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Category: Automation

Closer Than You Think: A Business That Could Think, Learn, and Act on Its Own

Closer Than You Think: A Business That Could Think, Learn, and Act on Its Own

March 27, 2026

Executive Summary Businesses today are moving beyond automation toward systems that can think, learn, and act independently. This shift is driven by intelligent technologies that combine data, decision-making, and execution into a single flow. Instead of managing tasks manually, organizations are adopting systems that continuously optimize outcomes. Introducing Vyom AI So what does it actually mean for a business to think, learn, and act on its own? Vyom AI is an intelligent system designed to bring together data, decision-making, and execution into one unified flow. Unlike traditional tools that require constant input, Vyom AI enables businesses to operate with minimal friction by continuously learning from data and adapting in real time. It doesn’t just support processes. It transforms how processes work. The Shift from Automation to Intelligence For years, businesses have tried to automate business workflow processes to improve efficiency. While automation reduced manual effort, it still depended on predefined rules. Today, that approach is evolving. With agentic AI in business, systems no longer just follow instructions. They: This marks a shift from automation to intelligence. Build a business that thinks, learns, and acts, and connect with our experts to unlock intelligent automation with Vyom AI. Why Traditional Process Automation Falls Short Most organizations rely on automated process management systems to handle repetitive tasks. These systems work well for predictable workflows but struggle when complexity increases. Common limitations include: As businesses scale, these limitations become bottlenecks. How Intelligent Systems Redefine Workflows Modern enterprises are adopting process workflow management software that integrates intelligence directly into operations. Instead of managing isolated tasks, these systems: This is where agentic AI in business becomes critical. It allows workflows to evolve dynamically based on real-time inputs. Step-by-Step: How Intelligent Workflow Systems Operate This transforms a traditional business process automation workflow into a self-improving system. From Workflow Automation to Autonomous Execution The next stage of evolution is not just automation but orchestration. With advanced workflow and process automation, businesses can: Instead of managing workflows, businesses begin to guide outcomes. Key Takeaways Conclusion The idea of a business that can think, learn, and act on its own is no longer futuristic. It is becoming a practical reality. As organizations grow, the ability to integrate intelligence into workflows will define how effectively they scale. Systems like Vyom AI represent a shift from managing processes to enabling outcomes. The future of business isn’t just automated. It’s intelligent. Ready to move from automation to intelligence? Discover how Vyom AI can transform your business workflows today. Let’s Connect FAQs

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The Ethical Implications of Generative AI in Business Applications

The Ethical Implications of Generative AI in Business Applications

March 24, 2026

Executive Summary Generative AI is transforming business operations by enabling automation, efficiency, and faster decision-making. However, its rapid adoption introduces critical ethical challenges, including bias, lack of transparency, data privacy risks, and unclear accountability. Without proper governance, AI can scale errors and erode customer trust. Organizations must balance innovation with responsibility by implementing strong oversight, auditing data, and ensuring human involvement in decision-making. Ethical AI is not a limitation but a strategic advantage that builds credibility and long-term value. Businesses that embed ethics into their AI strategy will not only innovate effectively but also establish trust as a core differentiator in the AI-driven future. Ethical Implications of Gen AI in Business Applications Generative AI is no longer experimental. It writes content, analyzes data, builds code, answers customer queries, and even supports decision-making. For many organizations, it feels like a competitive shortcut. But here’s the real question: Just because we can automate something, should we? As artificial intelligence in business becomes more embedded in daily operations, ethical considerations are no longer optional. They are strategic necessities. Why Ethics Matters More Than Speed Businesses are rushing to integrate AI into marketing, operations, customer support, and analytics. The pressure to stay competitive drives rapid AI adoption in business, but speed without responsibility creates risk. Generative AI systems: Without clear governance, what starts as innovation can become reputational damage. Bias: The Invisible Business Risk Generative AI models reflect the data they are trained on. If that data contains bias, the output can reinforce it. In hiring systems, customer profiling, or financial assessments, this can lead to unfair outcomes. And unlike human bias, algorithmic bias can scale instantly. Businesses must: Ethical AI is not about slowing innovation; it’s about safeguarding trust. Connect with our experts to build responsibly. Transparency and Accountability Customers increasingly expect to know when they are interacting with AI. Whether it’s a chatbot, automated recommendation, or AI-generated insight, transparency builds credibility. Organizations using AI platforms for business must clearly define: Without accountability, automation weakens trust instead of strengthening it. Data Privacy and Security Concerns Generative AI relies heavily on data. That raises critical questions: As companies expand their use of ai platforms for business, governance frameworks must evolve alongside technology. Compliance cannot be an afterthought. Responsible implementation requires: Human Oversight Still Matters One common misconception is that AI replaces human judgment. In reality, generative AI works best as an augmentation tool. Ethical use of artificial intelligence in business depends on maintaining this balance. Building Ethical AI Into Strategy Responsible AI adoption starts with intentional design, not retroactive fixes. Before deploying generative AI, organizations should ask: Businesses that address these questions early position themselves not only as innovators, but as trusted leaders. The Bigger Picture Generative AI is powerful. It accelerates workflows, improves efficiency, and unlocks new possibilities. But technology without ethics creates uncertainty. The companies that win in the AI era won’t be those who adopt fastest. They’ll be those who adopt responsibly. Because in business, trust scales faster than technology ever will. Generative AI can accelerate growth when implemented responsibly. If you’re evaluating how to integrate AI into your operations while maintaining governance and trust. Balance speed with responsibility, build AI systems your business and customers can trust. Let’s Connect FAQs

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Streamlining Claims Processing

Streamlining Claims Processing: The Role of Reporting Automation in the Insurance Industry

July 5, 2024

Cutting-edge technology is steadily becoming a buzzword for the insurance industry. Artificial intelligence (AI) and automation are some of the most potent tools that are revolutionising insurance processes, while unlocking numerous benefits for not just insurers, but their customers too. Reporting automation is one of these new-age innovations that have a vital role to play in streamlining multiple tasks, right from claims processing to underwriting. What is Reporting Automation? The basic meaning of reporting automation is automatic generation and sharing of specific data to selected people as per a pre-fixed time interval. In this case, automated reports may take care of multiple aspects of operations, while also linking to diverse KPIs (key performance indicators) simultaneously, along with other time-dependent data. How Reporting Automation Helps Streamline Claims Processing and Other Tasks Reporting automation can play a major role in making claims processing simpler and less time-consuming for insurance companies, while also enhancing overall customer experiences greatly. It has steadily become a vital tool for enabling swift results, actionable insights, and reviews. Here are some key aspects worth noting in this regard. Here’s how reporting automation can greatly enhance the claims process: Claims Processing Simplified with Reporting Automation With suitable reporting automation tools, insurance can handle and process claims better. The overall process usually involves multiple documents and details, while taking up sizable time in most scenarios. In this process, customers will be looking for reimbursements for damages and losses covered in their policies, while seeking settlements likewise. The conventional method involves accessing data from numerous sources, evaluating the same, and then working out the payouts for claims, and also weeding out data that is not just outdated, but also inaccurate. Here are some aspects worth highlighting here. Signing Off Hence, as can be seen, reporting automation has a crucial role to play in simplifying and streamlining claims procedures throughout the insurance sector. The usage of AI and automation also comes with several other benefits, ranging from fraud detection and better underwriting to lower costs and manual tasks for insurance companies. These tools even help in extracting vital data from unstructured sources like social media posts, emails, and more. This helps insurance companies swiftly respond to the queries of their customers, thereby enabling higher satisfaction levels and overall retention in the long run. Personalised claims processing, service, and offers are also facilitated through the usage of automation. FAQs 1. How does reporting automation speed up claims processing? Reporting automation helps speed up claims processing tasks considerably, through enabling quicker decision-making after gathering and analysing data. This helps customers obtain swifter settlements as a result. 2. With sensitive information involved, how does reporting automation ensure my data is protected? There will be several measures like classification and discovery along with centralised repositories for enterprise/business-wide auditing trails. There are other measures like access control and encryption to safeguard sensitive data. 3. Does this technology work for different insurance categories (e.g., auto, health, property)? Reporting automation works for various insurance categories, simplifying claims settlement and processing along with underwriting, and personalisation of offers and products for clients. 4. What role does data visualisation play in reporting automation for claims processing? Data visualisation enables clear visibility into crucial metrics for performance, including average settlement amounts, claims processing timelines, customer response timelines, and so on. This naturally has a positive effect on expediting the resolution and settlement of claims.   5. What are some examples of successful implementations of reporting automation in insurance claims processing? Some of the examples of successful reporting automation for insurance claims processing include data extraction, digitisation of documents, submission of claims electronically, fraud detection, and more.

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Cognitive Automation

Cognitive Automation with AI

April 8, 2024

Cognitive automation is a major buzzword these days. It combines AI (artificial intelligence) and process automation abilities for enhancing outcomes in business. It represents a wider array of approaches which boost the capabilities of automation in data capturing and automated decision-making along with enabling scale automation. Instances of Cognitive Automation Cognitive automation can be delineated into several examples. These include intelligent process automation (IPA), DPA (digital process automation), intelligent business process automation, cognitive service, and hyperautomation. It may also encompass the following aspects: Functioning of Cognitive Automation Cognitive automation indicates abilities which are provided as a part of a customised service or commercial software package. Basic services in this category enable customised offerings instead of relying on those designed from the ground-up. Business users can seamlessly customise and provision cognitive automation. Some uses may be delineated as follows: Cognitive Automation Advantages Some of the key advantages of cognitive automation include the following: Watch-Outs for Business Some of the key watch-outs for companies deploying cognitive automation include the following: Cognitive Automation and RPA- What Are the Differences? A few core differences between cognitive automation and RPA should be understood in order to build context. These include the following: As can be seen, cognitive automation is applicable in the real-world ecosystem throughout various sectors. This includes everything from processing loans and accounts payables for financial institutions to automated onboarding of employees and even payroll. It may also enable improved sentiment analysis or opinion mining as it is called. This helps determine sentiments in various input sources and the emotions/opinions/attitudes/perceptions are classified by ML and AI algorithms. It naturally gives a booster shot to customer engagement and experience for companies. They can provide more personalised and quicker support for improved customer journeys. These are systems functioning on the basis of natural language understanding, which means that they can easily tackle queries of customers, provide recommendations, and help with various tasks. Hence, with the growing inclination of companies towards unearthing valuable insights, trends, and patterns from multifarious and voluminous datasets, cognitive automation has a bigger role to play in the future. It will also help them adhere to regulatory compliance through the interpretation and analysis of complex policies and other regulations. They can be implemented easily into workflows, helping companies find major risks, track adherence to compliance, and also identify potential errors, missing data, or fraud. From this standpoint, it can be stated that strategic implementation of cognitive automation is the need of the hour. FAQs Can cognitive automation be applied to various industries, or is it industry-specific? Cognitive automation can be leveraged throughout multiple industries. These include all customer-facing sectors including financial services, banking, and even customer support and service at companies in all sectors. How does cognitive automation impact job roles and workforce dynamics? Cognitive automation can lead to a major productivity boost while unlocking newer opportunities for employment. It can automate mundane and otherwise time-consuming tasks, while also freeing up employees who can focus on more value-added jobs and complex activities. This may lead to better engagement and job satisfaction alike. How does cognitive automation leverage natural language processing (NLP) in interactions with users? Cognitive automation adopts a knowledge-based perspective or mission when integrated into contemporary workflows. It makes use of advanced techniques like natural language processing (NLP) for its user interactions. It can thus offer better advice and recommendations along with guiding users towards the information that they require in order to take better decisions. This is also fused with text analytics, semantic technology, data mining, and machine learning. How do organizations measure the success and ROI of cognitive automation implementations? There are several ways of measuring the ROI (return on investment) and success of implementing cognitive automation. ROI may be calculated at the outset through deducting the investment costs upfront from the final value while dividing the new figure by the investment cost. It has to be multiplied by 100 in order to know the final percentage. Another way is to undertake a thorough comparison of the processes at the company in terms of the future and current states. Companies can measure the success of cognitive automation through measuring cost savings (comparison of manual process costs to automated process costs). They can also track overall productivity on account of employees being freed up to emphasize strategic tasks and duties. It can be monitored through evaluating the time spent by employees on manual tasks after and before automation. Cognitive process information may also enable higher accuracy through lowering the count of errors across manual procedures. It can also be examined through tracking the count of errors prior and after automation. Other options include tracking lower risk incidents and customer satisfaction. Can cognitive automation work alongside human workers in collaborative environments? Cognitive automation can function seamlessly alongside human workers in environments that are more collaborative. Humans can deploy cognitive automation for streamlining various tasks and enhancing efficiency and productivity.

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How AI and Risk Management Can Work Together?

How Can AI And Risk Management Work Together?

August 16, 2022

The AI risk management combination has been making waves in recent times. No, it doesn’t indicate any Man vs. Machine war in the future, or a takeover of the world by intelligent computing devices. What it does indicate is that AI (artificial intelligence) has been steadily rising up the ranks in terms of its applicability to varied functions. From personal assistants and self-driving vehicles to shopping, there are several functions backed by AI-technologies. In fact, AI-based models may help in training computers for the recognition and identification of risks and other complex scenarios. AI driven risk management is always beneficial for enterprises, helping in the smooth tackling and evaluation of data which is primarily unstructured, i.e. which does not fit into structured columns or rows. Cognitive AI tech including NLP (natural language processing) makes use of cutting-edge algorithms for unstructured data analysis. With estimates pegging 90% of business data in the unstructured category, cognitive AI may help in positioning enterprises better as compared to their rivals. Fintech players, banks, insurance entities, and other companies execute solutions for risk management with AI for enabling better decision-making, lowering credit risks, and offering customized financial solutions for customers. Machine Learning and AI For Risk Management- The Biggest Benefits From AI in credit risk management to overall enterprise risk management functions, there is a lot that can be accomplished in this regard. Here are some of the biggest advantages of using AI and ML for managing risks. Evaluating Security Threats and Their Management Machine Learning (ML) algorithms may help in the evaluation and analysis of data in sizable amounts from various sources. Real-time models of prediction created from this information enable security teams and risk managers to tackle threats swiftly. These models also double up as systems of early warnings and alerts, enabling seamless operations of enterprise, while boosting data protection and privacy alike. Lowering Enterprise Risks AI plays a vital role in enterprise risk management. It helps companies analyze unstructured information, identifying risky patterns, activities, and behavioral aspects throughout operations. ML-based algorithms may help identify earlier behavioral patterns of a risky nature, while transposing the same as models of prediction. Detecting Frauds AI-based models can help in lowering workloads for companies with regard to detecting frauds. These algorithms can help with text mining, social media evaluation, and searches across databases, while lowering IT-security threats considerably. Data Classification AI may help in the superior processing and classification of data as per pre-fixed classification models and patterns. It may also help in tracking access to the data sets accordingly. Management of Security for Events Using log data and specific events, teams can swiftly identify any risk triggers, patterns, and indicators. This helps enable better alerts and detection alike. Lowering Workforce Risks Workers in high-risk zones will benefit from the deployment of AI technologies. They can help in analyzing data linked to all activities in such environments, where accidents may become fatal or catastrophic. Through the analysis of behavioral trends before accidents, there could be predictive scenarios modeled for enhancing safety systems and reducing the risks of such incidents. Of course, there are still hurdles related to large-scale processing of data, especially in terms of its cost and also privacy-linked concerns. However, these may be ironed out in the near future, relying on ML and AI to become mainstream in the near future. This could be a shot in the arm for security and risk management teams across enterprises, lowering their workloads and scaling up process-based efficiencies considerably. About the author: Dipak Singh is a thought leader and data cruncher, currently, he heads the Analytics Wings at INT. To know more do check out his LinkedIn profile here.

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Sentiment Analysis

How Sentiment Analysis Can Drive Insurance Industry

July 11, 2022

Sentiment analysis in insurance is emerging as a potent tool for companies in multifarious ways. Insurance companies have tons of unstructured information that they have at hand.  Following a suitable sentiment analytics process may help insurers enhance retention of policyholders and also in the identification of opportunities pertaining to up-selling and cross-selling. Sentiment analytics have already turned into a vital aspect of strategies pertaining to customer feedback for companies of diverse sizes. Sentiment analytics in insurance fuse Machine Learning (ML) and Natural Language Processing (NLP) along with deep-text analytics for illuminating intrinsic nuances of texts.  Sentiments can be translated more easily and analyzed seamlessly than expressions. The sentiment analytics process is also known as opinion mining.  Customer data is unstructured and comes in several forms including claim data, voice messages, surveys, emails, social media posts. The entire system is tailored not only to analyze feedback and its nature, but also to put it against the right context. Benefits of Sentiment Analytics Insurers can reap multiple benefits from suitable sentiment analysis procedures. Here’s looking at some of them: 1. Detecting Fraud Reports indicate how insurers lose millions annually on account of fraud. These are estimated at anywhere around 5-10% of total compensation payouts by insurers in a year at least.  These are claims that flew under the radar. However, predictive analytics and other tools can help detect the same. A sentimental analysis dataset will help insurance companies track and assess insurance settlement and claim patterns.  It will help in quicker decision-making on the basis of crucial parameters or key performance indicators. This will help in arresting fraudulent claims and enhance the insurer’s earnings.  Text analytics also enables better decision-making through dashboards and access to other necessary data. 2. Customer Understanding  Social media sentiment analysis will help in the classification and identification of customer interactions on the basis of parameters like the services/products being provided, the marketing platforms or channels that are used, the operations in place and so on.  What sentiment analysis does is help insurers understand the voices of their customers.  It fosters superior customer understanding above everything else. Social media datasets will help in the identification of specific aspects concerning any product, process, or service.  Whenever this analysis is implemented for social media comments, it helps in clearly delineating trends in the industry and perceptions of companies along with enabling timely alerts on any reputation related issues as well. 3. Managing Claims The analysis of complaints and claims is another natural segment for using such datasets. Complaints may be automatically identified and classified on the basis of the service, product and other parameters.  This enables passing them onto suitable agents/departments in order to ensure swift action on the same. Relating those to real world Sentiment analysis in insurance reduces costs, combats fraudulent claims, helps insurance companies understand patterns, trends and customer preferences, and also lowers overall workload and the time taken to respond to customer issues.  Simultaneously, social media sentiment analysis helps in enhancing satisfaction levels of both employees and clients, while enhancing client retention, brand-building, recommendations.  It also goes a long way towards lowering indirect expenditure. Sentiment analytics can help insurance companies keep leveraging unstructured information for identification of revenue-enhancing opportunities and industry/customer trends.  Although analytics is not perfect as of yet, it is continually evolving towards the same. In this case, the sustained focus on a specific domain (insurance) can help in enhancing the overall accuracy levels as well. Indus Net Technologies offers an array of solutions tailored towards the needs of insurance companies and the industry at large, right from cutting-edge analytics and other technological tools to back-end automation, risk profiling, customizable analytics, and modernization of legacy applications.  Having worked on diverse task requirements for insurers over the years, INT has the ability to tailor industry and company-specific solutions that harness the power of data, free up company resources, and ultimately boost company revenues and growth alike. About the author: Dipak Singh is a thought leader and data cruncher, currently, he heads the Analytics Wings at INT. To know more do check out his LinkedIn profile here.

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Which-Events-Justify-Writing-a-Press-Release

Challenges In AI adoption In Traditional Financial Services Companies

March 7, 2022

In the financial world, good old banks running traditional core systems are facing an uphill task—how to navigate through an ocean of data to understand customer behavior and then use those insights to better their offerings. Developing a robust AI adoption strategy for financial services requires careful planning, but organizations must also address the challenges of AI in finance, such as regulatory compliance, data privacy, and model interpretability. The challenges of AI in finance highlight the broader challenges of AI, including data privacy, bias, and regulatory compliance. Welcome Artificial Intelligence (AI), which, unlike humans, can parse tons of data to help traditional financial service providers uncover new product and service opportunities, while also red-flagging anti-money laundering patterns and identifying fraud. And it’s a trend that is catching on fast. The use of AI in global banking is estimated to grow from a $41.1 billion business in 2018 to $300 billion by 2030. Per a McKinsey Global AI Survey report, nearly 60 percent of financial services companies have been utilizing at least one AI capability. Currently, AI technologies in vogue include virtual chat assistants for customer service interfaces, machine learning techniques to support risk management, and robotic process automation for structuring daily operations. Incumbent banks primarily have two sets of objectives to fulfill with AI. First, they aim at speed, flexibility, and agility, inherent in a fintech. Second, they must adhere to compliances, standards and regulatory requirements of a traditional financial service company. However, deploying AI to do the heavy lifting isn’t as easy as pushing a button. In fact, big challenges remain in building responsible and ethical AI systems and simultaneously, traditional financial institutions struggle to deploy in-depth AI capabilities to truly harness its full potential.  Here are some key challenges global financial companies face in implementing AI Data quality and weak core structures Research finds that the existing data sets in circulation are mostly third-party, unstructured, and a lack of due diligence makes it difficult for AI and ML systems to identify overlapping and conflicting entries. Also, the existing control frameworks lack support for AI-specific scale and volume. Plus, the algorithm results can even show biased results when written by developers with a biased mind. For instance, A 2020 report stated that Apple cards give upto 20 times less credit to women as the decision AI was fed with an untested, historically biased data set. Clearly, the financial domain lacks a clear and ethical AI framework to ensure data quality and strengthen the core data structures. Lack of standard processes and guidelines A clear strategy for AI in the financial domain is the need of the hour. Presently, the inflexible, incompetent and weak core structures are bound with fragmented data assets, hampering collaboration between business and technology teams, further resulting in outmoded operating models. It is pertinent that traditional financial organisations consider the context, use case, and the type of AI model implemented to analyse the appropriate approach while collaborating or upscaling their core tech systems. Lack of talent AI adoption maybe the talk of the town, but surveys evaluating AI success rates reveal a not-so-happy picture. Per O’Reilly’s 2021 AI Adoption In The Enterprise report, 25 percent of companies saw half their AI projects fail. Analysis reveals that a key reason for that failure is the lack of capable talent and the ability to reskill in line with a long-term vision. To make things worse, too many firms see talent strategies as an administrative hurdle versus a strategic enabler, resulting in a lack of proper framework around hiring and reskilling in the AI domain. Budget constraints An omnipresent challenge associated with AI investment is determining the source of money. Will it be an IT project, a change management project or an innovation project? The definitive answer is all three, but only a small fraction of the budget is assigned to AI projects. But there is some good news on this front. With organisations gaining interest, The Economist’s research team found that 86% of Financial Service’s executives plan to increase AI-related investment over the next five years, with the strongest intent expressed by firms in the APAC (90%) and the North American (89%) regions.  The road ahead A significant commitment towards AI investment is the need of the hour with a clear focus on bringing in the required human resource capabilities to the front. Businesses that scale with AI over time, with an unwavering focus on compliance, customer satisfaction, and retention will be the ones laughing all the way to the bank.

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