RPA for customer service – Benefits, use cases, and future scope

Going by the modern-day requirements, customers are constantly shifting their inclinations in every aspect, including 24/7 automated customer service. Sensing this prospect as an opportunity, companies have recently begun to follow a customer-centric approach, thus prioritizing the convenience and availability of multiple channels, like live chat, social media, and mobile apps, to receive high-quality customer service. Hence, this is a transformational swing from a completely manual approach to automation. However, organizations that fail to adapt to these changes risk losing customers who demand a seamless and efficient experience.

‘RPA for customer service’ helps companies meet these customer expectations by automating routine tasks, freeing human agents to focus on more higher-value tasks and providing a personal touch to customer interactions.

What is Robotic Process Automation?

Robotic Process Automation (RPA) is a software technology that deploys and manages software robots that mimic human actions when interacting with digital systems and software. Software robots, like humans, can understand what’s on a screen, complete the correct keystrokes, navigate systems, identify and extract data, and perform various defined actions. However, software robots can perform tasks faster and more consistently than humans without standing up, stretching, or taking a coffee break.

Why do you need RPA in customer service?

RPA (Robotic Process Automation) improves customer service by reducing administrative and back-office workload. Customer service is sped up by software robots that gather information and documents from various systems, handle service requests, and update customer records.

According to EdgeVerve, by using AssistEdge Engage, customers’ query resolution can be improved by 15%, thus proving the faster customer service it promises leading to a better customer experience. By utilizing robots to perform routine tasks such as processing customer service request changes, our clients were able to free up their customer service agents to focus on customer interaction rather than manual work. Implementing RPA also enabled new service models for their customers, which benefited other departments, such as the Finance Department.

Another primary reason for using RPA in customer service is that the traditional approach has some challenges, which become a tedious task for contact centers to confront manually.

Some key traditional customer service challenges are:

Benefits of customer service RPA

RPA customer service tools provide numerous advantages, the most notable of which are as follows:

Top RPA use cases in customer service

Assist customer service representatives: According to Salesforce research, 66% of customers expect service representatives to understand their specific expectations and needs, including what they have purchased. RPA bots solve this problem by retrieving the customer’s data, such as purchases, demographics, previous tickets, or complaints, and delivering it to customer service personnel ahead of time to help them research customers’ inquiries and resolve the issue quickly.

Make a customer account: In the business CRM database, each customer has a personal account that includes their customer ID, name, credit card information, contact information, and purchase history. Customers typically create an account by interacting with a customer service representative or a bot in a recorded chat.

The bot automatically extracts relevant information from recorded communications, fills out forms to create the customer account, validates the payment mode against bank information, and notifies customers of account details once they are finalized.

Control customer refunds: RPA bots extract customer refund inquiries from ticketing systems, emails, or texts and begin refund processes without human intervention. The bots also send notifications to the user via text or email when the refund is complete.

Resolve rule-based problems: RPA bots collect and store issue information in the ticket system. They connect to the ticket database and automatically resolve customer queries like the text to inform:

Refresh CRM data: RPA bots automatically pull data from business databases (for example, interactions with customer service employees, past purchases, documents and reports, cold calls, and emails) and update it into CRM data (for example, lead scoring, order history, contact history) with information from new texts, emails, filed reports, or online surveys.

An automated workflow for issue resolution is created based on the assigned priority level, and the customer is notified of the refund decision.

Future scope of RPA in customer service

Using RPA when gathering data reduces the possibility of human error, improving customer satisfaction. Hence, the use of RPA enhances customer service by minimizing the back-office and administrative tasks. The solution does accelerate customer service by collecting data from multiple systems, implementing service requests, and reviewing customers’ records simultaneously and quickly.

Thus, by automating mundane tasks, customer service firms can involve their representatives in some productive tasks, such as client engagement, rather than tedious processes. Therefore, automating customer services can simplify many complex business jobs, thus enhancing customer experience as a whole. Finally, the threat of human error is considerably lessened, resulting in elevated customer satisfaction. Considering all these benefits RPA can generate in customer service, it becomes imperative for companies to implement RPA in the times ahead.

How automation in financial services helps retain customers?

Process management can be time-consuming for finance teams, especially regarding issuing and processing invoices, keeping track of budgets, and creating contracts.

Any system that relies on manual data entry wholly in financial services is bound to give slow and inefficient results besides being prone to more human errors, thus leading to the loss of customers. This is where the role of automation in financial services becomes crucial.

What is RPA in Banking?

RPA is a technology where banks can stay competitive in a growing market by automating manual business procedures. Customers can utilize RPA banking to automatically process payments, deposits, withdrawals, and other transactions without requiring manual intervention. Thus, banks can replace many tedious human operations by combining automation and AI.

As technology advances, RPA takes on new extents in banking. People are increasingly turning to digital banking, cryptocurrency, and mobile payments. These are just a few examples of RPA in the banking industry. These digital transformation projects continue to be at the top of many banks’ priority lists and will continue to drive the overall technological growth of the banking process.

How are financial companies making use of RPA?

Manual data processing, particularly numbers, is prone to human error. This is not only time-consuming but also quite costly. RPA in finance is a practical solution to the problem as it helps automate finance and accounting processes and can handle data more efficiently than the human workforce while saving enormous amounts of money.

RPA in finance and accounting has progressed from simple individual automation tasks to processing full-fledged automated reports, data analysis, and forecasting while interacting with other technologies. Along with lowering human resource costs, handling large, recurring data-related tasks can be better utilized with a focus on more meaningful outputs.

Benefits of using automation in the financial services industry

Finance organizations can use RPA to automate business processes and tasks, such as accounts, with minimal human intervention. As a result, RPA is one of the most valuable tools for companies in the finance industry, where time and accuracy are critical.

Hence, let us take a look at some of the critical advantages of RPA implementation in finance:

RPA use cases in financial services

Purchase order processing: Employees can spend significant time processing purchase orders and routing them for approval for the company to acquire critical goods and services. One could program a software robot with intelligent (or “cognitive”) data capture tools to scan POs for essential information, enter it into the appropriate system, and set it to recheck them.

Invoice processing: Invoices, like POs, present a consistent challenge for back-office staff, especially when they arrive in various formats. This process, like PO processing, is easily handled by a robot. When data differ significantly from expected, or the robot’s confidence is at the lower end of the scale, it can flag an invoice for manual user review.

Basic accounts reconciliation: Comparing the balances on critical business accounts can consume valuable time in a clerk’s day, requiring them to log in and out of various systems. Accuracy is necessary to support future audits. Many of the essential elements of these tasks can be easily handled by RPA robots, with only human intervention required when data does not align as expected.

Improved financial reporting: It is critical to provide accurate information to the company’s decision-makers, and that information flow necessitates precise and detailed reporting. Employees can devote more time to producing the deep data insights required for making wise decisions now that robots handle more of the office’s mundane finance tasks.


While RPA can help with these tasks, some organizations may need help with their complexity or multi-step processes. Full-cycle accounts payable automation tools offer a more customized approach to these tasks. A thorough examination of these solutions for modernizing finance departments will reveal the best strategy for your organization.

Finally, financial companies can thus improve the customer experience while lowering costs and increasing efficiency by embracing RPA. Increased automation in banking and financial services combined with more efficient processes simplifies employees’ daily lives by allowing them to spend less time on tedious manual work and more time on profitable projects.

How can work insights drive efficiency & sustain future disruptions?

COVID-19 variants have thrown a twist in many organizations’ return-to-office work plans over the last two years or so. Nevertheless, employees and employers are again returning to the office environment with their new set of work plans, including implementing work insights in their daily activities to adapt themselves to the current scenario, the hybrid work model.

Thus, the hybrid model gained its prominence as a response to the pandemic. Many companies shifted their employees to work from home, rapidly accelerating the adoption of remote work. However, this sudden shift presented new challenges for companies, including managing remote employees, maintaining company culture, and ensuring that remote workers had the necessary resources to be productive.

The transition toward a hybrid work model

In due course of time, companies have also realized that remote work could be sustainable in the long term, with many beginning to embrace the hybrid model. Hence, this working style allows companies to take advantage of the benefits of remote work, such as reduced commute time and lower overhead costs, while maintaining some in-person interactions to foster collaboration and strengthen company culture. Additionally, the hybrid model can give employees more flexibility in their work arrangements and a better work-life balance.

Overall, the hybrid work model has become a popular solution for companies looking to balance the benefits of remote work with the help of in-person work and is likely to continue to be an essential part of the future of work.

Workflow and transition challenges faced by companies

However, the transition to a hybrid work model can also come up with some challenges for firms, such as managing remote employees, maintaining company culture, ensuring productivity and accountability, adapting to new security and privacy concerns, balancing the needs of remote and in-office workers, and addressing the technology issues.

Workflows give companies the structure and direction of their operations, guiding projects from planning to execution and completion. A workflow is a systematic roadmap that keeps operations running smoothly.

When workflows begin to fail — and teams begin to lose their way — the entire organization suffers. Projects fall behind schedule, costs escalate, and customers become dissatisfied and unaffected. Considering this, what workflow issues should businesses be aware of? And how can work insight solutions address these workflow challenges effectively?

Some of the most common workflow issues are:

Thus, this brings us down to the role of effective work insight solutions for addressing the workflow challenges due to the recent change in working style, especially the hybrid model.

Steps to initiate the transition to a hybrid work model

The first step to transitioning to a hybrid work model is gathering data and insights about the current work environment. This can be done by implementing workplace insights, which involve collecting and analyzing data about employee behavior, work patterns, and technology usage.

Workplace insights can provide an understanding of how employees are currently working and their challenges, which can inform the development of a successful hybrid work strategy.

By using workplace insight solutions, companies can make data-driven decisions about how to structure their hybrid work model and implement it to address the unique needs of their employees and their business.

Overall, gathering data and insights through workplace sights is a critical first step in the transition to a hybrid work model, as it provides a solid foundation for making informed decisions about the future of work.

So, getting acclimatized to the new working style and erasing the pandemic-induced workflow challenges will require well-planned strategies and work insights for quick adaptability, harmonious flow of businesses, and scaling efficiency.

Introducing work insights in a hybrid working environment

The onset of the pandemic and its repercussions have changed the dynamics of the working styles of companies, with many employees preferring the ‘work from home’ for diverse reasons. Consequently, it has become a primary concern for organizations to fix this changing mindset immediately. Thus, evaluating all the factors, many companies have chosen the hybrid working style.

However, to run the businesses profitably in this digital-first hybrid ecosystem, the leaders have to initiate new work analysis systems, namely, work insights. Hence, the launch of effective work insight solutions into the working system becomes imminent for the seamless functioning of businesses.

How do work insights enhance productivity and efficiency in a hybrid work setting?

With the hybrid working style taking center stage of late, companies have begun to implement work insight solutions to manage the business processes’ overall functioning. Thus, work insights, by applying Machine Learning (ML) and AI capabilities, captures the task-level communications data across many applications with smart data capture by implementing computer vision. One of the essential features of work insight solutions is its cost-effective supervising of desktop activity and metadata compilation of the applications used by employees.

It can also evaluate and produce activity reports for daily productivity, login time, work styles, business applications, and many more, besides providing secured and customized reports with undisclosed data on visually rich dashboards. The insights also enable firms to minimize the loopholes of the traditional approach of monitoring and assessing productivity. Hence, it allows organizations to measure workforce productivity with ease, along with unlocking training opportunities to boost performance and efficiency.

Finally, it extracts precise, customized, and near real-time insights to measure and set the yardstick for the team’s output across businesses for inculcating and driving a culture of transparency and quality.

Work insights extraction methods

Process discovery

Business process discovery represents an organization’s current business processes, including workflows, structures, and elements. These techniques use existing records, documentation, and technology systems that run business processes to extract process information.

Processes are omnipresent in a business, just as they are in our daily lives. Process discovery techniques enable companies to investigate the underlying structure of existing processes using data logs, employee insights, and other process documentation. They also capture task-level details and aid in identifying bottlenecks, deviations, and exceptions in day-to-day process workflows.

Optimization of workforce

Workforce optimization (WFO) uses data to improve employee and organizational efficiency while lowering operational costs. WFO affects every aspect of the business, from marketing to finance. The goal is to maximize results by streamlining processes between departments and employees.

It reduces costs, improves operational efficiency, increases productivity, maximizes technology investments, improves customer service, enables process automation, reduces error, and provides deep-dive insights into every aspect of an organization when fully implemented.

Data digitization

Digitization converts an analog process to a digital format without affecting the process.

Digitization enhances efficiency, protects your records in the event of a natural disaster, theft, or loss, and modifies record retrieval while modernizing your enterprise to current market standards. However, the chance of losing your records is high.

Workflow automation

Workflow automation ensures that tasks, documents, and information across work-related activities operate automatically and follows defined business rules. When implemented, this type of automation should be a simple process that is carried out regularly to increase overall productivity.

An organization must determine the job’s tasks to automate a workflow. It then develops the rules and logic that regulate how those tasks should be carried out. Finally, the software is programmed with predefined business rules and logic. Finally, the software uses those rules and logic to complete the series of tasks from start to finish, eliminating the need for humans to handle the job.

Role of work insights in accelerating end-to-end enterprise automation

Work insights are key in accelerating end-to-end enterprise automation in a hybrid work model. The insights can help organizations identify areas where automation can have the most significant impact, such as reducing manual tasks and increasing efficiency, and areas where additional process improvements may be needed. For example, work insights can show which manual processes take the most time, allowing organizations to prioritize their automation efforts in these areas.

Additionally, work insights can also be used to optimize remote work processes. For example, work insights can show which remote methods are the most time-consuming and which processes can be automated to improve efficiency. Also, work insights can be used to monitor remote worker productivity and engagement, helping organizations identify areas where additional training or support may be needed.

In hindsight, work insights can offer companies information that can help them optimize their processes, increase efficiency, and ultimately drive end-to-end enterprise automation in a hybrid work model.

Some other benefits of work insights besides Improved decision-making include:

The future of work insights

The pandemic has hit all industries very hard and has disrupted the workflow to such an extent that it needs to be urgently resurrected by implementing effective work insight solutions. Hence, going by the sense of urgency of such solutions, it is inevitable that the companies will be reliant on work insights in the times ahead as it will not only revive the pre-pandemic workflow but will also reach a level where the organization will be able to harvest benefits precisely and quickly. Therefore, it can be inferred that work insights are definitely the future.

How can automation software boost the contract data extraction process?

The contract data extraction process can be tedious and labor-intensive if done manually. From identifying the key terms to aggregating the necessary data points, this task can take up hours of an employee’s valuable time.

But what if you cut these hours significantly – even potentially down to taking minutes instead of days? Considering these challenges, contract automation software has revolutionized how companies approach their automation needs and is quickly becoming a top choice for teams looking to simplify the document analysis process.

Limitations of manual contract data extraction process

Manual contract data extraction can be labor-intensive and time-consuming, requiring a manual review of every document. This process is highly prone to human errors since it depends upon the ability of the user to interpret the words on each page accurately.

In addition, such manual processes are expensive as they require extensive staffing dedicated to reading and analyzing all the documents. This is especially problematic for companies that have large volumes of contracts to manage, such as those in the financial services industry.

Manual processes also need to improve their ability to extract valuable insights from data quickly. For example, while manual processes can provide an overview of a contract’s contents, they cannot easily access specific details within the document or uncover hidden data points. This can be challenging for organizations that need to review their contracts and extract pertinent information promptly and regularly.

Automated contract data extraction thus enables companies to quickly and accurately extract data from large volumes of documents with minimal human involvement.

The shift from manual to automated contract data extraction

Contracts are a critical part of any business transaction, and the ability to quickly extract data from them is essential. This process was largely manual in the past, requiring significant time and human effort. But as technology has advanced, automated contract data extraction has become more commonplace.

Contract automation software can quickly and accurately extract critical details from contracts, speeding up the process and freeing up humans to focus on other tasks. From reducing costs to increased accuracy, automated contract data extraction can provide businesses with a wide range of benefits.

Automated contract data extraction tools rely on sophisticated algorithms that quickly identify relevant information within documents. This allows businesses to swiftly extract dates, names, prices, and other critical contractual details. Moreover, this process can be done with high accuracy, reducing the risk of errors that can lead to costly disputes.

What is automated contract data extraction?

Automated contract data extraction is extracting critical information from documents, such as contracts, agreements, and other legal documents, using Machine Learning techniques to identify and remove specific terms and conditions. Through automated contract data extraction, businesses can quickly analyze large amounts of data for insights into pricing strategies, negotiations, and regulatory compliance issues, and more quickly.

Thus, by extracting relevant data points, businesses can gain valuable insights into their contractual agreements and improve efficiency in contract management processes. In addition, this type of analysis helps to identify areas of risk, financial implications, and other details that are essential for making informed decisions. Additionally, contract automation software provides the basis for automated workflows such as Artificial Intelligence (AI) based document management solutions that help organizations streamline their processes.

The contract data extraction process

The contract data extraction process involves extracting meaningful data from contractual documents. Here are a few steps to the contract data extraction process:

Data extraction techniques

One of the most common methods for data extraction is web scraping, which involves using automation software to collect data from websites. Web scraping allows users to automate collecting and extracting large amounts of data from public or private sources, such as news websites, online forums, and social media networks.

Data mining is another commonly used technique for data extraction. It involves applying algorithms to large datasets to identify patterns and extract new information. This kind of data extraction is used in many industries, such as marketing, finance, research and development, and healthcare. It allows users to quickly identify trends and gain insights from the data collected.

Natural language processing (NLP) is another technique for extracting meaningful information from text-based resources. NLP is used to analyze unstructured text and extract meaningful information, allowing users to understand the context of a given text. For example, it can process customer reviews and comments, emails, tweets, blog posts, and other text-based data sources.

Finally, API (Application Programming Interface) is an increasingly popular method for extracting data. It enables developers to access specific types of information from external sources, such as websites, databases, and software systems.

Parameters when selecting a contract data extract software or solution

When selecting a contract data extract software or solution, several key parameters must be considered. These include accuracy and precision, usability, scalability, and cost-effectiveness.

Benefits of automated data extraction

The future of automated data extraction

The emergence of automated data extraction technology has changed the game, allowing companies to automate the data extraction processs and produce more accurate results in a fraction of the time. AI is leading this revolution, providing powerful tools that can quickly scan large datasets and extract relevant information with minimal human intervention. It promises to revolutionize how businesses manage their data, providing access to valuable insights that can be used to inform decisions and unlock new opportunities. Contract automation software also has the potential to increase efficiency by freeing up employees from manual responsibilities and allowing them to focus on more meaningful work.

A complete overview of document processing solutions

Document management can be one of the most tedious, time-consuming processes, especially with a plethora of documents mounting every passing day. However, as automated solutions evolve now and then, organizations across industries can take some respite from tiresome tasks by using document processing solutions to automate and streamline their document-related functions so they can stay productive and successful.

Let us discuss the diverse aspects of document processing systems, including traditional document processing, to gain better insight into how this technology can help make our daily workflows smoother and less complicated.

Loopholes in traditional document processing and how to overcome them?

Traditional document processing systems have several drawbacks. For one, they can be inefficient and expensive to maintain. Many manual processes involve document processing, such as scanning, data entry, routing, and filing, leading to longer lead times and higher costs. In addition, these systems often require specialized staff and additional resources for setup and maintenance.

Furthermore, document processing solutions can be prone to errors due to manual processes and human intervention. For example, data entry mistakes and incorrect filing can lead to lost documents or inaccurate information. Finally, traditional document processing solutions lack effective security measures, leaving them vulnerable to unauthorized access or manipulation of critical data.

Document automation provides organizations with an automated platform for streamlining the process of document creation and management. This system allows users to create sophisticated documents quickly and easily in an organized, secure environment. In addition, it offers several features that make it simpler and faster for users to create documents, such as templates, auto-fill capabilities, document validation checks, and automated reporting.

Furthermore, document automation (DA) can integrate with existing systems and databases to ensure accurate data capture. By using document automation systems, businesses can cut the time and costs associated with document processing and ensure that their documents are accurate and up-to-date. Thus, these shortcomings in traditional processing compel firms to move toward document processing solutions.

What is a document processing solution?

A document processing platform is an automated system that simplifies the data capture, analysis, and management of business documents. It can help businesses to save time, reduce costs, and increase efficiency by automating manual tasks such as data entry, digitalizing paper documents, and storing important information. It helps organizations quickly recognize, classify, and extract key information from documents. As a result, this technology can streamline workflows, reduce manual data entry errors, and improve overall accuracy.

Document processing solutions are computer-based systems that leverage artificial intelligence (AI) algorithms to read, understand, and process documents. They can be used with various document types, such as invoices, bills, contracts, and forms.

Document processing systems are valuable tools that help organizations save time, save money, and increase efficiency. Furthermore, they can help organizations improve compliance and reduce the risk of errors in their document-intensive operations.

Effective strategies to apply a document processing solution

When preparing to implement a document processing solution, it is important to consider how it can be implemented and managed successfully. Here are some effective strategies for getting started:

Hence, by following these maintenance routines, companies can guarantee a successful and effective execution of their document processing systems.

How do document processing solutions work?

Document processing tools use various techniques to extract data accurately and quickly from documents. These techniques include Optical Character Recognition (OCR), Natural Language Processing (NLP), Machine Learning, and more. For example, OCR allows computers to recognize text in an image format. NLP enables computers to understand the meaning of words in a sentence.

Machine Learning algorithms can train computers to recognize patterns in data and decision-making. These technologies help document processing solutions automate manually entering data from documents into a computer system. Document processing solutions can also securely store documents, ensuring only authorized personnel can access them.

Furthermore, they are often integrated with other software applications, such as customer relationship management systems, to provide valuable insights into documents. Document processing solutions can also detect fraud or identify patterns in data that might otherwise go unnoticed.

Top benefits of document processing solutions

What is the future of document processing solutions?

As businesses and organizations adapt to ever-shifting customer needs and demands, document processing solutions can adjust quickly without sacrificing accuracy or performance. This means that they can provide a reliable and efficient solution to document processing that can be relied upon for the long term.

Furthermore, these solutions are becoming increasingly sophisticated. With technological advances, document processing solutions are being developed with features such as automated workflow management and artificial intelligence capabilities to help streamline processes even further.

With the ever-evolving nature of technology, these solutions are becoming more advanced and can provide a reliable and efficient document processing method for businesses, organizations, and individuals. Hence, data processing solutions are more important than ever in a rapidly developing world of technology. It is an easy-to-use platform that can be accessed from anywhere worldwide.

How does intelligent supply chain management boost goods & logistics visibility?

The success of any business venture relies heavily on efficient supply chain management systems. Hence, it becomes imperative for companies to streamline the processes of receiving, storing, and distributing goods for businesses to run smoothly and fulfill customer demands efficiently. However, this can only be done by increasing the visibility within the supply chain, thus enabling easy identification of roadblocks such as stock-outs or bottlenecking so that resources can be managed appropriately, and the latest solutions implemented quickly. Therefore, to overcome these shortcomings and gain the necessary insights, it is high time that organizations should start harnessing intelligent supply chain management platforms.

Hence, let us look at how supply chain visibility can help companies track & monitor goods more efficiently than ever before so they can reap maximum benefits from their logistics operations.

Supply chain visibility & intelligent supply chain management

Supply chain visibility tracks and monitors products in real-time, from raw materials through production, distribution, and delivery. It gives organizations greater control over their supply chains by giving them insight into where products are located at any given time and the ability to monitor quality, stock levels, and delivery times.

With this information, companies can optimize inventory management and ensure the timely delivery of goods. An intelligent supply chain management system is a new approach to managing the end-to-end flow of goods throughout an organization’s entire value chain. It utilizes Artificial Intelligence (AI) technology to automate many aspects of the supply chain process, such as tracking shipments and monitoring performance metrics. This allows organizations to gain better insights into their supply chain operations and improve efficiency and accuracy.

Why is intelligent supply chain management important?

Intelligent supply chain management is important to maximize profits and minimize costs. By utilizing modern data technologies, businesses can make informed decisions about the movement of goods and services, resulting in increased efficiency and accuracy.

Furthermore, intelligent supply chain management allows companies to optimize inventory levels, reduce delivery times, increase customer satisfaction, and improve demand forecasting. Ultimately, these benefits lead to greater profitability and a competitive edge in the marketplace.

Goods & logistics visibility challenges in the supply chain

Achieving goods and logistics visibility in the supply chain can be daunting. Companies that need help with this issue often need several challenges, including data accuracy and reliability, inadequate visibility into inventory levels, high operational costs, and limited access to timely information.

Data accuracy and reliability are among the biggest challenges for businesses regarding goods and logistics visibility. Poorly maintained databases or inaccurate manual processes can lead to discrepancies in product information or incorrect stock levels that cause delays in shipments or delivery times.

To ensure reliable data throughout the supply chain, companies should invest in robust IT systems such as enterprise resource planning (ERP) software to provide consistent, up-to-date information about their inventory levels. Inadequate visibility into inventory levels can also present a challenge for companies. With an accurate overview of their current stock, companies can make informed decisions about production scheduling and shipping processes.

Businesses should look into implementing real-time tracking systems that provide complete visibility into inventory movement throughout the supply chain. High operational costs are another challenge faced by companies striving for goods and logistics visibility in the supply chain.

Implementation of intelligent supply chain management

Intelligent supply chain management is a modern approach to managing the flow of goods and services that relies on artificial intelligence (AI) technologies such as machine learning and deep learning. This system can help streamline processes, increase accuracy and efficiency in decision-making, optimize inventory levels, reduce costs, and improve customer service.

The implementation process starts with identifying the business needs it can address. Once these needs are identified, they must be matched with suitable AI solutions such as natural language processing (NLP), robotics automation, or predictive analytics.

Next, it is crucial to select the right AI platform or vendor to deploy it. This should include their capabilities, experience in the field, and customer reviews. Once a suitable venue is chosen, it must be integrated with existing systems such as ERP software and supply chain management applications.

Intelligent supply chain management approach & solutions

An intelligent supply chain management platform enables business leaders to gain visibility into their entire supply chain network, from sourcing raw materials to making better-informed decisions about managing their resources. This approach can help businesses reduce costs and optimize their processes for increased profitability.

Some standard solutions in intelligent supply chain management include predictive analytics, prescriptive analytics, big data analytics, and machine learning. For example, predictive analytics enable companies to identify potential issues before they occur, giving them time to take preventative measures or adjust their strategy accordingly.

Prescriptive analytics provide guidance on which decisions will likely be the most beneficial for a company. Big data analytics allow businesses to access and use vast amounts of data from their supply chain network to gain insights into performance and improve decision-making. Finally, Machine Learning can automate specific tasks within the supply chain, allowing for greater efficiency and accuracy. By utilizing these technologies and solutions, companies can create an intelligent supply chain with the right tools to maximize their operations.

6 ways to increase supply chain visibility

Supply chain visibility is essential for businesses to keep track of their operations and make decisions with confidence. Here are six ways you can improve visibility in your supply chain:

10 benefits of intelligent supply chain management

Revolutionize supply chains with TradeEdge Network

The TradeEdge Network is an intelligent supply chain management platform that brings together the key players in the global supply chain. Connecting manufacturers, shippers, suppliers, and buyers to a single digital platform helps them optimize their processes, reduce costs, and increase efficiency.

The platform also provides intelligent insights into market trends and improves visibility across the entire network.

With the TradeEdge, companies can make smarter decisions about their supply chain and streamline operations. It also helps them increase customer satisfaction by providing better service and faster delivery times.

Intelligent Business Automation: A framework for combining RPA and AI

Intelligent business automation (IBA) is one of the high points of modern-day technologies that swiftly and accurately serve contemporary business needs. However, the foremost challenge currently is the large volume of unstructured and semi-structured data requiring manual intervention in the absence of automation. This is where the role of IBA solutions becomes crucial due to its ability to segregate unstructured data from a humongous set of data information. Hence, it is always advisable to implement such tools and solutions to drive efficient practices in the workspace. Failing this will only result in wasted human work hours and sluggish progress.

The best part of IBA is that Artificial intelligence (AI) is now stretching the scope of Robotic Process Automation (RPA) with the help of IBA. This is achieved by implementing innovative algorithms that deploy predictive analytics to make better-informed decisions. Moreover, repetitive mundane tasks can be undertaken with an infusion of intuitive and actionable insights, yielding intelligent business process automation.

What is Intelligent Business Automation?

Intelligent Business Automation harnesses the combined potential of RPA, AI, and even intelligent OCR. Be it business process management workflows or handling (un)structured data, IBA relies on the unified information made available across various platforms. This ensures the combined effect of vital details and information accessible to all stakeholders. Furthermore, decision-making engines apply algorithm-based logic to extrapolate existing data and identify potential trends for improving processes.

Such enhancements also ensure sustainable development for future process flows. Consequently, this leads to quicker and more versatile operability for the masses and ergonomic interfaces, augmenting planning for streamlined commercial management. Automation in business intelligence is the future of how companies transact and operate. Gartner predicted that by 2025, 70% of organizations would incorporate structured infrastructure automation for flexible and efficient workflows, increasing by 50% from 20% in 2021.

Why do businesses need IBA?

As IBA can boost productivity with greater capacity or volume to process data, this can enable businesses to meet fluctuating and unexpected demand with a consistently stable yet dynamically capable supply. However, it also drives the main factor of operational contingency by adapting to various factors, favoring and prioritizing those that matter the most.

Significant impacting variables include the ability to drive commercially affiliated processes, including production and profitable exchanges through intelligence-based information. For instance, demand sensing comes from AI-based algorithmic computation to detect demand signals. Thus, an intelligent business automation platform offers a seamless flow of businesses by encompassing everything they need in one unified platform.

Strategies to implement IBA

IBA mainly depends on RPA and AI, which are the backbone for automating processes. Therefore, the method includes robust automatable frameworks where AI can be easily implemented. Moreover, various system formats must share common or universal compatibility and support to ensure a seamless transition.

Hence, AI is more focused on analyzing the needful with an intelligent digital outlook, whereas Robotic Process Automation capitalizes on this with an added measure of repetitive task assignability. This fulfills all monotonous yet essential tasks, such as data collation (promoting proactively reactive remedial actions). As per a survey by Gartner, only 50% of enterprises will develop skills for infrastructure automation across hybrid and multi-cloud platforms’, an increase of 40% from less than 10% in 2021.

Top 5 ways IBA combines AI and RPA to increase business efficiency

The synergistic effect of two or more factors tends to have a greater than combined overall efficacy. These streamlined and accurate plus productive results are achieved using a variety of methods, including:

What are the challenges of Intelligent Business Automation adoption?

Key use cases of IBA

IBA trends and future scope

More and more businesses may accept AI & RPA-based IBA within their operations. Whether healthcare or financial or banking vendors, everyone is gearing up to revolutionize and automate their vital processes to deliver better service to consumers. In addition, data-based algorithms will power low or no-code platforms, be it within the retail or wholesale industries.

When implementing a new strategy in business, companies should always be mindful of the implications on existing processes to enrich and not impede them in any way. Hence, firms need to plan what needs improvement, how this is intended, which objectives we must fulfill, by when (with set feasible timelines), where it needs to be applied, and why. Once they have clarity, they can then proceed with the implementation accordingly. Hence, these will ensure that the ideal version of IBA is constantly rolled out across any business to automate its processes.

Implementing supply chain planning software to boost businesses

The supply chain industry has undergone a sea of changes in recent years. The COVID-19 pandemic has disrupted the local, national, and global supply chain. The disruptions have forced businesses to review their supply chain process and make necessary changes to manage the chaos and streamline operations.

As a result, large businesses are shifting to new-age technologies to manage their supply chain challenges to usher in a positive change. To get a better perspective, let us discuss the supply chain, supply chain planning, challenges, and the top benefits of implementing supply chain planning software in business processes.

What is demand planning?

Demand planning, a critical step in supply chain planning, predicts the demand for a service or a product so it can be manufactured accordingly and delivered to the customers more efficiently, thus increasing their satisfaction.

Thus, supply chain planning and execution are the two main factors of supply chain management, as the planning of SCM naturally begins with demand planning. This process involves laying down a demand plan based on data estimate, which considers the factors that can impact inventory planning and marketing and mentions where to distribute products to meet the anticipated demand.

Thus, demand planning is associated with the subsequent steps within the supply chain process: production planning and material requirements planning (MRP). Following this, once the product is produced, it joins the execution phases of SCM, involving warehouse and transportation management.

What are the current supply chain challenges?

The supply chain industry is wrestling with two diverse challenges. First, age-old traditional obstacles and the new disruptions brought in by Covid-19 have compelled the supply chain industry to rethink introducing new solutions. So, let us look at the top supply chain challenges.

What are the top supply chain management strategies?

Supply chain disruption is a significant threat to businesses but can also be a potential opportunity. Considering the global economic crisis, evolving customer needs, and changing customer expectations, companies have no choice but to take a strong guard to prevent their supply chain and network from every fatal consequence. Here are the top supply chain disruption management strategies.

Benefits of supply chain management solution

A competent supply chain management helps manage the delivery of goods from producers of raw materials through manufacturers and eventually down to end users. As it touches upon the most critical aspects of product manufacturing, procurement, supply, and sales, it influences businesses across industries. A fragile or confident supply chain is instrumental in deciding the production process, cost, and profitability.

So, let us discuss and find out how supply chain management software impact businesses across industries.

Optimizes distribution costs: The supply chain has a significant influence on the distribution cost. A cost-efficient, well-performing supply chain can reduce overhead and direct sales simultaneously. Supply chain businesses have started leveraging technology-advanced solutions to optimize their route and navigation system to help their drivers and carriers deliver faster and positively impact the order-processing and purchasing of the customers. Optimizing distribution costs helps in lowering any additional costs and boosts overall profitability.

More advanced inventory management: With a competent supply chain, businesses can keep a tab on their inventory and manage it better. Unfortunately, most large-scale companies and enterprises struggle to manage inventory and often overspend on storage and transportation. A seamless supply chain management can help businesses reduce their list by monitoring real-time data, keeping unnecessary inventory expenses in check, and improving the company’s profit.

Level-up end-to-end operation: Competent supply chain management keeps a tab on all the shipments from production point to delivery at various locations, such as warehouses, distribution centers, and retail stores. The constant tracking helps businesses prevent theft, minimize loss, and ensure quick delivery to improve customer experience.

Improves supplier and customer integration: Supply chain dynamics and the flawless relationship between the supplier and customer are the two critical areas where every manufacturer wants to excel. Next, supply and delivery- the two last miles of supply chain management- must be organized and seamless. Enterprises are using a suite of technology solutions to automate order processing, accumulate enough customer data, and track the delivery points to improve supplier and customer integration.

Role of technology in supply chain management

The introduction of technology can help supply chain businesses leapfrog and overcome thousands of barriers. Here are the top benefits of embracing technology in supply chain risk management.

Final thoughts

The most important benefit of implementing supply chain planning software in processes is that it eliminates the need for manual processes, saving time, money, and resources while increasing overall efficiency. In return, it helps businesses to streamline their operations, improve customer service, increase revenue, and reduce costs. And when the supply chain management hassle is overcome, management can focus on other vital factors of business to spearhead it toward growth and success.

The role of modern technologies in shaping the future of supply chains

The global network of supply chains has been facing numerous disruptions since the onset of the pandemic in 2020. However, the tumultuous year presented opportunities for digitally native brands in the guise of increasing preference for online retail, a sudden shift in consumer choices, pent-up demand, and amassed savings. Unfortunately, the future of the supply chain still looked bleak and presented more challenges for e-commerce retail and others. Moreover, the problems continued in 2022 with material scarcity, hefty freight charges, effective demand forecasting, geopolitical situations, and rising inflation.

Enterprises are thus ramping up their digital transformation initiatives and bringing new technologies like Automation and AI into their existing processes. They are also reconsidering automated solutions to improve their supply chains. From this point, it is apparent that the future of the supply chain will be more tech-enabled and data-driven.

Key disruptors changing the future of supply chains

Supply chains have always been vulnerable to market disruptions. The current geopolitical situations, economic meltdown, and rising inflation are major disruptors impacting the seamless upstream and downstream movements of goods in the network.

Therefore, the rising complexity and diversity of global supply chains require smart management tools and new techniques, from digital alerting systems to tracking potential disruptive events that would help enterprises focus more on high-risk regions and suppliers. Timely updates of possible challenges would eventually help boost supply chain resilience, agility, and sustainability. These three elements are needed to create a future-proof supply chain network.

How do advanced technologies improve supply chains?

As stated earlier, enterprises worldwide resort to new technologies to improve their demand-supply value chains.

For instance, one of the leading sports merchandising clients leveraged EdgeVerve’s TradeEdge, a supply chain management platform, to restructure its Point of Sale (POS) Business Intelligence system and extract accurate insights for its retailers’ POS and inventory position. The client also wanted to boost the market share, collaboration, and efficiency that would enable it to obtain real-time and reliable data for correct decision-making.

Although the client has a network of many intermediaries like distributors, wholesalers, and retailers, it had to face its fair share of challenges with its own point-of-sale (POS) system. It was affecting its real-time visibility into sales performance across regions and products. However, after applying TradeEdge, the client was able to improve its demand visibility by 60% with near real-time harmonized data.

The role of supply chain automation in reshaping the future

Supply chain automation refers to the process of implementing tech-enabled digital solutions to build connections in the network and streamline supply chain operations for higher efficiency. Artificial Intelligence, Machine Learning, Blockchain, and Automation are some advanced technologies leveraged to create resilient and agile networks. Automation possibilities are being explored at every step in the supply chain to create an autonomous channel. With technical and manual operations automated, employees focus more on decision-making tasks. That reduces dependency on the human workforce, and its resultant outcome is seen in eliminating human errors, cost, and time.

For example, robotics, automation, and 3D printing are implemented in manufacturing to reduce production schedules. Further, digitized platforms facilitate quick data distribution between partners, offering real-time visibility into the supply chain. And software upgrades with integrated AI, Automation, and the Internet of Things improve the productivity of workforces.

As mentioned earlier, modern technologies are being increasingly explored and implemented to change the future of the supply chain. That being said, let’s dive deeper into supply chain automation’s benefits and use cases.

Automated supply chain network – Benefits and use cases

Supply chain automation has benefitted enterprises greatly and improved operations at each stage. The following advantages will help us understand how technology’s power effectively revamps the supply chain’s future.

Accelerated manual tasks: A connected supply chain powered by Automation and AI has spared employees from the burden of manual, long-haul recurring tasks. With eliminated manual tasks, the chances of human error, labor, and operational costs, cycle time, poor inventory, and warehouse management are greatly reduced.

Transparency of operations: Improved transparency of network operations provides greater visibility of processes and orchestration. It eliminates siloes and system gaps, fostering an end-to-end connected solution so enterprises can get real-time updates about possible bottlenecks and remediate situations to ensure business continuity. Further, product traceability is possible, which is essential for higher customer satisfaction.

Responsive and agile networks: With the advent of low-code automation, enterprises can bridge the connectivity gap in the network and access information about unforeseen events before time. This allows them to quickly respond to market changes and remediate any possible disruptions in the future.

Better adherence to regulations: Global manufacturers and suppliers often struggle to meet different regulations and adopt best business practices. Documenting and automating workflows using low-code automation platforms enable quicker identification and resolution of issues before further escalation.

As per market research, nearly 50% of international organizations will use AI, analytics, and IoT solutions in their supply chains by 2023. Advanced technologies are leveraged to gain an edge over market competition. With the power of automation and others, enterprises can increase the resiliency and responsiveness of their supply chain network to stay competitive.

Here are a few top-notch use cases in the supply chain that stand to benefit from the power of modern tech-enabled solutions.

Inventory management: The primary use case of supply chain automation is to ensure enough inventory is available in stock to meet customer demand. To support customer demand, enterprises use AI and other capabilities of automation to improve their demand forecasting accuracy for different product groups. Further, real-time inventory-level monitoring, inventory expiration date tracking, and inventory location identification are other critical functionalities of supply chain automation.

Sales order management: Automated order aggregation from multi-channels, order confirmation, order routing, order delivery time estimation, and tracking of order fulfillment are some critical functionalities of supply chain automation.

Supplier relationship management: Automation is the key to improving the future of the supply chain by enabling enterprises to carefully select their vendors, ensure compliance rules are met, and monitor their performance in real-time. Further, it can pre-define the routing of purchase orders for approval.

Logistics management: Automation empowers logistics and enterprises to create vehicle routes, track their movement in real-time and monitor the condition of transported goods using smart sensors.


Automation and other technologies have transformed businesses’ operations in the increasingly volatile market. With geopolitical conditions, severe weather changes, rising costs, and labor shortages disrupting every industry, it is evident that Automation and other added capabilities are needed to improve the future of supply chains. This is much needed to ensure operations continuity and address frequent customer demands shifts.

The top use cases and benefits of IDP in insurance

The post-pandemic period has been a challenging time for the insurance sector. They had to speed up digital transformation initiatives to provide service access to more customers. Customer centricity has been the highlighted factor for insurers to evolve as changed customers demand increased the importance of digitization, accessibility, flexibility, and individualization. New methods of communication, staff augmentation, and insurers’ intermediary models are some of the significant changes that occurred at the insurer’s end to keep up. Hence, solutions like Intelligent Document Processing in insurance became integral to the transformation.

IDP in insurance is the key to initiating digitization, and providers are fast embracing this smart solution to stay competitive in the market.

What is Intelligent Document Processing (IDP)?

Data is crucial for any sector. For insurance providers, customer data plays a significant role while processing new insurance covers. Unfortunately, customers’ personal information is usually scattered in unstructured or semi-structured documents. Manual extraction of such data from records of varying formats and domains only adds to the operational time. To speed up the process and provide services to more customers in a limited time, insurers need an intelligent automated solution.

Intelligent Document Processing is the right tool to get started as it converts unstructured/semi-structured data into structured and usable information. It speeds up the long-haul process of document processing and data extraction, allowing insurers to serve more customers at the same time. IDP in insurance provides end-to-end automation solutions to document-centric businesses, like banks and insurance companies.

Evaluating the importance of IDP in insurance

According to a claims digital study by Gartner, 60 to 70% in the US are of the view that they are reasonably implementing some of the latest digital tools, which presents enables them to process the claims quickly. Hence, companies have been using them, but they need to be applied significantly to make faster decisions and solutions.

But only a fraction of this data proves valuable for the insurers to make decisions. Since every step in the insurance process is data-centric, primarily depending on documents, insurers face a tremendous challenge in extracting the exact information from handwritten documents, PDFs, or images. Given the heavy volume of customer documents to process and mine for data, the task gets more difficult, long-haul, and laborious. With more documents to map, resources find it hard to keep up and eventually delay the entire process. And the chances of missed information or errors while picking the data are high. Hence, a manual approach to data mining in the insurance sector only adds to operational inefficiencies.

For insurance companies to effectively meet customer demands, IDP solutions can be the most valuable resource. IDP for insurance claims leverages the power of AI and Automation to reduce the claim process time and speed up data extraction so more claims can be processed in lesser time than before. IDP uses advanced technology to quickly identify different types of documents and intelligently extract the required insights from unstructured datasets. It allows employees to focus more on improving customer relations while letting the tech-enabled platform cater to recurring tasks.

How does IDP in insurance work?

Typically, an Intelligent Document Processing platform comprises four stages – Ingest and digitize complex documents, extract data with contextual information, analyze data for insights, and convert data into machine-readable information. However, a lot is happening between the above-mentioned four stages. Here’s a breakdown of all those functions that occur in the background to provide what enterprises expect from an IDP software solution.

How to implement an effective IDP strategy in insurance

Implementing an IDP solution into your existing process without proper planning and strategy can result in the ultimate failure and loss in terms of investment returns. Further, poor planning and implementation can cause a steep learning curve for employees, creating hindrances in the seamless flow of processes. To prevent such unpleasant occurrences, enterprises need to adopt a comprehensive strategy, which is divided into the following five steps:

IDP in insurance – Benefits and use cases

As per statistics, by 2030, innovative technologies like AI and Automation will replace an estimated 46% of insurance claims and policy processing jobs. Therefore, insurance companies need to embrace the change as soon as possible for competitive advantage in the saturated market. And they have valid reasons to do so, namely:

IDP solutions can benefit insurance providers in a great many ways, but ideally, there are six prominent use cases for scaling success with IDP implementation.

Contract and claims form processing: IDP solutions reduce the frequency of document handling to prevent loss or contamination of data. IDP auto-extracts various details from contracts and claim form submissions and stores them in contract management systems to allow centralized online access. Further, documents received by fax and snail mail are digitized for future access and processing.

Claims processing: Claim forms are digitized for auto-review and auto-categorized and stored in a shared centralized repository for further processing.

Document verification and KYC: Intelligent Document Processing in insurance allow employees to read and extract data from identification documents submitted by customers. IDP scales up customer onboarding and processing of claim applications faster.

Customer onboarding: Extracting customer-related data, such as insured name, company, industry, and beneficiaries, is catered by intelligent document tools to expedite the onboarding process.

Risk profiling: IDP solution can be used along with RPA software bots to collect data from internal databases and third-party websites. IDP and RPA automate the whole process and generate customer risk scores in less time.

Invoice generation: IDP platforms allow auto-ingestion of data from spreadsheets and integration of the same in the enterprise ERP system. IDP maintains records of previous invoices, auto-generates new invoices, and shares them with concerned parties via email.

The future of IDP in insurance

The demand for Intelligent Document Processing is surging, not just in the insurance sector but in others as well. There are two critical drivers for IDP market growth, namely:

Hence, the future of IDP in insurance is likely to be dominated by more cloud-based solutions, advanced Machine Learning capabilities, enhanced features supporting multi-language and handwriting recognition and signature detection, and connected enterprise automation solutions.

IDP in insurance has proved to be a game-changer for the whole sector. With added capabilities, Intelligent Document Processing is set to transform document-heavy business processes and scale enterprise automation journey.