Category: Big Data

Analytics-Driven Personalisation: Redefining the Customer Experience in Banking

Analytics-Driven Personalisation: Redefining the Customer Experience in Banking

October 16, 2023

Analytics-driven personalisation is the biggest recent trend that has completely changed the game in terms of enabling personalised banking along with improved customer experience in banking. Digital transactions, payments, and banking platforms have completely changed the modus operandi as far as both customers and executives are concerned. At the same time, the higher digital engagement and transaction volumes lead to the generation of huge amounts of data on a daily basis. This is in the form of both non-transactional and transactional information.  Banks are now finding several merits in tapping and analysing this data to gain invaluable insights for positively transforming customer experiences and processes. Technologies like banking analytics are being used in tandem with machine learning, artificial intelligence, and big data analytics to generate the best possible results for banks in this context. Even McKinsey Global has stated how data-driven entities are 23 times likelier to acquire new customers, while being six times likelier to retain them and 19 times as likely to be profitable due to this aspect.  Another key aspect lies in the fact that banking analytics or data analytics in this segment had a value of approximately $4.93 billion in 2021 and is estimated to hit $28.11 billion within 2031 (indicating compounded annual growth rates or CAGR of 19.4%). There are several data or touch points for customers including websites, mobile apps, digital transactions, social media platforms and a lot more. Rich data can be used for redefining customer experiences while also predicting customer engagement and mapping the journey.  How Analytics-Driven Personalisation is the Key Factor When it comes to offering personalised banking and redefining customer experiences, big-data analytics is the key element that institutions are looking to leverage in the current scenario. Here are some pointers worth noting in this regard.  Several banks and financial institutions have multiple products for customers which cater to varying requirements. Redefining customer experiences thus becomes a major differentiator for these financial institutions in order to enhance customer satisfaction and retention levels alike. Gaining a better understanding of customers and identifying gaps or potential issues will also help improve the overall experience for customers while enabling more personalisation at the same time with full scalability.  What are the challenges of data analytics in banking?  There are a few challenges of leveraging banking analytics that institutions also need to be aware of. These include:  However, analytics-driven personalisation is the biggest trend that will completely reshape customer experiences across banks and financial institutions. Customers now engage across several touchpoints and expect more personalised banking solutions and quick assistance and support for their queries. Hence, institutions will have to rely more on data analysis and insights to make better decisions that lead to improved customer experiences and higher retention. However, maintaining a customer-centric approach is the biggest takeaway that banks should keep at the forefront while scaling up data analytics initiatives simultaneously.  FAQs Analytics-driven personalisation greatly enhances the banking experience for any customer. Banks get a full view of the customer profile and specific needs, pain points and requirements. Hence, they can customise their offerings and solutions to meet these needs while solving the pain points and making sure that the customer gets the right solutions at the right time.  Both transactional and non-transactional data are used for driving analytics-driven personalisation in banking. This includes data directly gathered from transactions across multiple channels and also other data from surveys, forms, websites, mobile applications, social media platforms and many other sources.  There are a few considerations and challenges that banks should keep in mind while implementing personalisation through analytics. Data quality and integrity should be a major focus area, since poor quality may completely jeopardise the whole process. Other considerations include data silos, gathering disparate data across systems, integration and dealing with legacy infrastructure.  With more personalised services and engagement, customer experiences naturally improve over time. This leads to higher loyalty and superior engagement since customers get solutions tailored to their needs and their pain points are addressed by banks swiftly due to analytics-driven insights.

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Analysing Unstructured Data in Life Sciences

 Analysing Unstructured Data in Life Sciences

July 28, 2023

Unstructured data analysis is a key talking point when it comes to the life sciences industry. The need for better life sciences data management has grown rapidly in recent years, with the help of better data integration and advanced technologies like machine learning, big data analytics, data visualisation, and natural language processing (NLP). Data scientists usually classify data as semi-structured, structured, and unstructured. Unstructured data represents information that has not been organised into any uniform format and hence is difficult to operate. It may include images, text, video, and audio materials. This data may come with semantic tags but may suffer from inconsistencies or the lack of standardisation. Unstructured data analysis cannot be neglected, since this data type is vital. This is usually extracted from human languages via natural language processing (NLP) and gained via sensors, scraped from the web or databases, and so on. This data has vast benefits in terms of generating helpful insights for life sciences companies. Machine learning for identifying patterns & trends in unstructured data: Gartner has forecasted how the life sciences and healthcare segment will keep surpassing average growth in IT expenditure. This investment will be majorly targeted towards cloud transitions, digital care delivery transformations, data and analytics, virtual care solutions, and more. Here are some key points worth noting in this regard:  Natural language processing (NLP) is the cornerstone of extracting insights from vital text data. Here’s learning more about the same.  NLP for extracting insights from text data: Here are some points relating to natural language processing (NLP) which enables machines to interpret, understand, and generate human languages. Here are some points that should be taken into account:  The third step in the process is data visualisation. Here’s learning more about the same below. Data visualisation for communicating the insights from unstructured data to stakeholders Data visualisation is also a vital step for unstructured data analysis. It indicates data representation via the usage of various displays and graphics for communicating complex relationships and insights to stakeholders. Here are some aspects that should be noted in this regard:  Thus, automatic classification technologies driven by ML, NLP, visualisation, and other tools will enable the identification of trends and patterns throughout unstructured data. This will lead to better insights, usage, and decision-making throughout product development, patient care, safety, logistics, and various other aspects.   FAQs 1,What is unstructured data in the context of life sciences? Unstructured data for the life sciences industry is a form of data that is not uniform and may be hard to understand. It may have inconsistencies and may be hard to integrate or standardise.  2.What tools and technologies are available for handling unstructured data in life sciences? There are several technologies and tools used to take care of unstructured data in the life sciences industry. These include machine learning (ML), NLP (natural language processing), data visualisation, and artificial intelligence.  3. What are the potential benefits of analysing unstructured data in life sciences? There are several advantages of analysing unstructured life sciences data. These include identification of patterns and trends, generation of easy-to-understand actionable insights and faster decision-making as a result.  4. What are the challenges associated with managing and unstructured data in life sciences? Some of the challenges linked to the analysis and management of unstructured life sciences data include data silos, issues with visibility, collaboration throughout teams, data export and access issues, lack of data organisation and integration, and problems with its retrieval and classification.

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Blockchain's Impact on Data Interoperability in the Healthcare Industry

Blockchain’s Impact on Data Interoperability in the Healthcare Industry

July 26, 2023

Data interoperability is a buzzword that has gained traction throughout the healthcare industry. This usually comes with multiple levels, including semantics, foundational, and structural. At the same time, data standardisation and privacy are key concerns, since the system should enable interoperability of data with full consent, trust, and permission support.  Blockchain can thus add a whole new dimension to healthcare data management while solving several interoperability challenges simultaneously. It ensures a proper framework for anonymised information while making sure that the data cannot be modified, forged, or tampered with. Blockchain can thus add a whole new dimension to healthcare data management while solving several interoperability challenges simultaneously. It ensures a proper framework for anonymised information while making sure that the data cannot be modified, forged, or tampered with. Authenticity and data security are major worries that are solved with this technology and the owner of data through smart contracts can make it more accessible across treatment sites and providers in a selective way. Here are some of the vital aspects related to the impact of Blockchain on healthcare data interoperability.  Blockchain for clinical trials: Along with data interoperability, Blockchain has enabled better clinical trial management. Here are some core points worth noting in this regard:  Hence, Blockchain technologies can boost the quality and volumes of patients who are recruited for clinical trials. Distributed ledger technologies will help patients store medical information through anonymous mechanisms and it can be visible to recruiters who may reach out if the data is eligible for the trial. Blockchain for patient consent: Patient consent is another vital facet of healthcare data management and this is also enhanced by Blockchain. Here are some factors worth noting in this regard:  These are some of the ways in which Blockchain technologies contribute immensely towards enabling patient consent and putting data management in their hands.  Blockchain for drug supply chain management: Blockchain technology also helps immensely in terms of drug supply chain management. Here are some aspects that can be highlighted in this regard:  Hence, Blockchain technology is a game-changer in terms of ensuring seamless data interoperability in the healthcare industry and also ensuring better consent management, clinical trials, and drug supply chain management. The possibilities are endless in terms of reshaping and refining these systems to ensure the best possible outcomes for the sector.  FAQs 1.How does blockchain improve the accuracy and integrity of healthcare data during interoperability? Blockchain greatly improves the integrity and accuracy of healthcare information during interoperability. This is possible with the immutable nature of data that is securely stored on its networks. Blockchain enables one version of the truth that cannot be tampered with.  2.What are the potential cost savings and efficiency gains from implementing blockchain for data interoperability in healthcare? There are several benefits of using Blockchain for healthcare data interoperability. The first one is the higher efficiency involved in secure storage of immutable records with full authenticity and the second one is the lower cost involved in the process. Along with data security and authenticity, financial losses due to data breaches and losses are also prevented.  3.Are there any real-world examples or success stories of blockchain’s impact on data interoperability in the healthcare industry? There are numerous real-world success stories and examples of how Blockchain has positively affected data interoperability in the healthcare sector. For example, several healthcare stakeholders are already using this technology to ensure authentic data for clinical trials. At the same time, smart contracts are being used for securely storing patient data.  4.What are the main challenges in achieving data interoperability in healthcare, and how can blockchain help overcome them? Some challenges exist in terms of data interoperability in the healthcare sector and Blockchain solves them with ease. These include the absence of data standardisation, security, privacy, consent, and technological expertise. Blockchain ensures a secure and standardised way of storing verifiable records with full consent and privacy. 

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Sales Return Forecasting with Data

Mastering Sales Return Forecasting with Data: A Merchandising Guide

July 21, 2023

Sales return forecasting occupies a predominant position with regard to strategic decision-making, optimisation strategies, merchandising success, and overall business performance. Businesses would do almost anything to get accurate and predictable forecasts of sales and revenues by leveraging data. This will not only help in drawing up annual budgets, but also boost overall performance through plugging loopholes and staying ahead of trends. Sales return forecasting is a specialised technique that also influences future growth and expansion plans. Here’s a closer look at the importance of the same. The Importance of Sales Return Forecasting Sales return forecasting is indispensable for companies in an increasingly cut-throat environment. They can enable effective forecasting through leveraging data and using advanced forecasting techniques and models. This is how it is beneficial and important for companies in merchandising: But how does data slot into the picture here? Here’s finding out. The Power of Data-Driven Sales Return Forecasting Accurate and powerful sales return forecasting is only possible by leveraging data. It is data which is king here and hence it possess the capabilities to empower better and more accurate forecasts. Here are a few points worth noting in this regard: These are some of the data types that can help companies enable more accurate sales return forecasting. But how does it shape up in the future for the merchandising space? Here’s taking a closer look. The Future of Sales Return Forecasting The future of sales return forecasting will primarily be driven by several factors, including the following: However, while sales return forecasting has its clear benefits for organisations, there are a few challenges worth considering too. Here’s looking at the same. The Challenges of Sales Return Forecasting Here are some hurdles linked to sales return forecasting that companies may have to contend with: However, these challenges can be surmounted with the right training, technological tools, and investments in building forecasting solutions for the future. Companies are increasingly depending on tech and data-driven sales return forecasting for better merchandising success and overall business growth. FAQs 1.How does the accuracy of sales return forecasting impact business performance? The accuracy levels of sales return forecasting have a direct impact on business performance, since accurate forecasts enable better decision-making on future expansion and operations. They also enable better budgeting, planning, resource allocation, procurement, future demand anticipation, and identification of potential issues.  2.What are the key considerations in selecting the right forecasting models for sales returns? The key considerations include the forecasting context, availability of proper historical and other data, relevance of the forecast, accuracy degree, time period, benefit/cost of the forecast to the organisation, and the time at hand for the analysis in question.  3.What strategies and technologies can help address the challenges of sales return forecasting? Some strategies include maintaining proper quality and relevance of historical data and using qualitative data in the process too. Other approaches include better communication throughout departments, accounting for seasonal variations and trends, and also removing stockout periods from forecasts. Some technologies that can address challenges in the space include artificial intelligence and automation, along with machine learning and data analytics. 4. How can companies leverage historical data and trends to improve sales return forecasting precision? Companies can tap historical trends and data for enhancing the overall forecasting precision of sales returns. This is possible since accurate data will enable better visibility into expected future sales patterns and revenues. It will give companies an idea of the sales cycle, pipeline estimates, and what to budget in the coming timeline. 

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InsureTech Insights: Leveraging Alternate Data for Risk Assessment

InsureTech Insights: Leveraging Alternate Data for Risk Assessment

July 12, 2023

InsureTech is the latest buzzword that is making the headlines in the insurance sector and with good reason. From suitable risk assessment using alternate data to tapping big data analytics and artificial intelligence in insurance for better outcomes in diverse arenas, insurers are expected to step on the gas further across the next couple of years in this domain. Here is a brief glimpse into the same. 1.What are the key drivers of InsureTech? These are some of the major driving forces behind the InsureTech revolution that is sweeping the world today. Let us now learn a little more about the deployment of artificial intelligence in insurance. 2. How is AI used in insurtech?  Artificial intelligence in insurance and InsureTech are symbiotically linked due to the multifarious applications and use cases that have transformed the industry in recent years. Here are a few aspects worth noting in this regard: AI is beneficial for the entire InsureTech ecosystem in multiple ways, as is mentioned above. A closer look is also necessary at the various sources or types of alternate data that insurance companies can use for better risk assessment. 3.What kind of alternate data can help towards solving the credit risk? FAQs 1.What are the privacy and ethical considerations associated with using alternate data in risk assessment for InsureTech? InsureTech players must address privacy, ethics, and data validity when using alternate data. Key considerations include responsible data collection and usage, obtaining consent, ensuring analytical tool validity, fairness, and unbiased systems, data quality, regulatory compliance, and full disclosure principles. 2.Are there any successful case studies or real-world examples of InsureTech companies leveraging alternate data for risk assessment? There are many examples of InsureTech entities making use of alternate data for risk assessments. ZestFinance, for instance, deploys AI for evaluating both traditional and non-traditional information to gauge risks while automating its underwriting procedure for lower risks. Nauto has already been using AI for forecasting purposes. The aim here is to avoid collisions of commercial fleets (driverless) by lowering distracted driving. The AI system uses data from the vehicle, camera, and other sources to predict risky behavior. 3. What future trends do you foresee in the use of alternate data for risk assessment in the InsureTech industry? There will be greater emphasis on leveraging telematics and usage data garnered through connected vehicles and IoT devices along with smart home devices. At the same time, more machine learning models will be used for algorithm-based risk assessments. The Metaverse will be another channel for insurers to combine their AI-backed Chatbots with sales pitches, internal training, data gathering, and even NFTs for personal document verification. 4. Are there any challenges or limitations in leveraging alternate data for risk assessment in InsureTech? There are a few limitations/challenges in using alternate data for assessing risks in the InsureTech space. The quality of the data and whether it tells the whole story is one challenge along with the fact that there are ethical and privacy-related considerations, regulatory aspects, and the issues related to disclosures, user consent, and the methods of gathering data.

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future of customer acquisition in banking and finance (bfs)

Expanding Horizons: Enhancing Customer Acquisition with External Data in BFS

June 26, 2023

Customer acquisition is a vital aspect for any BFS entity. There are instances where tapping external data for the same has proved to be a bigger value proposition for these companies. Here’s taking a closer look at the same. How to Use External Data for Customer Acquisition in BFS As is evident, external data is increasingly proving to be a game-changer for banking and financial services entities. It is helping them get a better profile and view of the customer. This is naturally enhancing customer acquisition efforts greatly, helping personalise products/services along with interactions. It is naturally leading to higher customer loyalty and retention. The Benefits of Using External Data for Customer Acquisition Customer acquisition will increasingly be driven by the need to gather sufficient data about customers and then personalise their journeys. This will be the guiding principle for banking and financial services companies in the future. FAQs 1.What types of external data are commonly used to enhance customer acquisition in the BFS sector? Some external data types include geopolitical and economic data, historical data, weather data, satellite imagery, demographic data and so on. 2.What are some specific examples of how external data has been successfully utilised to enhance customer acquisition in BFS? External data can help companies understand customers better in relation to external events and factors. It helps predict market and consumer behavioral patterns and other dynamics. 3.What privacy and data protection measures are in place when using external data for customer acquisition in the BFS industry? Companies should follow strict data privacy protocols including informed consumer consent while gathering data, encryption, multi-factor authentication, transparent privacy and usage policies, and so on. 4.What are the challenges or considerations when integrating external data into customer acquisition strategies in BFS? Some challenges include data quality and delivery issues along with privacy and security risks. The absence of actionability may be another challenge, in addition to resourcing-related constraints.

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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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For Enterprises, what does it mean to be AI ready?

February 28, 2020

It would be so cool if we can ask Amazon what shoes should I buy, ask Siri about the cause of the delay in my cab or even request Google AI to fix my fuse. AI is expected to dramatically reshape fundamental business processes that serve faithfully in the background, enabling digitization to fully penetrate key business interactions and transactions. Why do enterprises need AI? Businesses today need to run at the same pace to stay in this competitive market while balancing the wavering factors like knowledge retention, sustainability, scalability, etc. To achieve the pace and agility, an enterprise requires exemplary harmony, self-governing data, content and strong management in such a way that it provides meaningful support to all the business problems. As the volume of this data keeps increasing exponentially, it becomes a little difficult to derive valuable insights from them. As a result, it affects the decision-making process. AI is helping in creating the maximum opportunities by being a great business driver. A survey conducted by BCG (Boston Consulting Group) among 112 CIOs across multiple industries saw that Artificial Intelligence technologies could significantly improve the cost-effectiveness and performance of IT operations, allowing organizations to stimulate innovation rapidly without making any sacrifice on service, security, or stability. Where does the setback originate? AI has proved itself to be revolutionary but if we look at the other side, it leads to certain setbacks too. Data crisis can lead to hazardous errors Development and training go hand in hand. When you develop an AI-based product, it needs to be supported by both monetary and non-monetary factors like training. A recent failure tasted by IBM purely justifies the statement. In 2013, IBM partnered with The University of Texas MD Anderson Cancer Center and developed a new health care system called “Oncology Expert Advisor” with a mission to eradicate cancer. In July 2018, it was found that the AI was recommending erroneous treatment solutions. The technical experts suggested that the main reason behind it was the lack of training on real cancer patients. Too fragile to respond to the supporting data There are several instances where the outcome of AI and machine learning proves to be biased, sexist and misogynistic. There cannot be a better case study than Amazon’s AI for recruitment to justify the statement. Amazon has developed an AI that shortlists the best 5 candidates out of the given 100 resumes. They trained their AI based on current engineering employees who were male and white. Therefore AI learned that white men are the perfect fit for engineering jobs. Lacks a mind of its own AI is artificially sensed, as it does not have a brain of its own. It was witnessed in the case of our commuter partner Uber. Uber’s self-driving car was running at a speed of 61kmph and could not recognize the lady who appeared from nowhere in the dark, resulting in a crash. In the previous couple of years, enterprises have seen many cases of AI failures. The tiniest of loopholes can lead to big problems. The product managers need to spend the maximum time testing the product. Can AI still be game-changing? There are two areas of artificial intelligence that are most applicable, such as Machine learning and Natural Language Processing. Machine learning is a subset of AI techniques that uses statistical methods to enable machines to improve with experiences. Whereas Natural Language Processing is a subsidiary technology of AI that understands and responds to everyday conversational language such as Alexa, Google assistant, chatbot. These artistic features are world-changing algorithms. According to a report, It is claimed that, by 2035, Artificial Intelligence will have the power to increase productivity by 40% or more. Enterprises who have explored the core use of artificial intelligence have reached a long way. There are a few examples that satisfy the statement pretty well. Dominate the global business The e-commerce giant Alibaba used Artificial intelligence and machine learning to expand its business operations all over the world. They collect the data related to the purchasing habits of the customers. With natural language processing, it automatically generates product descriptions for the site. It has also used AI algorithms to reduce traffic jams by monitoring every single vehicle in the city. Additionally, with the help of its cloud computing division called Alibaba Cloud, it is helping the farmers to monitor crops to improve yield cost-effectively. China is planning to be a dominant AI player and build an AI industry worth $1 trillion by 2030. Back up huge profits An AI software company, Sidetrade, has built a core AI platform known as AIMIE (Artificial Intelligence Mastering Intercompany Exchanges) that processes 230 million B2B transactions. That’s equal to around $700 billion sales and finance undertakings over the last three years. Web-crawling robots further enrich this data with 50 billion data points collected through websites, social networks, and online media sources that are relevant to the activity of 23 million European companies. What AI can do for your enterprise? If a business looks at Artificial intelligence with the lense of business capabilities, it will wipe off 50% off their pressure. AI majorly contributes to three major areas in business: Business process automation, Customer engagement, and Insight development through data analysis. AI can help an organization in creating new products, enhancing its features, providing a creative workspace to the employees by automating tasks, making better decisions, optimizing market operations, etc. In a survey conducted among 250 employees of a particular organization, the percentage of the contribution made by AI in an organization was found. Is your Organisation AI ready? Every enterprise needs to analyze if they are ready for the AI revolution. To bring AI into the business mainstream, companies need to complement their technology advances with a focus on governance that drives ethics and trust. If they fail to do so, their AI efforts will fall short of expectations and lag the business results delivered by competitors that responsibly embrace machine intelligence. Organization gotta embrace the

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