How TradeEdge increases supply chain visibility for clients enabling them to make data-driven, customer-centric decisions

The explosion in disposable income among the middle class has converted emerging markets into a goldmine of business opportunities for many global brands. However, more opportunity translates into more competition and other challenges at a micro-level, one of them being the lack of supply chain visibility.

What is Supply Chain Visibility, and Why is it Important?

Visibility into the existing supply chain system offers an end-to-end real-time view of the company’s logistics, inventory, warehouse, and distribution networks.

The traditional model of retail distribution in emerging markets, not initially disrupted by new-age technology, is increasingly facing competition from tech-driven direct-to-consumer models. In addition, online-only stores are fiercely competing with the traditional supply chain network to align with changing customer preferences. Hence, survival has become challenging for businesses that were too slow to predict and adapt to the change.

This is why real-time supply chain visibility is so important.

In this article, we will explore the various supply chain network complexities persisting in emerging markets regarding the beverage industry. Also, we will identify different ways supply chain visibility can help address those challenges.

How Absence of Supply Chain Visibility Adversely Affects Production Planning & Growth

The global beverage market, both alcoholic and non-alcoholic, is estimated to reach USD 1.8 trillion by 2024. Since 2017 the Asia Pacific has gradually emerged as one of the largest global markets for spirits, closely followed by Brazil, the Middle East, and North Africa. Experts believe the next leg of growth for beverage companies will come from emerging markets.

However, the changing customer preferences, idiosyncrasies of local culture and practices, disconnected logistics, and absence of supply chain visibility give rise to the new set of challenges that global beverage companies were unprepared for.

Here, we will touch on the challenges in beverage distribution:

No collaboration; poor production planning

Most business is done through personal relationships and back-of-the-envelope calculations in an emerging market. Hence, most distributors fail to provide real-time data on their sales/stocks. When many breweries/factories and multiple distributors were thrown in the mix, the existing problems for businesses compounded. And new challenges cropped up. Poor supply chain visibility makes timely and consistent viewing of inventory and secondary sales information a challenging task for the entire supply chain. Hence, making informed decisions is impossible, resulting in sub-optimal replenishments, stock-outs, and inefficient production planning.

Low tech maturity; poor sales visibility

Low tech maturity in emerging markets is a major concern for global companies. The paper-pen approach to conducting day-to-day business, emails for closures, and spreadsheets for reports create a huge technological gap between the center and its subsidiaries.

Poor supply chain visibility, absence of structured mechanisms for collecting and processing data, and manual processes present contaminated data, poor sales visibility, and incorrect future predictions.

Lack of data; no wider market reach

Developing markets mostly have distributors concentrated only in urban areas. Poor logistics and poor supply chain visibility in rural markets restrict wider market reach for beverage companies. Hence, proper planning of promotions and discounts and communicating the same to all vendors across the market remain a distant dream.

In the absence of a robust platform for small retailers to proactively place orders and distributors to communicate effectively with their customers, the overall market reach for any beverage company will automatically shrink.

Lack of consumer data; poor effective promotion

In-store purchases form nearly 70% of all buying decisions, and most are done on an impulse by the consumer. Hence, a traditional distributor-driven retail model with little or no influence on the consumers’ buying decisions rarely provides valid, actionable data.

Promotion planning and execution remain ineffective without considering stock availability, customer purchase pattern, and other related insights. Hence, possibilities of cross-sell/up-sell remain outside the planning process.

How Supply Chain Visibility is the Best Way Out?

Only technology can bring manufacturers, distributors, retailers, suppliers, and other stakeholders on the same platform. This will not only help the concerned parties gain more visibility into the supply chain network, but it will also help them make crucial data-driven decisions and forecast future sales effectively.

This vision is achievable following a collaborative and inclusive ecosystem connecting every stakeholder across the supply chain. Let’s elucidate:

Distributor collaboration using data

A collaborative data exchange platform connecting all distributors enables easy data exchange between parties. However, one should consider the following points while setting up a logistics visibility platform:

Retailer outreach via value creation

Building an all-inclusive distributor-retailer connected platform translates store sales data into insight-driven refills. This creates an ecosystem where retailers receive best-in-class distribution services.

To ensure the complete success of this platform, the following features should be considered:

The Best Way Forward

Getting your supply chain ecosystem digitized will increase the supply chain and distribution ecosystem’s visibility and help you make profitable decisions at every step. TradeEdge is user-friendly and easily matches up to varying requisites of businesses.

Benefits of Supply Chain Visibility Software:


Data is paramount in gaining a competitive advantage in the emerging market. However, a lack of supply chain visibility and low-tech maturity impair businesses from capturing insights and making data-driven decisions. Hence, increased visibility via AI-based supply chain management can help businesses easily optimize production, improve distribution, and increase sales.

TradeEdge Enables Insightful Demand Planning – How?

The consequences of poor demand planning and management failures have risen with shrinking product life cycles. As a result, companies are implementing a new level of demand management technology to boost profit margins by better accounting for supply chain variability.

According to research, 50% of businesses claim it takes more than a month to detect changes in consumer demand. In today’s fast-paced business world, this is unacceptable. Improved demand sensing and shaping procedures offer considerable opportunities for organizations to increase top-line sales, profit margins, and inventory levels.

Demand Planning Vs. Forecasting

Demand planning is the technique of precisely forecasting and ordering items to have the right number of sales at any given time. In addition to preventing out-of-stock situations, accurate demand planning can help businesses develop beyond their wildest dreams.

Demand forecasting is an essential component of a trustworthy plan. The technique of demand planning, which considers previous results, considers external and internal changes, and puts out what can be anticipated based on the parameters, is the reason it can reliably predict the future.

Benefits of Demand Planning

What Hinders Demand Planning for Businesses

Demand planning relies heavily on data, the absence of which can hinder the entire planning and forecasting process. However, data is available everywhere but in an unstructured format, which, when processed properly, can help businesses with insightful decision-making and stay ahead in the competition.

Data-related challenges that hinder informed decision-making:

Data-Related Challenges Impact Businesses Significantly

Data-related challenges not only hinder proper demand planning and efficient data-driven decision-making, but also impact businesses negatively. Lack of structured data prevents stakeholders from measuring sales vs. targets needed for efficient demand planning and forecasting. From partner onboarding to higher Non-productive inventory, among data-related challenges significantly impact businesses.

Let’s elucidate with a case study on one of EdgeVerve’s clients – a multi-billion-dollar consumer goods enterprise.

The client was looking to overcome the above-mentioned challenges, strengthen the supply chain and drive sales efficiencies through a Demand Sensing solution.

The client was looking for the following features:

How TradeEdge Market Connect Helped?

TradeEdge’s cloud-based, low-cost, pay-as-you-go solution helped the client spread their risks, enabled a quicker go-to-market, and met business goals faster than earlier. TradeEdge Market Connect is based on an end-to-end partnership model that caters to delivering, consulting, product implementing, and managing various services for the client.

With TradeEdge Market Connect, the client has seen a 4-10% improvement in sales and has automated 90% of efforts spent on data acquisition and harmonization. Near real-time visibility into secondary sales and inventory data helped the client with better demand planning and forecasting.

The Role of Data in Supply Chain Visibility

Enterprises with a global footprint have to work closely with local distributors and suppliers. However, the complex supply chain network and technology immaturity give rise to multiple issues; hence bringing all parties on the same page became a major roadblock. Also, the tech maturity of one distributor vastly differs from the other.

The absence of supply chain visibility, poor consistency, and quality in data transfer reflect inaccurate secondary sales numbers and inventory situations for businesses.

Role of Data in Supply Chain Visibility

Any informed business decision is quite impossible in the absence of relevant data.

Hence, when we speak about increasing supply chain visibility, we imply the availability of granular data carrying information about the supply chain network. Data in the shape of numbers, graphs, emails, invoices, and others capture subtle nuances in the process, challenges, and missed opportunities. This This information is scattered across different documents, PDFs, emails, or so carry valuable insights, which, when analyzed, can help in better decision making. Furthermore, since the data reflects an accurate picture of the entire process, one can easily understand what is happening against what is perceived to be happening.

Let’s walk through the challenges enterprises face when working with a global supply chain.

Issues with Poor Supply Chain Visibility

According to market statistics, nearly 65% of procurement leaders have limited visibility beyond tier 1 suppliers. Poor or limited visibility is a significant hindrance when businesses need real-time data to strategize, forecast, and implement plans. Approximately 76% of CFOs agree that it is impossible to meet business objectives without knowing the two versions of the same truth.

Poor visibility makes data capturing a real challenge. Likewise, the supply chain network complexities remain hidden from sight in the absence of accurate data. Both are directly and indirectly connected to one another.

Below are a few challenges that arise from poor visibility of your global supply chain network.

Case Study: How TradeEdge Enabled Supply Chain Visibility for the World’s Largest Snack Company in Emerging Markets

Our client, a Fortune 500 firm and the world’s largest snack company, had to work with a complex supply chain network of distributors closely.

In the absence of a proper global supply chain management solution, the client faced several challenges, a few of which are mentioned above. Hence, they could not project future sales and forecast inventory situations without ground data. The lack of supply chain visibility and disparity in technological maturity among distributors and vendors compelled the client to seek help elsewhere.

The client needed a globally scalable data exchange platform for their fast-growing Indonesia and Malaysia markets. They were looking for an AI-based supply chain management solution to cater to the following requisites:

TradeEdge Market Connect helped increase supply chain visibility, thereby enabling the client with a complete view of the supply chain network.

How does TradeEdge Work?

TradeEdge Market Connect, a turnkey cloud-based solution backed by a robust service-level agreement (SLA), is designed to improve supply chain visibility for exchanging, cleansing, and harmonizing business data. Further, TradeEdge provides a usage-based pricing model with low capital expenditure, keeping in mind affordability in developing and emerging markets.

TradeEdge Platform features:


Supply chain visibility is imperative for businesses with a global footprint to forecast future sales and make data-driven decisions. However, a disjointed distributor-supplier-retailer network in emerging markets and technological immaturity can significantly hinder global brands from competing with local ones and surviving.

How XtractEdge Contract Analysis Optimizes Contract Management Processes for Enterprises

When you enter into a business relationship with a client, you sign a contract, which forms the foundation for all future business dealings. Inadequate contract visibility and oversight can lead to many unforeseen challenges, both for you and your client, in the coming days. Hence, contract analysis should be your second step to sealing the deal with your client.

What is Contract Analysis

As mentioned earlier, contract analysis captures minor discrepancies existing in the terms and conditions mentioned in the contract. A careful analysis of each term can add value to your business’s bottom line moving forward.

Any contract signed can have errors, sapping your business profits. And such errors can lead to many roadblocks, hampering the seamless workflow and unfulfilling terms and conditions. Hence, projects get delayed, and clients are left highly unsatisfied.

A Few Common Errors in Contracts

These errors can be in any shape and form, a few of which are highlighted below:

Poor contract handling translates into painful losses for both! Here, a careful contract analysis helps, especially when a contract comes with a defined contract lifecycle.

What is Contract Lifecycle Management?

Contract lifecycle management or CLM is a contract management process done methodically to cater to significant cost savings and efficiency improvements. The entire journey begins right from the initiation stage and continues till terms renewal if needed.

AI contracts, however, use the power of automation to streamline and accelerate contract processes during key stages. Only the best contract management software can ensure an errorless contract creation and analysis, which safeguards partners from irreplaceable errors and delayed deliverance of terms and conditions.

The Importance of Contract Analysis – Why does It Matter?

Effective contract analysis powered by AI-enabled software can be a crucial driver of business success. It allows business transactions visibility and enables teams to make necessary changes in the existing processes based on what they see in real-time. Further, the team can mitigate plausible financial risks while deciding on a fair offer.

How does Contract Review Software Help?

Contract analysis software powered by AI and automation help users review business contracts faster and seamlessly organize the processes, workflows, challenges, and solutions. Such software ensures the contractual terms align with new laws and regulations.

A unique example of the best contract management software for small businesses would be EdgeVerve’s XtractEdge.

This contract analysis software is a business-friendly platform helping parties identify key clauses and terms, mitigate contractual risks and reduce the negotiation cycle. XtractEdge leverages ML technology to extract insights from contracts and give customers complete control over their contractual data. Hence, making smarter decisions become easy.

Let’s elucidate its benefits with an example:

Case Study: XtractEdge Improved Productivity by 9 Times for a Fortune 500 Client using AI Contract Analysis & Risk Review

Our client, a Fortune 500 conglomerate, competes in various industries, including construction, machinery, railway systems, electronics, financial services, and many more. The client’s procurement team was tasked to manually create, review, and conduct risk analysis of contracts. This led to the following challenges:

Hence, the client partnered with XtractEdge to analyze existing processes and configured AI-enabled contract analysis software to match the client’s requirements.

Soon enough, the client witnessed a 9x improvement in employee productivity and 90% cost savings per contract review.

The Bottomline is

XtractEdge Contract Analysis proved efficient and economical for this client and many others who faced bottlenecks while handling bulk procurement contracts. Moreover, with the help of automation and new-age technology like Machine Learning and Artificial Intelligence, contract analysis has become a less cumbersome task for businesses.

AI-Powered Intelligent Document Processing & Extraction for Loan Processing

Loan origination for business clients is a long-haul procedure and is both time and labor-intensive. Document extraction and processing needed during loan approvals can burn many productive hours when handled manually. In addition, this approach deters banks and financial institutions from focusing on other value-added business areas.

Since the major bottlenecks arise during loan processing, cutting corners to save time is a recipe for disaster. Let’s dig deeper to understand the core areas of concern.

The Banking Industry: Challenges of Manual Document Extraction & Processing for Loans

Most banks and financial institutions lack centralized Intelligent Document Processing software. Hence, manual loan processing and client communications can become a major concern for many reasons; a few of which are described below:

Delay in Manual Paperwork

Document processing for business loan applications is time-intensive, especially when done manually. And incomplete paperwork can halt the application processing endeavor. Moreover, since process documentation is impossible without complete application paperwork, banks can face missed purchases/acquisitions and lost clients. Document processing automation using AI platforms like XtractEdge can save the day for banks or loan extractors.

Terms & Offerings Create Confusion

Most clients aren’t familiar with loan terms and offerings. Hence, a loan officer is the best person to educate applicants on these terms and offerings. Unfortunately, a delay in communication can result in the client eventually losing interest or switching loan providers. That translates into lost business for the bank. A comprehensive Intelligent Document Processing platform should describe each term in simple language so that applicants do not need to approach an executive for help. That will arrest time-lost in communication and knowledge gap.

Human Errors on Forms

Manual paperwork is prone to human errors; hence completing them on time doesn’t necessarily give the green light for processing loans. Missing fields and other inaccuracies caused by human entry add to the loan generating time and often translate into loan abandonment.

AI document processing can bridge the gap and guide applicants accordingly with various paperwork. For example, digital filing of loans will immediately raise red flags in case of errors and address them instantly.

Time-Intensive ID Verification

Verifying clients’ identity documents and other Government papers is painstakingly long. Much paperwork is involved, which eventually delays the loan processing system. Hence, digital intervention powered by intelligent data capture and document extraction capabilities can reduce the time and labor involved here.

Gathering Signatures from Parties Concerned

Likewise, getting signatures from all parties concerned delays the whole process. Therefore, banks should opt for a digital signature to match the convenience of clients’ timing preferences without eating loan processing time.

Also, there are other ad hoc challenges like the cost of paperwork and the burden of handling multiple loans simultaneously. Loan aggregators stumble mostly when a manual approach to loan generating and processing is practiced. And when bulk loan applications hit them suddenly, the whole process falls apart.

This is where a digital solution can really make a difference and greatly ease the burden on bank executives. Let’s elucidate with an example.

Case Study: How XtractEdge Helped a US-Based Bank Achieve 90% Accuracy in Loan Processing

One of the largest US-based financial institutions faced serious challenges when handling over 25K loan applications per week. Due to the COVID pandemic, an unprecedented surge in Paycheck Protection Program (PPP) Small Business Administration (SBA) related loan applications was witnessed. Manual document processing and data extraction only added to the employees’ overall workload.

With XtractEdge Platform and its computer vision capability, the client was able to digitize and process 170K+K loan applications.

AI-Powered Intelligent Document Processing & Extraction for Banks & Financial Institutions

Consumers are increasingly opting for digital-only banks. Hence, it is high time for the latter to shed off their traditional approach to offering banking services and switch to a smarter solution. And many are already doing it by opening their online services. AI is the next best bet for banks as this technology helps streamline comprehensive banking processes, manage customer requests faster with fewer resources, stay relevant and competitive in the market, and make smarter decisions.

The end-to-end document extraction and processing capability powered by AI and ML techniques make XtractEdge the one-stop business solution. Banks, financial services, and other organizations looking for a cutting-edge technology solution for data extraction and processing can benefit immensely using this platform.

Key Strategies to Enable a Resilient Supply Chain for The Post-Covid Future

In a time of fierce competition from specialized companies, e-commerce acquiring a larger market share, and a general shift in customers’ purchasing behaviors, the CPG sector has proliferated in recent years. This led to a significant uproar in the supply chain network.

COVID-19 has strongly emphasized our systems’ flaws, from public health to global supply chain networks. Essential supplies had been in short supply throughout the world. Even while businesses stabilize, the dynamic nature of demand and the ongoing economic pressures leave room for improvement. The shortage is primarily due to a complete lack of visibility into the supply chain network.

The Importance of Supply Chain Resilience

Supply chains that are reliable, adaptable, and sustainable are critical to the resiliency of the construction and infrastructure industries. Organizations have already started considering alternative ways for integrating future resilience as lessons acquired from the COVID-19 pandemic are used.

Optimizing supply chain resilience necessitates a holistic approach that considers all layers. Critical programs and spend categories will require strategic supply chain maps to help identify potential areas of failure in future shocks.

Cloud-based supply chain management systems can enhance forecasting and notification of interruption repercussions. Analytics are set to determine existing resilience measures’ potential effectiveness and suggest any necessary mitigation actions before it’s too late.

What is Supply Chain Resilience

Connectedness and Cognition

The concept of supply chain resilience isn’t new; it’s been debated in academia, C-suite, and on the shop floor for years. Experts have debated risk mitigation, adaptability, and the cultural shifts required to achieve resilience since the early 2000s.

There is no foolproof way to avoid all the dangers, especially high-impact/low-probability occurrences like SARS, foot-and-mouth disease, or a sizeable terrorist strike. Furthermore, the lack of historical data precludes predictive statistical tools from aiding in risk containment.

The current pandemic has highlighted the importance of creating supply chain resilience. Now more than ever, enterprises require supply networks to recover from the current crisis and maintain stability in the aftermath.

We believe that supply chain resilience is defined by two characteristics: its ability to be “connected” and “cognitive.” All components of a connected supply chain — wholesalers, retailers, and local stores have accurate, end-to-end, near real-time visibility. A cognitive supply chain thus uses the data gathered due to its interconnectedness to learn and produce actionable insights.

Building a Solid Foundation Connectedness

A linked supply chain is one in which wholesalers, retailers, and local stores — have accurate, end-to-end, near-real-time visibility. And increased visibility of your supply chain empowers you to closely track products as they travel from supplier to manufacturer and finally to consumer.

Supply chain visibility gives you more control over your inventories, costs, various disruptions, and risks. Here, data is the connecting link fetching valuable insights from the entire supply chain network, helping companies in the following ways:

Versatile and cost-effective data collection strengthens a worldwide consumer products company’s overall demand planning. For instance, TradeEdge Market Connect also resulted in a 4-10% increase in sales, in addition to the considerable cost reductions.

The Power of Cognition

A cognitive supply chain uses the data gathered due to its interconnectedness to learn and produce actionable insights.

The supply chain generates massive amounts of data. Mostly, this data is unstructured across various documents, emails, files, invoices, and others. Analyzing them manually is no more an accepted practice given the short time companies get to make decisions.

With the help of analytics, CPG companies can get a structured summary of relevant data in the shape of graphs, charts, images, and valuable insights for making data-driven decisions. Automatic data exchange enabled by machine learning is the best bet for CPGs as the process will:

End-to-end supply chain transparency improves sales and organizational performance for a multinational beverage firm. For example, TradeEdge Market Connect contributed to a 90% reduction in manual reporting, a 20% reduction in stock-outs, and a 10% boost in case-fill rates.

Influence of Pandemic on the Supply Chain Network

The COVID-19 pandemic has put 20 years of supply chain resilience study, debate, and preparation to the test. The challenge for companies will be to strengthen their supply chains while maintaining their competitiveness.

EdgeVerve thus believes that a connected and cognitive supply chain is essential for a sustainable company. Enterprises can accomplish this objective using the TradeEdge suite of solutions.

How Can AI Help Overcome Data Challenges Across the Demand Value Chain? 

The complexity of today’s supply chain network makes data capturing a challenging endeavor. Businesses need to classify the data broadly into three dimensions: Product, Market/Channel, and Marketing. However, there’ one thing common in each of these dimensions: a problem of plenty. The puzzle of building a model based on data captured from one location-based market and reusing it in another begs an answer: Is AI for Sales and Supply Chain?

It’s time we get an answer!

Understanding the Supply Chain Network Complexities

Artificial Intelligence and Automation have become the need of the hour. Unfortunately, unless businesses proactively adapt to the changes, they are likely to lose the game for good. And this is not just to scare you; it is just a matter of time before you find yourself left out in the mayhem.

However, jumping onto the AI bandwagon without understanding the various complexities of businesses processes is the biggest blunder to commit. One needs a ground reality-check of applying Artificial Intelligence in logistics or any value chain for that matter.

Here, we will explore how leveraging AI can help your organization overcome data challenges that you may face across the demand value chain.

AI in Supply Chain – Can this be the Future?

The Big Data Conundrum

Courtesy of Artificial Intelligence, every human action leaves behind a data footprint. More often than not, human actions or decisions start following a specific pattern, which can model those decisions on reverse-engineering.

This helps businesses, for instance, forecast sales figures for their products/services in the coming quarter. As the behavioral purchasing pattern is repetitive, future forecasts become easy, right? Maybe not!

Reasons are that the market is fragmented and highly diversified, with multiple players rolling out similar products in tune with their competitors. The problem of plenty puts tremendous pressure on businesses, as they need to evolve to stay ahead in the competition continuously.

The sooner they get a new product out, the sooner other similar products replace the former. How buyers respond to them compared to what alternatives they have at their disposal only complicates the data capturing process. Businesses’ inventories are left untouched or wasted to match the competition. Here, AI inventory management software might help, but does it answer the data capturing conundrum? Hardly!

Product Dimension

The rate at which new products are introduced in the market was hard to imagine fifteen years back. Coupled with retailers rolling out private labels and mushrooming start-ups bringing innovation on the table have compounded matters for many big brands.

Thousands of new products roll out in the market every day, making it virtually impossible to identify a new product, let alone get a perfect match for it against your product portfolio. Confusions arise when you are a global corporation competing with regional brands, a few of which are mentioned below:

Using AI inventory management might not fetch the desired outcome because AI input still needs human validation before it can be rendered useful. Moreover, this approach is not time and labor effective. Hence, the question persists – how can you make AI work for you if you fail to make AI better in the first place?

Channel Dimension

How effective will AI be, especially when more than 80% of the data needed is outside the enterprise?

Building a supply chain strategy in Vietnam and using the same in the US shouldn’t be the desired approach for multiple reasons as there’s a huge gap in size and tools used in these two markets. Besides, the rise of online shopping made measuring ‘sell-out’ versus ‘sell-in’ the new mantra for success.

Handling a complex supply chain network of local/regional suppliers and distributors became a challenge. Sales reps had the onerous task of serving the millions of new outlets through thousands of local distributors even though you had no clue what the existing distributors were doing.

This is where a strong data foundation comes into play and is undoubtedly the first step in an organization’s AI journey.

Marketing Dimension

Digital marketing spending is on the rise and is accelerating at a scale more than print or television media can match.

Hence, big brands spend nearly 20% of their revenues on promotions, wherein direct marketing or consumer promotions account for another 10%. How can one measure the efficacy of marketing spending without sufficient verifiable data?

AI in logistics, inventory, and supply chain networks using multi-variate algorithms can allow you to measure the impact of these on product-specific sales and understand the impact of one over the other. But, the absence of useful data makes it difficult to map them effectively, although most retailers acknowledge the value of collaborative planning. The latter willingly provide as much visibility of their sales to their suppliers as possible.

However, the main challenge lies in the distributor network, where such asks are generally greeted with suspicion. Then again, creating business incentives for distributors or making it a condition of their distributorship might fetch valuable data, however less granular it may be.

Role of Data and AI in Delivering Value Across the Supply Chain

Though a buzzword, Artificial Intelligence or AI is still nascent. And it survives only in the presence of data. Then again, building a high-quality data foundation takes time, and not all business functions are amenable to AI modeling. Added to that are other issues, like shorter product lifecycles, sales and marketing methods explosion, fragmented and complicated supply chain network, and rabid focus on personalization.

For AI in the supply chain and other value chains to work properly, one must constantly feed new data. Therefore, you might have to gradually start small, learn, and expand automation to other areas.

The question ‘Is AI ready for Sales and Supply Chain?’ depends wholly on how well your organization navigates the changing market dynamics.

AI Document Extraction is the Next Step to Unlock Valuable Insights from Enterprise Data

Unstructured documents are a bane for businesses looking for data-driven decision making and strategy building. A one-size-fits-all approach to document extraction, processing, and comprehension cannot deliver the desired outcome for different enterprise scenarios. Today, businesses need Intelligent Document processing software to break the document conundrum; otherwise, a huge volume of unstructured data remains untethered.

This translates into lost opportunities for businesses. Hence, leveraging the power of AI in document extraction and processing can save the day for enterprises, as per statistics.

Why AI Document Extraction is Needed

Studies show that insights-driven businesses can grow 8-10 times faster than the global economy. Unfortunately, in the absence of AI-powered data extraction software, many valuable data lay untouched and scattered across various mediums. 80% to 90% of data within organizations is unstructured, and most of it is locked in documents, emails or images.

You may be surprised to know that around 129 billion business emails are sent and received daily. These emails are a powerhouse of information, if optimized properly, could provide valuable insights for better business decisions.

These documents hold much value for businesses. However, unstructured data can be challenging for employees; hence an AI document extraction process is needed to override those challenges. What are these roadblocks? Let’s find out!

Challenges of Unlocking Insights from Unstructured Documents

Organizations have to deal with a colossal number of documents carrying valuable insights daily. Data extraction from documents manually is confronted with many challenges; a few of which are described below:

Understanding the Importance of Document Processing

Data is paramount for driving business excellence using data-driven insightful decisions. Given the present condition of unstructured data, a comprehensive technology-based solution is what every business needs to stay ahead in the competition.

An end-to-end document extraction processing, and comprehension solution is the need of the hour. Below are a few benefits:

An Insight-Driven Enterprise Beyond Document Processing

On the one hand, data extraction, processing, comprehension, and consumption from documents are evolving, enterprises’ expectations are also changing.

Enterprises are looking for something beyond document processing to transform business outcomes with on-demand, contextual information. Since a one-size-fits-all approach to document extraction no longer matches every enterprise scenario, a more purpose-built, AI-powered document extraction and processing platform is the ideal bet for many.

For instance, the XtractEdge Platform with advanced AI capabilities, combining various Machine Learning and Deep Learning-based techniques, processes data just like enterprises want and when they want.

How XtractEdge helps Unlock Enterprise Document Intelligence

EdgeVerve’s XtractEdge Platform is a document extraction, processing, and comprehension platform designed to unlock business value from scattered and unstructured enterprise documents aptly. It serves the purpose of extracting intelligence from enterprise documents, regardless of complexity or domain specificity. The platform is user-friendly, quick to train, and extensible for any business use case.

For example, XtractEdge successfully helped a US Financial Services Major process over 25,000 loan applications in 10 days, thus improving the productivity by 10x.

The Bottomline is…

Document extraction and processing using the power of automation, AI, and ML have proved beneficial for companies handling massive unstructured data, like the above example. XtractEdge is the new-age technology that makes insightful decision-making possible for companies and helps them achieve greater outcomes and yield faster ROI.

AI is integral to business success in the new normal, and the faster you adapt it, the farther you will be in business value creation.

Document AI is the Key to Making Data-Driven, Game-Changing Business Decisions – How?

Enterprises need instant access to comprehensive data and actionable insights for data-driven strategic decision-making. Unfortunately, business data is unstructured and complex, posing a challenge for manual and disjointed approaches to data extraction. Also, a one-size-fits-all approach to document extraction and processing does not apply in every business scenario.

This is where Document AI comes to the rescue.

What is Document AI?

Document AI uses the power of Natural Language Processing and Machine Learning to train computers to imitate human reviews of documents better than humans.

EdgeVerve’s XtractEdge Platform is an Intelligent Document Processing software. It successfully overrides the manual approach to data extraction and processing complexities and unlocks business value from complex enterprise documents. XtractEdge can benefit enterprises in many ways. The user-friendly platform is quick-to-train and extensible for any business use case. With the help of RPA, Chatbots, and Analytics Tools, XtractEdge extracts intelligence from complex enterprise documents and makes them business consumption ready.

Why is Document AI Needed for Decision-Making?

The answer is simple – capturing granular data and subtle variations negligible to the human eye to support AI document analysis is thus needed for accurate decision-making. The manual approach to data extraction is ridden with flaws and errors, time and labour-intensive.

There are other challenges as well; a few of which are elaborated below:

Automated Document Processing & Extraction Use Cases & Benefits

Businesses need an intelligent solution powered by automation, AI, and ML to address the complexities of manual document processing and data extracting. Hence, Document AI software like XtractEdge is the ultimate solution for businesses to gain an edge in the competition.

Let’s understand the benefits of using an automated solution here:

Document AI for On-Demand Decision Making

AI document unlocks intelligent insights from enterprise documents and leverages on-demand contextual information to help enterprises make strategic decisions. Document AI ensures enterprise search for regulatory intelligence.

For instance, the XtractEdge Platform helped an American Pharmaceutical Enterprise to sift through thousands of R&D documents and reports to mine insights from data related to clinical drug trials. With NLP-driven answers to natural language questions and cognitive-aided precise intelligence, they improved workforce productivity and achieved high accuracy of search results across document types.

Increasing Revenue Footprint

AI insights are paramount when comparing policies and quoting prices in real-time, achievable with Document AI.

For example, EdgeVerve’s client, a leading American Health Insurance Provider, was looking for a document intelligence solution to process various complex Benefits Plan documents on the fly during negotiation cycles, giving a competitive quote to enterprise employers. With Computer Vision capability, they were able to perform structural analysis on the uploaded documents and guide the OCR software to make it more efficient. They achieved high accuracy of policy extraction for comparing policy benefits in real-time and increased productivity.

Document AI for Operational Transformation

Accelerating loan processing, a time and labour-intensive task is possible with accurate AI insights. XtractEdge Platform helped a large US-based Financial Institution with a loan processing expedition.

For example, the bank in question was troubled by an overwhelming surge in PPP related loan applications forcing them to scan through 25K Small Business loan applications or more in a short span. With XtractEdge Platform, the client was able to process 150K+ documents, enabling automation of underwriting process and significantly reducing manual intervention.

Document AI for Risk Reduction & Compliance

Document AI mitigates contractual risks and reduces compliance check cycles using AI-powered Contract Review and Risk Reporting.

For example, XtractEdge helped a Global Communications Company with more than 3 million historic multi-party contracts with high variation in contract clauses and unique contract templates, terms and clauses to find favorable clauses for revenue gains. They were able to digitize over 500K contracts, resulting in 90% cost savings.


Document AI is the new-age technology solution that answers all the problems enterprises face when handling bulk, granulated, and unstructured data for strategic decision-making. Platforms like XtractEdge uses Document AI capabilities to unlock hidden insights and values and provide businesses with reliable data insights needed for making game-changing decisions. Without that, gaining a competitive edge in the market will be a distant dream in the fast-paced, technology-driven, and data-dominant world.

How Data Digitization is helping Businesses with Intelligent Information Extraction & Improved Decision-Making

A business cannot run in the absence of comprehensive data. Unfortunately, unstructured data in non-digital formats serve as a significant roadblock in apt decision-making, which RPA tools cannot help enterprises save time.

That’s why enterprises today are turning to data digitization, which implies retrieving information from bulk, unstructured data faster and quicker. And the process has a name; it is called Intelligent Information Extraction.

What is Intelligent Information Extraction?

The whole concept of businesses dealing with bulk paperwork and entering data manually into their IT systems has become old school. Today, data extraction using various Intelligent Document Processing software has become mandatory to keep up with the changing market trends.

Technologies such as optical character recognition (OCR) and handwriting recognition help extract data easily but have their share of challenges. However, with the advancements in deep learning and cloud systems, a new breed of text digitization solutions has emerged. These solutions enable enterprises to easily capture the connection between field labels, values, the structure, and the layout of data elements – from boxes and tables to specific details like checkboxes and signatures.

This entire process falls under Intelligent Information Extraction.

Understanding the Difference between OCR Extraction and Information Extraction

OCR or Optical Character Recognition detects text regions on any scanned images and converts those regions into the correct digital text. With the help of cognitive AI, OCR can extract data from pages featuring handwritten text.

Contrarily, extracting information from unstructured text using NLP algorithms and automation fetches essential information into more editable and structured data formats. Intelligent Information Extraction facilitates the whole process.

OCR data extraction has a couple of limitations, a few of which are illustrated below:

Intelligent Information Extraction is responsible for overriding the challenges mentioned above and helping businesses with clear and concise structured data.

Intelligent Data Extraction Use Cases

There are various use cases for data digitization, a few of which are discussed below:

Form Digitization: Multi-page paper-based forms involving a mix of typed text, handwriting, checkboxes, and other fields and tables can be better optimized for data extraction using intelligent data extraction software.

Touch-free Zero Template Extraction: Data digitization of non-standard and unstructured input documents containing information in varying layouts addresses manual or OCR data extraction challenges.

Mixed-type Documents: Documents in different formats and types make data extraction a herculean task; not with an Intelligent Information Extraction approach.

Information Consistency Checking: The most complex use case requiring mature products and covering previous use cases, including consistency verification rules, can fare well with data digitization.

Data Digitization Solution Features

A comprehensive data digitization solution to extract information intelligently should comprise certain basic features. Some of them are listed below:

Benefits of Data Digitization

Businesses need intelligent data that’s well-structured and formatted to cater to data-driven decisions. Therefore, data digitization using intelligent information extraction features, like the ones mentioned above, can prove game-changing for enterprises.

Let’s explore a few benefits of using data extraction software for capturing granular data from structured and unstructured data sources.

In a nutshell

To deliver stellar customer experiences, businesses need data to make improved business decisions. However, manual input and extraction of unstructured data is time and cost-intensive, and the outcome is often clouded by errors. Intelligent information extraction can address such challenges and help businesses make informed decisions faster than usual. Data digitization is the need of the hour; sooner businesses realize, it is better for them to gain an edge in the competition.