Unlocking Success: Key Aspects for Designing and Implementing AI Solutions

By addressing certain fundamental elements, you can navigate the challenges associated with AI adoption and create a solid foundation for harmonious collaboration between humans and machines. This section will explore the essential components that unlock success in designing and implementing AI solutions. By focusing on these critical aspects, you can build AI systems that inspire trust, deliver reliable outcomes, and uphold ethical practices.

Maintaining Data Integrity and Privacy – The Cornerstones of Effective AI Governance: Data integrity plays a crucial role in the success of any AI framework. The abundance of data enables improved AI modeling and a more reliable engine. However, this abundance also brings forth challenges concerning data privacy and compliance with regulations governing personally identifiable information (PII). To address these concerns, organizations must develop robust data protection and privacy strategies to ensure the security of user data within their AI systems. Implementing encryption, role-based access control, and identity and access management systems is essential to safeguarding sensitive information.

Fostering Diversity – Mitigating Biases in AI Systems: The presence of historical biases in the input data used for training algorithms has led to erroneous outcomes, undermining trust in AI systems. It is crucial to prioritize diversity at every stage of the AI development process. It starts from problem conceptualization and ideation to framework design, model training, implementation, and continuous improvement. By incorporating diverse perspectives and data inputs, organizations can proactively avoid unintentional biases that may skew the performance of AI systems, ensuring fairness and accuracy for all groups involved.

Safeguarding Security and Reliability – Enhancing Safety Measures for AI Systems: Like any other IT system, AI systems are vulnerable to cyberattacks or hacking, potentially resulting in disruptions, malfunctions, or data manipulation that can lead to unexpected behaviors. Implementing layered defense systems and prioritizing security and robustness when designing AI systems is crucial. By adopting a proactive approach to enhance safety measures, organizations can effectively mitigate potential threats and ensure the resilience and reliability of their AI systems.

Fostering Accountability – Ensuring Transparency in AI Systems: The significance of accountability in AI systems is steadily increasing. It entails conducting thorough audits and scrutinizing every aspect of the system, from the process and lifecycle to the data and stakeholders involved. For example, to establish accountability in AI systems, the US Government Accountability Office (GOA) developed an AI Accountability Framework to identify critical practices to help ensure accountability and responsible AI use by federal agencies and other entities involved in the design, development, deployment, and continuous monitoring of AI systems. Critical approaches encompass comprehending the entire lifecycle of AI systems, engaging all stakeholders, regardless of their technical expertise, and conducting meticulous accountability assessments covering governance, data, performance, and monitoring at each stage. Organizations can instill trust and reinforce accountability in their AI implementations by prioritizing transparency.

Encouraging Trust through Transparency – Enhancing Explainability (taking an ML model and explaining the behavior in human terms) in AI Systems: Transparency is crucial in establishing trust within any system, including AI systems. By implementing a well-documented approach, organizations can provide comprehensive insights into the data sets utilized during training, implementation, optimization, and usage of the AI system. Additionally, explaining the factors behind altered outcomes further enhances transparency. With governments worldwide enacting regulations to prevent misuse and unethical practices related to AI systems, incorporating built-in transparency becomes imperative for both explainability and strengthening the overall credibility of the AI system and the organization. By prioritizing transparency, organizations can foster trust, build confidence, and promote the responsible use of AI technology.

Conclusion

Cultivating an environment of trust is pivotal for successful AI implementation. By proactively implementing strategies and processes centered around transparency and trust from the outset of designing AI systems, organizations can instill confidence in the AI system itself and its ability to navigate complex programs effectively. It, in turn, enhances the organization’s credibility regarding data protection and the overall safety of its systems.

As a leading AI solutions provider, IGT Solutions understands the importance of trust and transparency in the domain. We are committed to providing the necessary tools and expertise for harnessing the potential of AI in the most impactful manner. Through the fusion of cutting-edge technologies, deep industry knowledge, and an unwavering commitment to ethical practices, trust, and transparency, IGT Solutions empowers businesses to embrace AI’s transformative power while ensuring data integrity thoroughly. Leave your details to learn more about our AI services.

Author:

Chanchal is the Global Director of IGT Solutions CoE in Testing. With nearly 17 years of experience in Software Quality Assurance and a remarkable track record of heading QA practices, Chanchal brings a wealth of expertise to IGT’s Testing CoE. Using cutting-edge tools and technologies, she has successfully delivered Cloud Infrastructure automation testing, UI and performance, and scale test automation projects.

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Hyperautomation is powered by machine learning (ML), artificial intelligence (AI), natural language processing (NLP), and optical character recognition (OCR). In other words, hyperautomation is intelligence-added automation that expands business efficiency, improves decision-making capabilities, and builds a strong bedrock for innovation. Therefore, unsurprisingly, enterprise leaders are gearing up to optimize intelligent automation for their business advantages. Research predicts that the hyperautomation market size is growing steadily and will be worth $29.2 billion by 2028, growing at a CAGR of 22.2%.

Unlocking business value at scale

Today, most businesses operate in volatile market environments. To succeed, they must satisfy discerned customers, survive intense competition, combat rising inflation rates, foster sustainability, adapt to emerging market trends, and constantly stay on top of their game. Enterprises can overcome all these roadblocks by embracing hyperautomation.

It allows them to shift their focus from repetitive, effort-intensive tasks to business-critical functions and larger business goals. Such streamlining of business priorities helps enterprises unlock new growth opportunities and increase operational efficiency. It ensures that decision-makers have access to richer, more accurate, and quicker insights that can give them granular visibility into enterprise-wide operations. This enables them to make more informed choices in real-time and improve resource utilization, data security, compliance adherence, and so much more. A report by Gartner suggests that by the year 2024 hyperautomation will reduce 69% of a manager’s work.

While robotic process automation (RPA) allows organizations to only transform the way they handle rule-based, repetitive processes, hyperautomation goes beyond this. It offers the value delivered by RPA and performs tasks that are outside RPA’s scope and require considerable human intervention, logical thinking, and critical perceptions at different stages.

Cost optimization is another key benefit of hyperautomation. Intelligently automated tasks, disruption-ready operations, and differentiated customer and employee experiences facilitated by hyperautomated processes can help enterprises reduce opex considerably. By turning key business processes into efficient digital workflows, hyperautomation forges a pathway into the future of work and expands revenue streams. The payoff? An innovation opportunity that benefits organizations, their employees, and customers, alike.

Discovering real-world value

It’s not just large-scale enterprises in any specific industry that have recognized and embraced the game-changing hyperautomation capabilities. In fact, according to a recent study by Gartner, 80% of hyperautomation offerings will limit industry specificity by 2024. It indicates that enterprises of all sizes across industries are jumping on the hyperautomation bandwagon with zeal.

For instance,  UBM, a global B2B media company has reduced its production expenses by 30% and shortened its time-to-market by using hyperautomated processes. Our previous blog post illustrated how the hospitality and travel industry is utilizing hyperautomation for contactless and easy check-ins and check-outs, quick responses to customer inquiries, and streamlined reservation processes. Likewise, game advertisers and developers have been leveraging hyperautomation to understand their target audience as well as streamline performance evaluation and make necessary creative iterations. Similarly, by using hyperautomated workflows, BFSI enterprises have been enhancing their marketing and sales, regulatory reporting, payment collections, lending operations, back-office processes, customer support, and digital banking offerings.

A study conducted on the US health industry has revealed that nearly $41 billion are annually paid to patients to compensate for the manual errors that occurred during clinical data processing. Fortunately, hyperautomation has started to change this. It irons out back-office operational inefficiencies, which in turn would support medical experts to improve clinical results and offer superior patient experiences. Many clinics have already introduced digital nurse avatars to communicate with patients as the first line of help. These hyperautomated bots ask relevant questions to patients for collecting an accurate account of their well-being and symptoms. It is then from this first-hand information that appropriate and timely medical assistance is directed, further diagnosis is decided, and contacts with medical centers are made.

Conclusion:

While the benefits of hyperautomation are quite promising, enterprises need a synergy of incremental, thoughtful approaches and the right tools to optimize the technology. And that’s where IGT Solutions can help. Our Customer 360 solution has enabled global brands with a single-pane, real-time 360-degree view of their customers. Powered by our hyperautomation-first offerings and expertise, enterprises have unlocked unprecedented efficiencies and business value. The aviation sector for example can utilize our AI/ML-powered Fare Note Interpreter to improve their transaction processing times by 80% and reduce refund processing errors by 99%. Enterprise across industries can similarly achieve large-scale transformations smoothly and bring about meaningful, sustainable change with us.

To know more about our services and solutions, and understand how they can help you pave the way to future-ready business operations, get in touch with us now.

Author:
Vitesh Kohli is an Intelligent Automation thought & execution leader with a track record of setting up CoE, best practices, robot delivery, managing change and extensive program and project management experience with a strong entrepreneurial background. Have strong expertise in leading Transformation -Intelligent Automation across industries. Strong knowledge of planning big robotics set-ups, cognitive-based chat products, multi-components automation and multi-product Tech solutions. He is a domain Expert and Hands-on Technologist using Artificial Intelligence, Intelligent Automation and Conversational Commerce to solve business problems in large enterprises.

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But the good news is, you have a solution!

The answer is hyper-automation. This technology enables enterprises to delight their customers with an interactive, immersive, and personalized experience.

What is hyper-automation?

Hyper-automation is the planned application of a wide range of tools, platforms, and technologies, including Artificial Intelligence (AI), Machine Learning (ML), Intelligent Document Processing (IDP), Robotic Process Automation (RPA), etc., to expedite operations and improve their effectiveness. It enhances business processes and integrates functional and process siloes.

Benefits of hyper-automation in the hospitality industry

Streamlined Reservation Procedure: Every customer wants a simple and quick booking procedure, especially in the travel industry. At the same time, customers also look for discounts on different portals and book the hotel that offers the best deal. Additionally, when there is a mass cancellation ( it happened during the pandemic years) making a refund to each customer was a huge task All these together have made hotel reservations a cumbersome process for hotel owners.

All manual repetitious operations, such as bookings and cancellations, can be automated with rule-based configuration and intelligence-based automation. Additionally, the technology enables instant refunds following cancellation.

Improved management of hotel tasks: When you go to a hotel and place your first order, the staff who takes your order is the first touch point where a brand impression is created. The first interaction can make or break the reputation of the hotel.

But a hotel owner can completely mitigate this risk by leveraging a hotel job management system.

For example, if a wrong staff member is given a task, they only need to immediately enter the information into their task management software on a handheld device. A prompt response will result from the automated rules and alerts notifying the affected party or department.

Contactless and simple check-in and check-out: The COVID-19 pandemic has created a demand for contact-free services. Customers can check into their rooms by simply arriving at the hotel using their smartphones thanks to automatic check-in software. This means the customer doesn’t need to wait at the reception to get their room keys. Additionally, it enables the hotels to clear the headspace for their staff and lowers the operational cost.

Respond in a timely manner to consumer queries: Nine out of ten times, customers who call or send an email with a question must wait for sometime to hear back. Longer wait times can negatively impact a customer experience.

The use of chatbots and voice bots have evolved as a crucial channel of communication for the hospitality sector. If a customer’s question can be answered right away, the system processes their emails and chat requests, analyses them, and responds appropriately. If not, the Chatbots/voice assistants forward the inquiries to a live agent, who is now free because there are fewer demands for answers.

Improvements in Customer Feedback Management: When consumers have a negative experience with a brand, they typically write online reviews about it. There are considerable chances that you might lose a sale if there are many negative reviews or if they are not addressed right away. This can be detrimental to your online reputation.

A hotel task management system comes with a built-in automated feedback management system. The customer support team is automatically notified whenever there is a negative review to take appropriate measures.

Are you ready to embrace hyper-automation technology?

Hyper-automation is the future of the hospitality business. It enables business owners to fully refocus their attention from mundane data-oriented duties to more crucial functions that enable them to provide superior customer service.

Author:
Vitesh Kohli is an Intelligent Automation thought & execution leader with a track record of setting up CoE, best practices, robot delivery, managing change and extensive program and project management experience with a strong entrepreneurial background. Have strong expertise in leading Transformation -Intelligent Automation across industries. Strong knowledge of planning big robotics set-ups, cognitive-based chat products, multi-components automation and multi-product Tech solutions. He is a domain Expert and Hands-on Technologist using Artificial Intelligence, Intelligent Automation and Conversational Commerce to solve business problems in large enterprises.

Source: Gartner

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What is Computer Vision and what is CX?

Computer vision is an AI capability that allows IT solutions to extract data from digital images, videos, and other visual inputs, understand it and take or recommend action. It uses cutting-edge ML and AI capability to replace the human eye.

As per Annette Franz, founder, and CEO of CX Journey, “Customer experience is the sum of all the interactions that a customer has with an organization over the life of the relationship with that company or with that brand”

Why is Computer Vision enabled CX important?

As consumers are increasingly embracing digital, many of their transactions and interactions are getting digitalized and personalized. Computer Vision is one of the technologies that enable this personalization, and the possibilities are immense.

What are some of the use cases of improving CX through Computer Vision?

The most common use cases these days are in the retail and fashion industry.

Effective CX in a retail store nowadays depends on hassle-free availability, quick checkouts, and customized offers. This may seem to be difficult to achieve especially during periods of high footfall. Retail Stores can use Computer Vision for quick self-checkout, automatic refilling alerts for empty shelves, monitor customer activity for suspicious behavior, measure footfalls, and do customer traffic management in real-time.

It can reduce waiting time in long shopping checkout queues by automatically alerting the activation of more checkout nodes seamlessly. It can also provide accurate footfall analytics with data on repeat customers in the store etc.

The Fashion Industry is leveraging Computer Vision to create image-based searches for design availability, virtual trial rooms for e-commerce to give a look and feel or to generate new ideas and provide intelligent recommendations. Similarly, it can also be used for the furniture, home décor, and eyewear industry to name a few.

Parking management in large shopping malls can be another use case. Computer Vision can be used for occupancy detection and management based on visual data inputs. Hassle-free parking will optimize the shopping traffic and improve the CX of customers shopping there. This can be extended to many other applications of parking management such as – at airports, public transport parking, or large trade shows or concerts.

 How can you apply Computer Vision to improve the CX of customer support?

Customers need a quick and effective resolution of their needs when they connect with a support center. The optimal CX here would mean minimal waiting times and minimal steps in an effective resolution process.

Customer Care centers can be upgraded with Computer Vision enabled Virtual Assistants. It can extract details, authenticate customers and automatically fill details based on visual data inputs of the customer or the product. It can zero in on objects in stills or videos provided by customers and recognize details like category, model no., and manufacturer.

Advanced solutions also have the capability of detecting the operational status of components of a device and detecting faults with high accuracy even for poor-quality images provided by customers.

 How can you apply Computer Vision to improve the CX of air travel industry?

Air travel passengers choose an airline based on the CX offered, besides cost and convenience. Computer Vision can help provide a better CX with intelligent baggage handling, quick check-ins, advanced airport security capability, better aircraft traffic management with the least delays, real-time snag detection of aircraft and in the airport infrastructure. It can also pull up details of repeat passengers for in-flight staff for a better and more customized flight experience. A customer would be delighted with the more customized, hassle-free, delightful, and on-time experience.

Do you think the time has come for Computer Vision to transform CX across domains?

The applications of computer vision are endless and can be used at every node of customer interaction that involves vision to create a better customer experience. It generates a large amount of accurate data for optimization of the experience with analysis and insights in real-time. Businesses that care for their customers want to provide a high customer experience that CX enabled with Computer Vision can provide.

Computer Vision-enabled CX is happening now!

Author:

Nitin Rai is the Vice President of Marketing and Growth Initiatives at IGT Solutions. He has spent over two decades working with leading information technology service providers and is currently enabling various aspects of Marketing, Demand Generation, CX, Digital, MarTech deployment, PR, and Communications at IGT Solutions. He aims to help the organization attain an enviable position as a leading Digital CX, Data, and Digital services provider in the chosen industries, globally.

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Let’s start with some industry examples where companies across the world have been working with legacy systems as they are critical to their business.

• Mainframe systems that airline companies use for reservations and ticketing systems
• Command-line interface systems based underwriting engine used by insurance companies
• Companies with a core platform on legacy systems (such as COBOL, DB2, etc.) and being used by numerous customers

Many companies are generating good revenues from their operations and supporting the business, but they are missing out on the opportunities for connected data across the ecosystem. The architecture is not supported for enabling seamless data porting, scalability, and speed. Moreover, these systems have a higher cost of operation and high maintenance; and are plagued with usability and security issues. Technological obsolescence and resource scarcity are a few more challenges.

Key challenges with the Legacy systems

Integration issues with other systems: Gone are the days of monolithic architecture; nowadays, solutions are designed to have benefited from multiple systems connected via APIs. Modular and SOA-based architecture are common with scalability benefits and integration with other 3rd party systems. Legacy systems are not designed to integrate with other heterogeneous systems and thus cannot achieve fully integrated systems.

Go to market is slower: Legacy systems usually follow the waterfall model of software delivery, and aligning with the market demand of releasing software features on the fly is not feasible.
These systems are heavily focused on a manual way of working with little or no integrations available for 3rd party build, configuration, and CI/CD tools, causing the entire process to become slow and error-prone.

Misalignment with customer requirements: In the current era of a customer being the king, legacy systems are not synchronized with customers’ needs, especially in terms of flexibility, agility, usability, and customer experience. Legacy systems are still tightly coupled with OS, interface, screen, etc. In the case of growth, these systems cannot scale at the same pace as that of business.

Artificial Intelligence and machine learning: Legacy systems are not built to integrate 3rd party data sources and are relatively inflexible. When the world moves towards NoSQL, a flat-file kind of database, legacy systems still run on relational DBMS, slowing the entire operation. Creating intelligent dashboards analytics is a cumbersome task, if not impossible, in these systems. These systems cannot harness the power of AI/ML and achieve predictability and digital transformation benefits.

Human resource challenges: The availability of skilled resources with expertise in legacy systems is seeing a decreasing trend. Universities and institutions, nowadays, focus more on newer technologies such as cloud, AI/ML, Internet of Things, Intelligent Automation, Blockchain, DevOps, etc. This means the engine to produce the team with legacy application skills is dying; moreover, the new generation of engineers is inclined to work on the latest technologies and not on legacy ones.

Compliance and Regulatory requirements: Nowadays, regulatory authorities and governments are very keen on adhering to compliances or heavy penalty is imposed on them. HIPPA, SOX, PCI, GDPR, etc., require your technology to be current and aligned with the regulations. These rules demand specific data to be shared with government bodies, and their implementations/modifications are very time-consuming in legacy systems.

Solutions to legacy technology challenges

We understand the challenges legacy technologies pose, like speed bumps, to leverage data modernization and use it to achieve higher revenue and innovative offerings at the utmost speed to enhance customer experience.

The question is, how can we resolve these legacy issues?

Digital transformation is the key to reducing the risk and unpredictability and improving the customer experience in the current market.
The implementation of Agile and DevOps coupled with a technological revolution comprises cloud, IoT, and AI/ML with enriched and interconnected data that will resolve the issues mentioned above.
Each organization is unique in the context of a market, customers, products, ecosystem, etc., so there can’t be an umbrella approach. The approach must be customized to different companies to harness the full benefits of digital transformation.

How can IGT Solutions help you?

With its technological expertise and process champions, IGT Solutions is well placed to implement methodologies and achieve a faster delivery cycle, cost optimization, and excellent customer experience.
IGT’s expertise in transitioning from legacy to cutting-edge technologies in a phased manner does not disrupt the ongoing business engagements. It can easily transition into the latest technologies fitting to customers’ needs. IGT’s expertise in Agile, DevOps, Cloud, and experience in implementing AI/ML, IoT, and Blockchain will help you ensure a smooth journey from legacy to latest technologies.

Author:

Yatender has 20+ years of experience in software test engineering. As the head of Testing Practice at IGT Solutions, Yatender is actively involved in innovations related to test engineering covering new tools, technologies, and solutions, and enabling IGT’s clients to achieve faster time to market quality improvement, and optimization of developer efforts in overall SDLC. A result-oriented leader, proficient in delivering high customer value and achieving excellence in service delivery management with proven skills in consulting and managing large and complex test programs. When away from work, he enjoys reading on a variety of topics and spending time with kids.

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Both the IT industry and software testing domains have different yet relevant trends that organizations should be focused on to remain relevant.

Let’s look at some key areas of software testing that will see a lot of traction in the year 2022

Hyper Automation Testing: The new trend around the block will take a platform-based approach where test automation of web applications, mobile apps and desktop apps will take place with a heterogeneous tool set. We will also see a growth in open source tools as well as low code platform for automation testing. The year 2022 will also see a greater focus on implementing In-Sprint Automation, wherein testing teams are performing functional testing in 1 to 2-week sprints, designing and executing automated test cases within a sprint is bound to provide benefits of shift-left, quality improvement and faster time to market.

Another trend is to take automation to next level, called as Hyper-automation, that combines the power of AI/ML and automation technologies. This will help achieve faster, scalable, high-quality product development.

The use of AI will help to automate the areas within the automation testing process, which are still performed manually such as functional test case writing, identifying regression, impact analysis etc.

Artificial Intelligence and Machine learning: Artificial intelligence is already implemented in several use cases in the industry and its effectiveness is increasing as the days pass:

There are several uses of artificial intelligence in software testing:

• Risk prediction in code-based historical data and key variables
• Optimization of testing efforts and timelines based on risk profiles
• Fine-tuning of regression suite and self-healing frameworks
• Analysis of application logs, identification of errors and automatically find failure reason
• Prediction of key test configurations and quality index levels

Security Testing: There has been an increase in the use of IT applications in the last few years, we have observed an exponential increase during the covid period, in addition to that, we also saw a sharp rise in cyber security incidents including ransomware. The positive effect of all these problems is the enhanced focus that organizations are now putting on the security testing of the applications, systems and infrastructure. The year 2022 will see greater traction on security testing right from pipeline security to DevSecOps to penetration testing. As security is picking up fast and has a large attack surface area, it’s important to perform security testing in layers. A customized security testing strategy will have a combination of DAST, SAST, IAST and API security tests spread across guidelines from NIST, COBIT, ISO 27001 and PCI DSS.

As per the world risk report 2022 of the World Economic Forum, the cyber vulnerability data trend is worrying:

• 435% increase in ransomware in 2020
• 3 million gaps in cyber professionals needed worldwide
• 800 billion estimated growth in value of digital commerce by 2024
• 95% of cybersecurity issues are traced to human error

Companies need to upgrade their infrastructure and ramp up staff skills to tackle cyber vulnerabilities. Security testing can help alleviate cyber risks by shift-left and implementing a continuous security testing pipeline.

UX/CX Testing: User experience is increasingly becoming a key factor in customer engagement and helps retain the existing user base. There will be an additional push to achieve a good user experience for applications used by organizations, especially websites and mobile apps. This is of utmost importance for organizations that use such web or mobile applications for business e.g. ecommerce, insurance, etc.

There is a plethora of tools available both in open source as well licensed categories spanning from user profiling, customer journey mapping, accessibility, user persona analysis, sentimental analysis etc. The best approach to UX/CX testing will be to understand the business flow, Application landscape, target customer segment, customer touchpoints, marketing mix and competitor analysis.

Performance Engineering: Performance of the application is imperative from a usability standpoint and users see it as a primary factor to either continue using the application or discard it. The organization’s focus will now be on the performance and results right from the architecture designing level to usage on production.

The market has seen some good traction in the performance testing tool segment, such as:

• Tricentis acquired Neotys
• Perfecto acquired Blazemeter

These trends are clearly giving good signs as performance testing is going to become a part of the DevOps cycle, performance as a pipeline. It will be great to see full pipeline testing for non-functional areas including security, accessibility and performance.

Agile, DevOps and Lean: Agile has helped organizations to bring the teams together and remove the compartments of BA, developer, tester, system engineer etc. and has set up expert teams that are working together to deliver good quality deliveries. Similarly, DevOps ensures that applications are deployed frequently and automatically thereby enabling faster value delivery to end users. Lean helps in the continuous improvement, removing the extra waste from the system and processes.

These practices take place at various stages in the organization and this year will see further adoption among its teams.

Continuous and automated testing enabled with CI/CD pipelines augmented with AI will help achieve faster & quality software delivery thereby enhancing customer experience.

Conclusion

The future of testing in the year 2022 will be more technology-enabled. If you are looking for more updates on upcoming trends of testing and how to prepare your teams to harness it, you can connect with me at  Yatender.sharma@igtsolutions.azurewebsites.net

Source: Gartner

Author:

Yatender has 20+ years of experience in software test engineering. As the head of Testing Practice at IGT Solutions, Yatender is actively involved in innovations related to test engineering covering new tools, technologies, and solutions, and enabling IGT’s clients to achieve faster time to market quality improvement, and optimization of developer efforts in overall SDLC. A result-oriented leader, proficient in delivering high customer value and achieving excellence in service delivery management with proven skills in consulting and managing large and complex test programs. When away from work, he enjoys reading on a variety of topics and spending time with kids.

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Introduction

In an increasingly digitized world, corporates have realized that the fastest way to ensure efficiencies within internal operations is to automate – and the biggest roadblock to automation is structured and digitized inputs. Consequently, there has been a big focus on digitization and document processing. The target of these drives is to boost accuracy and effectiveness to structure unstructured and semi-structured data, and to digitize any non-electronic data into machine-readable formats. Towards this end, multiple technologies have been explored – optical character recognition through digital character libraries, redaction solutions to un-redact or redact digital documentation, AI/ML to classify and categorize multiple formats and data structures and provide automated learning.

It is a base understanding that the automation scope in the back office today is limited to the volumes that come from electronic and structured data, with a manual intervention needed for the rest. Through IDP, the scope of automation can be expanded to all the volumes that can be digitized and brought to a structured format, thereby maximizing the value delivered. The following sections look into how Intelligent Document Processing takes process automation to the next level, by adding digitization, structuring, and intelligence to the picture.

Components of the IDP Solution

The document reading and extraction market today is highly fragmented. Different vendors provide different process flow and differentiated capabilities. Any comprehensive Intelligent Document Processing or IDP solution will comprise of five critical flows – Data Ingestion, Pre-Processing, Document Classification, Data Extraction, and Validation or Feedback Loop.

Data Ingestion: Any IDP solution needs to be able to read different documents using OCR or other powerful ML algorithms. Normally, any data captured by an organization can be categorized as Structured (fixed structure and hierarchy), Unstructured (unorganized and multi-format, free form), and Semi-structured (blend of structured and unstructured data). Market research shows that more than 75% of data in the world is either in an unstructured or semi-structured format. While it is easy to read and categorize data in structured formats, like Excel tables, reading data in unstructured and semi-structured formats requires the use of AI based solutions – such as Optical Character Recognition (OCR), Computer Vision (CV), and Natural Language Processing (NLP). OCR detects language-related characters, letters, numbers, etc. by depending on structured data tables. However, with CV and NLP, the capabilities of OCR to handle unstructured and semi-structured data have undergone a paradigm shift, resulting in a set of solutions called Intelligent Character Recognition (ICR).

IDP solutions capture the data extracted by ICR and enable the system to structure it based on the data types, regardless of whether the documents are structured, unstructured, or semi-structured. Adding ML algorithms on top allows the solution to learn from training with the manual corrections made every document-reading iteration.

Pre-processing: IDP may need to run on handwritten documents, or scanned images or computer generated PDG files. Each of these have a different standard of quality. Before any data can be extracted then, a document needs to be evaluated from a quality assessment standpoint – including cleaning, organizing, and transforming the raw data to meet the quality parameters mandated by the IDP or machine learning models.

Some popular preprocessing methods used by IDP tools include:

• Data annotation and labeling: A configuration process where specific document types, including document fields, get tagged and annotated to help with classification.
• Merge/split documents: Capability to analyze multi-page documents to recognize the layout to detect when documents need to split or analyze multiple documents to recognize when they need to be merged into one.
• Skew correction: Ability to recognize when a scanned document is not upright and is skewed, where the text appears rotated or tilted in different angles. Capability to carry out a skew correction or de-skewing.
• Noise Removal: The process of detecting and removing unclear sections, such as black dots, shadows, or blurs, and cleaning them up to ensure better quality. Noise removal methods include median filtering, edge detection, linear regression and auto-encoding.
• Data validation and correction: Compatibility check of the document, format validation, resolution specification.
• Taxonomies and ontologies: Collection of data in categories, and the identification of a pattern of various entities and relationships among the data

Any IDP solution should be able to take up pre-processing to ensure data quality for extraction.

Document Classification: When a back-office process requires multiple types of documents or multiple types of information to be captured – these need to be classified correctly by the IDP solution to be able to extract the relevant information. This could be the classification of multiple documents or even the classification of pages or sections within a single document basis the data type being captured.

The classification module of an IDP solution is based on a combination of NLP, ML algorithms, deep learning, and other AI technologies. In today’s IDP market, a classification does not just identify the content type within a document and categorize it but aims to achieve intelligent classification by looking at additional contributing parameters of classification, such as date ranges.

Data Extraction: The actual data extraction element of an IDP solution goes beyond traditional OCR. Since OCR focuses on character recognition, it is limited in terms of intelligence of what a data point indicates. With the evolution of technology, businesses are looking to neural networks and algorithms for natural language processing or computer vision to go from OCR to intelligent data extraction.

An efficient IDP solution addresses many extraction challenges:

• Textual data extraction: Use of entity extraction models to identify and segregate sets of information based on similar or common semantic parameters. For example:
  1. Key value pairs
  2. Entity recognition
  3. Questions and answers
• Visual data extraction: Understanding of visual elements such as tables, graphs, checkboxes, logos, and signatures. IDP solutions focus on the following during extraction:
  1. De-noising irrelevant content, and detecting the region of the visual element presence accurately;
  2. Detecting elements with multiple layouts and mostly different variations;
  3. Detecting the exact boundaries, and segmentation based on semantics;
  4. Detecting sub-elements in the region of interest and extracting information from them, such as rows and columns for tables; and
  5. Decoding the structural relationship of the information

Validation and Feedback Loop: The key evaluation parameter of any IDP solution is the accuracy of extraction. Unlike OCR, IDP looks beyond quality of extraction to data validation against external sources to improve accuracy. An efficient data validation system can improve IDP accuracy by almost 5-10%. Data validation can be dictionary-based, context-based, or pattern-based. Most IDP solutions validate data against defined business rules.

Another method used by IDP solutions to improve accuracy is the incorporation of feedback loops. Any corrections performed by a user carrying out quality checks can act as an input to the system so that the accuracy can be improved for future extractions. Modern ML-based IDP solutions automatically learn from manual corrections to ensure the accuracy of future extractions is improved.

Author:

Nandhagopal Muralithar is a Senior Business Consultant at IGT Solutions’ Intelligent Automation and Analytics Practice. With specialized experience of 7 years in Digital Transformation Consulting across Travel, Hospitality and Retail domains, Nandh has worked extensively on the front edge of process and conversational automation technologies, designing and delivering innovative back and front office automation solutions.

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