the software can provide by reducing manual efforts, improving accuracy, and enhancing operational efficiency.

By carefully evaluating these factors and conducting thorough research, you can select the right intelligent document-processing software that best meets your business requirements and accelerates your document-related workflows.

What is Hyperautomation?

Hyperautomation refers to the use of advanced technologies such as artificial intelligence (AI), machine learning (ML), and Robotic Process Automation (RPA) to automate complex business processes. It involves the use of multiple technologies working together to automate and streamline business operations, from data extraction and analysis to decision-making and process optimization.

It goes beyond traditional automation by combining technologies such as AI, ML, and RPA to create intelligent automation that can learn, adapt, and make decisions on its own. This allows businesses to automate more complex tasks that were previously performed manually, reducing errors, improving efficiency, and freeing up employees to focus on more strategic work.

Hyperautomation involves a combination of software tools, algorithms, and workflows that work together to automate processes end-to-end. It can be used across a range of industries like banking, healthcare, insurance, manufacturing, and IT across multiple business functions like fraud detection, claims processing, anti-money laundering, ticket intelligence, and a lot more. 

Benefits of Hyperautomation

Hyperautomation refers to the integration of advanced technologies, such as artificial intelligence, robotic process automation, and machine learning, to automate complex business processes. Here are some of the benefits of hyperautomation:

1. Increased productivity: Hyperautomation enables businesses to automate repetitive tasks, freeing up time for employees to focus on more strategic and creative work. This leads to increased productivity and efficiency.

2. Improved accuracy: Automation eliminates the risk of human error, which can improve the accuracy of business processes. This can be particularly important in areas such as finance, where even small errors can have significant consequences.

3. Cost savings: Hyperautomation can help businesses reduce their operational costs by streamlining processes and reducing the need for manual labor.

4. Enhanced customer experience: Automation can improve the speed and accuracy of customer interactions, leading to a better customer experience.

5. Better decision-making: By providing real-time insights and analytics, hyperautomation can help businesses make more informed decisions.

6. Scalability: Hyperautomation can be easily scaled to accommodate changes in business needs and growth.

Hyperautomation Key Components

Hyperautomation is a comprehensive approach to automation that involves the use of multiple technologies and tools to automate complex business processes. The key components of hyperautomation include:

• Robotic Process Automation (RPA): RPA involves the use of software robots to automate rule-based, repetitive tasks. It can be used to automate processes such as data entry, data extraction, and data processing.

• Artificial Intelligence (AI) and Machine Learning (ML): AI and ML are used to create intelligent automation that can learn from past decisions and improve future outcomes. These technologies can be used to automate decision-making and to perform tasks such as natural language processing and image recognition.

• Process Mining: Process mining is a data-driven approach to process analysis that involves the use of algorithms to analyze event logs and identify process inefficiencies and opportunities for improvement.

• Analytics and Insights: Hyperautomation involves the use of real-time analytics and insights to enable better decision-making and to identify opportunities for process optimization.

• Low-code or no-code development: Low-code or no-code development tools are used to create custom applications and workflows without the need for extensive coding. This can help to speed up the automation process and make it more accessible to non-technical users.

• Cloud Computing: Cloud computing provides the infrastructure and resources needed to support hyperautomation, including storage, processing power, and collaboration tools.

 

Trends in Healthcare Automation that are Affecting the Future of Healthcare

Rapidly Evolved Patient Requirements

Patients expect 24/7 assistance for their health checks and diagnosis, which paved the way for automation and AI across the organization.

AI-infused Healthcare

To emphasize patient choice, healthcare organizations worldwide utilize AI to create a personalized experience.

Bridging the Healthcare-Technology Gap

RPA in healthcare and AI have proven to be the sure-shot solution to bridge the technology and healthcare gap.

Intelligent Apps

Apps with ML, and NLP can augment human activity to collect insights that lead to better virtual care, homecare and patient care

Smart Data Management

Application of RPA in healthcare leads to better management of electronic health records and share data access to patients, providers, and suppliers.

Internet of Things (IoT)

IoT leverages human-machine interaction with wireless trackers that help to perform and monitor real-time healthcare operations.