Artificial Intelligence Archives - Page 8 of 10

Yashica Chopra
October 06, 2021
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11 Extremely Helpful Use Cases of Computer Vision in Medicine

Computer vision identifies patterns in data using artificial neural networks. It helps doctors accurately diagnose complex health conditions and provide effective treatment to patients. Computer vision improves the quality of medical services. Science and technology are a part of our lives. But there’s one area that relies exclusively on them, and that’s the medical industry. Be it for research or healthcare, the medical industry is dependent on science and technology. The computer vision in the healthcare market in 2019 was valued at $445.6 million. It is expected to have a CAGR of 23.6% and touch $2.50 billion by 2028. Over the years, we have seen how new equipment and advanced tools are helping doctors and scientists to diagnose diseases accurately and provide better treatment. Artificial intelligence has begun to make a lot of difference in the medical and healthcare industry. From using robotic arms to perform surgeries to relying on computer vision for medical imaging, AI is enhancing the quality of healthcare services offered to the public. Artificial intelligence is an umbrella term that has various subfields interconnected with each other. If machine learning is one, computer vision is another. Artificial neural networks, natural language processing, and deep learning are other areas of AI used in various industries, either independently or in sync with different subfields. What is Computer Vision? We have mentioned computer vision as a part of AI. But what exactly is computer vision, and how does it help the medical industry? Computer vision has been designed to empower computers to recognize and understand images so that actions that are usually performed by humans are now done by computers. It is the process of automating tasks that were previously handled by humans. A computer vision consulting firm works on training and enabling computers to replicate human sight and understanding processes the way humans do. It is much more than simply copying human actions without understanding the reasons for it. Computer vision is already being used by platforms like Facebook and Amazon to provide personalized services. The technology is making progress in the medical industry to assist doctors and healthcare personnel in identifying patterns, detecting anomalies, and making accurate diagnoses to provide correct treatment to the patients. Evolution of Computer Vision Do you know that it took less than a decade for computer vision technology to be 99% accurate from being just 50% accurate back then? Computer vision was first used in the 1950s to distinguish between typed and handwritten text. Today, technology can process and analyze images to understand which action has to be performed. One of the primary reasons for the growth of computer vision is the increase in data. With billions of images being shared online every day, it has become necessary to use technology like computer vision to process these images and identify the objects/ data in them. How Does Computer Vision Work? In a way, the process is similar to recognizing and tracing patterns. We could say that computer vision relies on neural networks and algorithms that mimic human minds to observe images the way humans do. Facial recognition is one example of computer vision. Earlier, creating something like this needed a lot of manual work. And yet, the margin of error was high. But with machine learning algorithms and deep learning becoming more precise, the need for human intervention has reduced. However, if you want to invest in computer vision technology, you will need to hire CV experts to handle the systems. Many organizations, including the medical industry, are hiring computer vision services to enhance their processes and deliver high-quality services. Medical Fields Where Computer Vision is Used The medical industry is made up of several subfields, each of which focuses on different specializations. For example, dermatology deals with skin, cardiology is about the heart, and so on. Computer vision is used in each field in a different way to assist doctors to detect early signs of symptoms and prevent the disease from affecting the patient after they have recovered from it. Radiology Computer vision assists physicians to make the correct diagnosis, especially if he hasn’t come across a case like that before. The job of a radiologist is to- Dermatology Computer vision applications help dermatologists in three ways- Cardiology Computer vision applications and virtual reality hardware are used together by cardiologists so that they can- Embryology Computer vision is transforming IVF (In Vitro Fertilization) through- Use Cases of Computer Vision in Medicine 1. Accurate Measurement of Blood Loss Computer vision is now accurate enough (more than 99%) and is known to predict and measure data by analyzing images with great clarity. Gauss Surgical has developed a computer vision application that helps measure blood loss during childbirth. Excessive bleeding during childbirth has caused a lot of complications and the death of new mothers for centuries. This application helps measure the amount of blood loss so that the doctors will know when things will get risky for the patient. The CV tool uses studies of images of surgical sponges and suction canisters used during the delivery to determine if the patient is at the risk of suffering from excessive blood loss. 2. Minimizing False Positives False positives can prove to be costly, not only in terms of money but also in terms of life. Treating someone for a disease they don’t have but have been tested positive because of faulty analysis can lead to short-term and long-term ill-effects. AI Vision Consultancy services help healthcare centers develop computer vision tools that can process a vast amount of data and identify subtle changes in patterns. There are two advantages of using CV applications for testing patients. One is the reduction in the number of false positives, and another is the detection of minute symptoms that could, in all probability, be missed when using traditional testing methods. Instead of going through multiple tests to confirm whether they are really positive for a disease, a patient can rely on the reports generated using CV tools. 3. Timely Detection of Symptoms Patients suffering from cancer, Alzheimer’s, Parkinson’s,

Yashica Chopra
October 01, 2021
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Machine Learning is Right for Your Business: Is it True?

Machine learning can make an enterprise an industry leader by streamlining the processes and increasing returns. ML is right for every organization, and here’s why. Have you heard of the terms artificial intelligence and machine learning? Or, the correct question would be how many of you have not heard of them. Isn’t it? The interest in AI and ML has been steadily growing over the years. Many organizations have already started investing in ML technologies to improve their business operations. However, it is vital to understand what ML is, why your business needs machine learning, and what kind of technologies you should adopt. To make things easy for you, we’ll explain the various facets of ML in this blog. In today’s world, there is no shortage of data. The world has generated around 2.5 quintillion data bytes every day in 2020. Every user created 1.7 MB of data per second. Data is available in real-time for processing and analysis. This helps organizations around the world to derive accurate insights and use them to make decisions. But how is this data processed? Manually collecting, cleaning, and analyzing big data is not really possible and takes too much time and effort. Artificial intelligence tools and machine learning algorithms help replace the manual processing of data. Machine learning models can process data in real-time, identify patterns, and share valuable insights that help enterprises streamline their day-to-day business operations. In recent years, machine learning adoption rates have improved in all parts of the world. The machine learning global adoption rate is 45%, with another 21% exploring ML methods to get started soon. There are several practical applications and benefits of using machine learning in the business. It has become necessary to invest in artificial intelligence and machine learning tools to revamp the business process and compete in the market. Both SMEs and large-scale enterprises need to understand what machine learning is and how it can change their business for the better. But first, let’s start with the basics. In the first section, we’ll read more about machine learning, the definition, model, purpose, and more. Overview of Machine Learning Machine learning is an AI application that helps systems read and understand vast amounts of data. Machine learning is used to develop programs that can process and analyze data to deliver predictions and insights. The main aim of using machine learning is to empower a computer to learn and understand data in various forms. In short, machine learning helps computers process data the way a human mind does. Enterprises develop machine learning models customized to work with the type of data collected to make the business successful. Is machine learning AI or is machine learning the same as Artificial Intelligence? Machine learning is a subset of AI. The concept of machine learning is to empower machines to learn and adapt through self-training. It deals with the statistical methods and algorithms that teach a computer to understand and process data. This means that while machine learning can be called AI, the reverse is not true. It is an application of artificial intelligence where a machine is trained to think and make decisions like a human. AI has more components than machine learning. It is a broader subject. Artificial intelligence aims to make computers smart enough to solve complex problems without human intervention. In other words, Artificial intelligence creates intelligent machines, while machine learning trains these machines to become efficient and accurate. Machine Learning Models The machine learning algorithms are widely classified into supervised and unsupervised learning, semi-supervised learning, and reinforcement machine learning. 1. Supervised Learning This model uses the learning of the past and implements it on new data through labeled examples. It is used to predict future outcomes and trends. The input is mapped to output based on a given example of input-output pair. Regression Model: A regression model is where the output is continuous and is further subcategorized into the following- 1. Linear Regression This is where you find a line that best fits that data when you draw a graph with the X and Y-axis. Multiple Linear Regression (where a plane of best fit is determined) and Polynomial Regression (where a curve of best fit is found) are an extension of this model. 2. Neural Network We hear about neural networks more often than other models. It is a series of algorithms to recognize the several relationships and underlying connections between datasets. Neural networks are vital for deep learning. Each node is connected to the other to send data across the network. The network is trained using huge data sets and becomes accurate over time. Google’s search algorithm is the best example of a neural network. This model has been developed to resemble the operations and thought processes of the human mind. 3. Decision Tree This is a popular model where every question/ assumption is a node. The probable answers to each question will lead to more questions (nodes), and you finally have the answers (called leaves). This method considers all possible conditions before coming up with the final answer. The more nodes you have, the better will be your decision. However, this method is not always accurate due to the various factors involved. 4. Random Forest This is a collection of decision trees. However, random subsets of variables are selected at each step of the decision tree. Then the mean of predictions for each tree is selected. The final decision is selected depending on how many predictions are similar (same). This is a majority-based model that tries to eliminate the risk of individual error. Classification Model: A classification model is where the output is not continuous but discrete. It uses logistic regression the probability of outcomes in two. The output value should either be 0 or 1. Decision trees, random forests, and neural networks are a part of the classification model as well. The only difference is the change in output. 2. Unsupervised Learning The information used to train the algorithm is neither labeled

Kavika Roy
September 27, 2021
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8 Tips for Making AI Adoption Easier

How can organizations streamline AI adoption? Learn a few tips from artificial intelligence consulting experts on integrating AI in an organization and ensuring the adoption is successful. It’s not news that companies have been implementing Artificial Intelligence in their business process to optimize time, costs, and the staffing involved. AI solutions are taking centre stage for conferences and showing their vast potential across various industries, including retail and manufacturing. The processes in industrial facilities have been embedded with virtual assistants, while chatbots are installed to answer all the customer questions on e-commerce or other websites in general. According to a recent report, the global AI market is expected to grow by $76.44bn, progressing at a CAGR of 21% during the financial years 2021-25. Despite AI’s popularity in organizations and businesses being aware of its importance to remain competitive, AI’s adoption is not easy. Despite many AI services available to choose from and implement, companies find the process comprehensive and challenging. Right from data ingestion to model monitoring, each step of AI has its own advantage. However, it needs to be implemented in the right way. Tips for Making AI Adoption Easier Here are a few tips we gathered from artificial intelligence consulting experts on integrating AI in an organization and ensuring the adoption is successful. 1. Familiarity with the technology An organization has people employed from different backgrounds and work in various job profiles. Everyone must understand the need and importance of implementing a technology meant to improve growth and provide expansion for the firm. Experts suggest that it is essential for the firm and teams to become familiar with what modern AI is capable of. There’s an ocean of information available online. It is advisable for the team leads to familiarize themselves and share knowledge about predictive analytics and machine learning with others. 2. Identification of problems Once everyone is through with the basics, identify the issues that AI is expected to eliminate. Exploring the problems with different solutions helps in integrating the existing process with new ones. That way, the organization gets to keep the originality of the process in place while adopting AI and its capabilities to solve business problems. However, there’s one thing that MUST be considered to provide demonstrable value to the solution- AI implementation examples or use cases in the same industry or similar business. For example, suppose a retail shop owner wishes to use AI for his store. In that case, he may look for use cases of retailers or supermarket franchises that have implemented computer vision analytics for the growth of their business. 3. Find the internal capability gap There is a lot that goes into the process of AI adoption in the firm. To ensure everyone is on the same level, it is important to focus on what are the organization’s business goals that need to be achieved through AI implementation and the tools that you have to identify and analyze it. Identifying and addressing your internal capability gap would mean for you to evolve as a team. Depending on the business, there may be existing projects or teams that can organically support it. However, if there are multiple requirements and the team is short of skills or the knowledge to handle AI adoption single-handedly, AI consulting firms can provide an implementation plan. 4. ROI Yes, like every business deal, you need to assess the potential of the technology and the financial value of its implementation. Even successful AI projects take a long time to generate ROI. In fact, pilot projects may not yield any at all. However, pilot projects don’t cost much and can always be outsourced to AI and ML consulting firms. They can help you determine what type of data should be collected at higher volumes and identify the current gaps. The more you analyze, the more knowledge you tend to gain out of it, opening doors to a greater ROI in the long term. 5. Build an in-house team or partner with vendors While it is suggested for a company to build AI internally in the long run, there might be several limitations to these as well. For example, if a business falls under the shopping retail, SME, or manufacturing industry, hiring a whole team to look into the AI requirements doesn’t sound like the best decision. Not only will it require a lot of effort to onboard the suitable candidates, but also it will be financially draining. Partnering with an AI vendor to build customized AI tools is also the best option for a tailored solution. To cite an example, HSBC partnered with an AI vendor to develop an anti-money laundering tool even though they had an internal AI team available. Connecting with leading AI consulting services like DataToBiz can be the key to the right implementation of artificial intelligence technology in your processes. Talk to the AI engineer today. 6. Integrate data-driven decision making at all levels AI adoption in a firm is expected to improve daily operations by empowering people with data insights. Since people carry out daily operations, the firm must adopt a culture of data-driven decision-making, where people, right from those in the C-suite to those at the bottom, are made a part of it. When AI is adopted, the right way employees can augment their skills and judgment with algorithmic recommendations to achieve a better outcome than humans when doing it themselves. This can only happen if employees trust their AI tools and feel empowered to make decisions. Take, for example, a national supermarket chain where the manager needs to optimize floor space and product placement using existing data. The local managers using an AI tool can track real-time in-store customer behavior and decide where to place high-demand products during a particular time of the year. 7. Break down data silos AI requires a lot of data from many parts of the organization. The corporate departments store data in silos that don’t interfere and can only be accessed by specific teams. This has been a barrier

Yashica Chopra
September 15, 2021
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7 Ways of Using AI Marketing to Improve Customer LTV in Retail

Can you run a business without promoting it? No, right? You need to have a detailed marketing plan to attract new customers and retain the existing ones. Marketing is essential for a retail business to flourish in competitive conditions. When we talk about marketing and customers, things get rather tricky. Who is your target audience? What metrics should you consider when reaching out to the target audiences? Why is customer LTV important and yet undervalued until recently? Marketing relies on too many factors, and not understanding these factors can result in an unsuccessful or semi-successful marketing plan. Though several companies have understood the importance of data and are making an effort to collect it in real-time, the results are varied. Why? It’s because collecting data doesn’t automatically translate to understanding it. Unless the enterprise adopts the data-driven model by investing in artificial intelligence and machine learning, it is not possible to fully tap into the benefits of processing real-time data. AI solutions can help you understand your target audiences and customers. You can improve customer lifetime value using AI and ML technology and generate more returns for the retail business. How? When we talk about marketing and customers, things get rather tricky. What metrics should you consider when reaching out to the target audiences? Why is customer lifetime value important and yet undervalued until recently? How can AI solutions help you improve customer LTV and bring more returns? We’ll find the answers to these questions and more in this blog. Our focus will be in terms of the retail sector since customers are an integral part of the industry. Without customers, there is no retail sector, isn’t it? What is Customer Lifetime Value? Whether you call it CLTV or customer LTV, the term represents the amount of money a customer would bring to the brand during their entire time as a paying customer of the business. It tells us the value of a customer and helps decide how much you can invest to retain them with the business. CLTV shows us if a customer will repeat their purchases or not. A customer with a higher value is more likely to be a fan of your brand and will continue to make purchases (unless you do something to annoy them). Does it mean retailers do not have to focus on customers with lower value? Of course not. Marketing is sophisticated, and hence the need for artificial intelligence in retail. Understand CLTV and how it can impact your marketing plan, customer satisfaction, and profits. How to Calculate Customer LTV? Now that we know what customer LTV is, it’s time to see how this value is calculated. How do you decide whether a customer is valuable or not? How do we know if the value is high or low? CLTV is calculated based on four KPIs (Key Performance Indicators)- CLTV = AOV * PF * GM * (1/CR) One aspect you should remember is that the customer LTV should be higher than the customer acquisition cost (CAS). There’s no point in spending $100 if the customer’s LTV ranges around $50 or $70, isn’t it? Why Does Customer LTV Matter in Retail? Now, this is a question many retailers might have in mind, especially if you haven’t bothered about CLTV earlier. Your retail business might be booming, and in such instances, you might wonder if there’s a need to put so much effort into something like this. The answer is yes. Customer LTV matters to every retailer. Whether you know it or not, whether your business is highly successful or struggling to take over, CLTV should be one of the primary factors to consider when deciding a marketing plan for your business. Traditionally, retailers went with instinct and experience in assessing customers and their LTV. While we cannot completely ignore the business instinct, relying on it is not enough in this competitive market. That’s where AI in the retail industry is changing the way retailers make decisions. Artificial intelligence makes it easy for you to identify valuable customers and devise a marketing plan to retain them. AI helps in increasing customer value. The approach you follow can convince a customer to spend more and stay with your business for the long term. Customer LTV is necessary for retail for the following reasons- Speeds up Customer Retention CLTV helps you identify customers who are easy to retain and find a way to speed up the process. Your marketing strategy will be more effective when you know how much you should invest in acquiring a customer and the best way to go about it. Helps Incentivize Repeating Customers In the eCommerce market, the chances of selling to a repeat customer are as high as 60%-70%. However, the likelihood of selling to a new customer is around 5% to 20%. CLTV helps you understand your repeat customers and increase their value. AI empowers you to nudge these customers to spend more money, either by buying frequently or buying more (or both). Generates More Profits Most retailers follow the Pareto Principle. It means that 20% of customers bring 80% of the business. This principle helps in categorizing customers with higher LTV and spending more money on retaining them. Your marketing plan should have different strategies to acquire and retain customers from different categories. Increases Customer Loyalty Do you know that just a 5% increase in customer loyalty can lead to anything between a 25% to 95% increase in the average profits? This goes to show how vital customer loyalty is for every business, especially a retailer. Customer LTV helps you assess which customers can bring such profits. You can create a marketing plan to increase their loyalty towards the business. Remember that customer LTV doesn’t mean spending more on customer acquisition. It means spending carefully on each customer to increase the retention rate and purchasing frequency. AI services help you achieve this by targeting CLTV. How to Improve Customer Lifetime Value using AI Marketing in Retail The following are seven ways to use AI marketing to improve customer LTV and increase

Ankush Sharma
August 25, 2021
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8 Tips for Using AI to Improve Customer Insight Strategy

Artificial Intelligence or AI applied appropriately, can help deliver customer insights that deliver an in-depth understanding of purchase behavior, needs, likes, dislikes, and preferences. Do you know your customers? Do you understand what makes them buy your products or avail of your services? Do you know how many customers are unhappy with your business and looking at competitors? How do you plan to retain them? To successfully answer these questions requires customer insight. Customers are the heart of every business. In order to succeed, you need a loyal customer base to buy, promote, and recommend your brand. The demand and supply ratio in the modern market is constantly changing. People today have more options to choose from. While this is wonderful for consumers, it puts responsibility and pressure on businesses to ensure that their customers are satisfied and keep them happy. Whether it is product quality, variety, price, or post-purchase services, several factors play a role in influencing a customer. Sometimes, all it takes is one special offer by a competitor to lose a customer. In such instances, having a comprehensive customer insight strategy can help you retain your customers and attract new ones. However, traditional processes aren’t enough anymore. You need artificial intelligence solutions to use your customer insight strategy and obtain the expected results. In this blog, we’ll learn more about customer insight strategy, why it is necessary, and how AI can effectively improve the strategy and your business. What is the customer insight strategy? Customer insight means having an in-depth understanding of customers’ purchase behavior, needs, likes, dislikes, and preferences. It is to know whether a product will be loved by your customers or not. The insights are a combination of multiple factors like inquiries, feedback, customer analytics, market intelligence, research, segmentation, loyalty analysis, and more. Consumer insights play a critical role in marketing strategy development for your business. You need to use those insights to create a strategy that delivers results and puts your brand at the top. To survive the competitive market and set your business apart from your peers, you should compulsorily gather customer insights and create a strategy to achieve your goals. Ways to gather customer insights Customer insights aren’t generated of their own accord. You need to collect them using different methods. Each method has its pros and cons and focuses on revealing a new aspect about the customers. The following are customer insight techniques used by businesses around the world- Trend analysis This is classified into three types based on geographic location, time frame, and behavioral patterns. The current trends are analyzed to predict the future ones. Custom surveys These are conducted to get a general idea of how satisfied/ dissatisfied customers are with your brand. Market analysis It is a quantitative and qualitative assessment of the market to understand customer segmentation and purchase patterns. Interviews These are used to gather the voice of the customer to see if a particular product/ service is worth pursuing. Online research Using the internet to conduct surveys, polls, share questionnaires, and gather feedback is known as online research. Social listening This is a consumer AI technique where you monitor social media for mentions, tags, etc. You track your business as well as your competitors. Predictive analytics Using ML algorithms and AI to predict future trends based on historical and real-time data is known as predictive analytics. Focus groups You collect data by interacting with a selected group of customers/ target audiences. The need for customer insight strategy for businesses Before we talk more about using AI services for customer insights strategy, let’s see why you need to consider customer insights for your business. Customer insights and product strategy: Customer insight strategy provides you with relevant information about how customers will receive a product from your brand. You might be sure that customers will like a product you plan to release in the coming days. But what if they don’t like it? How will you know with certainty that the new product will be successful in the market? Customer insight strategy is the answer. You can collect firsthand information from the target audiences and use that to develop a proper product strategy. Customer insights and marketing: Customer insights analytics are used to help in planning your marketing strategy for existing and new products. How to take a product into the market and attract people? You use customer feedback to create a strategy that will highlight the products and the brand to show customers that you can provide them with what they are looking for. It also helps in segmenting customers into exact categories and creating targeted promotional campaigns for each segment. Customer feedback and customer service: Customer feedback and service go hand in hand. It is a part of the customer insight strategy to gather feedback from customers and make the necessary changes to the processes so that customers are happy with your business. You can collect feedback immediately after providing a service, contact them after a while, or wait for them to review your business. Find answers to the following questions If you want to use artificial intelligence to improve the customer insight strategy for your business, you need to first answer a few questions. These will help in providing you with a clear picture of how to implement AI in the business. What do you want for business? Unless you have a concrete goal, you cannot define the parameters to collect customer insights and use them to develop a strategy. You need to consider the short-term, mid-term, and long-term visions of your brand. Where and how to gather data? Which customer insight tools should you use to gather data? How do you plan to approach your customers? We’ve already mentioned different methods to collect customer insights. Besides the above, you can also use Google Analytics, social media analytics, social mentions, Google Trends, and other data collection tools to gather insights. Which data storage and data analytics tools do you need? The collected data requires to be stored and processed to derive insights. Do you want to

Kavika Roy
August 09, 2021
1 Comment

10 Steps for Turning Enterprise ML into Success

Artificial intelligence and machine learning (ML) are fast becoming a part of our lives, professional or domestic. Virtual assistants, chatbots, data-driven models for better decision-making, data analytics, and much more result from implementing AI and enterprise ML in the business. AI-powered tools streamline the processes and help enterprises improve quality, increase productivity, enhance customer satisfaction, and get more returns. SMEs can either build an internal team to integrate AI into their business systems or hire AI services offered by the leading consulting companies. Though AI and ML improve the performance of an enterprise, things can quickly go wrong if you don’t have a proper plan to integrate AI into your business. In this blog, we’ll read how to implement ML models in your organization successfully. We know about artificial intelligence and are aware that machine learning is a part of it. But what exactly does a machine learning algorithm do? A machine learning algorithm is software that uses historical and real-time data to help enterprises trace patterns, predict trends, detect fraud, analyze customer behavior, and provide personalized suggestions. The algorithm is written in a way that uses the feedback in the system to improve and deliver better results. Over time, enterprise ML algorithms can perfect themselves and provide accurate predictions. Of course, ML is much more than a simplified explanation. There are different types of ML based on the kind of algorithm used (or how the software approaches learning). Types of Machine Learning Data is a common factor for any machine learning. Reasons to Invest in Machine Learning With AI and ML making great headway in the market, it has become essential for several enterprises to invest in the technology. The following are some reasons you should invest in enterprise machine learning and how it can help achieve your business’s short-term and long-term goals. Inventory Maintenance Inventory management and maintenance is a labor-intensive and time-consuming process. It is even harder for large-scale enterprises with vast production. Machine learning simplifies the process by automating inventory maintenance to minimize the need for human intervention. Reduce Work Pressure When repetitive tasks are automated, employees do not have to spend most of their time doing the same thing repeatedly. Therefore, employees have more time to devote to their projects and less stress completing them on time. Also, with virtual assistants and chatbots enabling self-servicing within the Enterprise, employees can be empowered to become more productive without feeling the pressure. Market Analysis Machine learning algorithms can process data in real-time and detect the latest trends in the market. Suggestions about changing the product’s price, reaching out to a new target audience, managing demand with supply, etc., are possible. Data Sorting and Analytics AI and ML models are an inherent part of data analytics. From collecting data to cleaning and sorting it and data labeling, machine learning can make things easier and complete the task in less time. The data science teams can use enterprise ML models to analyze data faster than before. Decision Making AI and ML software provide accurate insights and predictions that help make the right decisions for the Enterprise. The reports produced are easy to read and can be presented in any format (graphical, tabular, textual, etc.) so that you can understand the insights and know where things stand. Data and System Security Data forms a vital part of every business and has to be protected from external forces. The machine learning algorithm can help enterprises enhance the overall security system in the business. The latest antivirus and spamware software is built using AI and machine learning to identify and prevent cybercrime before affecting the business. Fraud Detection and Prevention When discussing cybercrime, we should also mention fraudulent transactions commonly seen in the eCommerce, insurance, and banking industries. The machine learning algorithm can detect such transactions and alert employees. Many insurance companies and banking institutions have invested in ML-based fraud detection tools. Retailers and eCommerce business owners also integrate AI solutions with their business systems to prevent being duped by fake transactions. How to Ensure Enterprise Machine Learning Success Let us look at the steps for a successful enterprise ML implementation and getting the expected results for your business. 1. Understanding AI and ML and Becoming Familiar with Them The first step to implementing anything would be to know what it is. Unless you and your employees understand how artificial intelligence and machine learning can help you improve, adopting new technology will not help much. Several ML consulting companies assist right from the start and continue to offer support even afterward. They help in training your employees to work on the ML models and increase their work efficiency. Read the use cases shared by the consulting companies and download the whitepapers and understand them. Join online crash courses or training programs to gain a comprehensive idea about AI and ML. 2. Knowing Why You Need ML- Identify the Problems ML Will Solve in the Enterprise Why do you want to invest in Enterprise AI? Which issues do you want to tackle by integrating AI software into the business systems? Saying AI and enterprise ML will simplify the workflow is not enough. You must know where and how to use machine learning algorithms. Start by making a list of problems and gaps in your business system that can be solved using ML tools. Which problems can be solved using NLP, ML, DL, computer vision, etc., and what kind of AI tools will you need to integrate into the business? 3. Prioritizing What You Want from ML and Acknowledging the Talent Gap Investing in ML is a costly affair, whether you own a startup or a large-scale enterprise. You have to prioritize the areas where ML will first be implemented to scale it throughout the organization later. It would help if you also considered your existing talent pool. Can your employees adapt to the changing systems? How many will have to be trained in the first batch? How many new employees should you hire? Even if you

Kavika Roy
August 05, 2021
1 Comment

How to Use AI to Move IoT to Next Level

Enterprises around the world are widely adopting artificial intelligence. The Internet of Things (IoT) is not far behind either. In fact, with IoT making it easy to collect and share data across a network of devices, it has become a must-have for many industries. However, you need advanced tools and technology to process and analyze the data collected by IoT. That’s why large enterprises have been investing in artificial intelligence to streamline and automate business processes. As a result, Gartner has predicted that around 65% of SMEs and large-scale enterprises will be using IoT by 2024. Combing AI and IoT will take things to the next level by helping businesses use data in a way that’s never been done before. The combination of AI and IoT is a hit because IoT helps digitalize the physical world, while AI solutions make it easy to process and analyze a huge volume of data in less time. What is the Difference Between AI and IoT? Artificial intelligence is a mix of rules and intelligence. Rules are the part where programs are written. Intelligence is the use of machine learning and deep learning to understand data and make sense of it. Internet of Things is a network of several devices that are interconnected and constantly send information to each other. This makes the devices smart and allows you to control them from remote locations. Voice assistants and smart devices at home (smart locks, smart lights, etc.) are examples of IoT in domestic life. The main AI and IoT difference is that the Internet of Things is essentially hardware and network-based. Sensors, data collectors, computers, etc., are connected through a wireless network and send data to a central cloud server. Artificial intelligence usually works with data. Be it data gathering, cleaning, structuring, formatting, analyzing, or visualizing, AI is used to understand data collected by IoT. Internet of Things is less expensive compared to AI but has a higher success rate. The only way IoT can fail would be when the devices and components in the network are damaged or don’t work. However, AI has more chances of failure due to the wrong AI tool, incorrect data processing, or even inaccurate data. As long as you hire reputed AI strategy consulting companies to assist you, you can be assured that your investment will deliver the expected results. Reasons for the Surge in Demand for AI and IoT A survey by SADA Systems in 2018 showed that AI and IoT were the leading technologies that the enterprises adopted. The trend is probable to continue for the foreseeable future. So what resulted in this increase in demand for AI and IoT? AI as a service is helping streamline business processes and adopt the data-driven model to make better decisions in less time. From automating recurring tasks to providing virtual assistants for employees, AI increases productivity, quality and reduces costs. Internet of Things has made it feasible to develop new products and find new ways of distribution. Since devices and cloud computing technology are already available, IoT is very much a reality that’s delivering great results to industries. It is helping solve real-world problems ranging from traffic issues in cities to data sharing in remotely located factories. AI and IoT are providing enterprises with a definite edge over competitors and helping them become industry leaders. Adopting AI has become necessary to survive in this competitive world. Several offshore companies offer customized artificial intelligence solutions to assist SMEs in unlocking the power of data and increase returns. Using AI to Move IoT to the Next Level Let’s see the role of AI in IoT and how artificial intelligence can move the Internet of Things to the next level. Risk is a part of every business. Be it the safety of the workers, maintenance of heavy machinery, unexpected market conditions, or fraudulent transactions, IoT and AI help businesses understand and predict different risky scenarios in the enterprise. This makes them aware of what all could go wrong and how they can prevent it. Data is collected from sensors in the factory, devices worn by workers, transportation vehicles, computers, etc. This data is analyzed using AI to alert the management in time and prevent an accident from happening. Automation has changed the way work is done in enterprises. Instead of putting excess pressure on employees to complete the recurring tasks, the process is automated using artificial intelligence. As a result, employees have more time on their hands to take up complex tasks and be productive without getting stressed out. AI and IoT together assist employees in identifying tasks that can be automated and fine-tuned to increase efficiency. In addition, understanding patterns and processing data in real-time facilitates management to make faster decisions. Artificial intelligence can make IoT scalable in an enterprise of any size. A range of the device can be used as a part of the IoT network. Whether it is a mobile phone or a high-end computer, it serves the purpose when powered by AI. In fact, many organizations use low-end sensors for data collection to manage costs. AI reduces the large volume of data to a manageable level and allows you to add as many devices as necessary to the IoT network. AI and ML solutions use data from IoT to identify patterns and trends in customer behavior. This helps you in developing new products/ services that are useful for customers. It also helps in making changes to the existing products/ services to attract more sales. We can say that using AI, especially NLP (Natural Language Processing), to understand customer behavior will help manufacturers streamline their R&D. Rolls Royce, the British luxury automobile manufacturer, has been using IoT and AI to develop new aero engines. Broken and damaged machinery causes a lot of loss for manufacturers. The only way to prevent unexpected machine breakdowns is by implementing preventive maintenance. But how does the management keep track of so many machines and their maintenance? That’s where IoT has been

Isha
July 26, 2021
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5 Computer Vision Strategies To Implement For Your Business

As humans, we can easily see, process, and act on something that could be considered a visual input. But how can that be replicated in machines? That is precisely what computer vision aims to do. While there may be limitations for a machine to act like humans, they are quite close when it comes to analyzing and acting as programmed to do. To cut it short, Computer Vision can be described as a process when a computer using artificial intelligence is able to identify and process visuals (like photos and videos), extract insights from them to create an appropriate output that makes the process of decision making simpler. According to this market analysis report conducted in 2020, the global computer vision market size was at a value of USD 10.6 billion in 2019 and is expected to grow at a compound annual growth rate (CAGR) of 7.6% from 2020 to 2027. Before going into the computer vision techniques that you can implement for your business, let us understand the basics. Understanding Computer Vision Computer Vision is enabled with the capability to read, identify, classify and verify objects. The recent developments were facilitated by ML or Machine Learning technology, especially the process that requires iterative learning, and significant updates in computing power, data storage, and high-quality yet inexpensive input devices. Before we look at how firms are making use of this technology through in-house computer vision services or by outsourcing it to an expert computer vision consulting firm, we need to understand what goes into making this technology so different. Computer Vision Process 1. Capturing an image When a digital device like a camera or CCTV captures an image, it is basically creating a digital file consisting of zeros and ones in the computer’s language. 2. Processing the image Algorithms are used to determine basic geometric elements to create images out of the acquired binary data. 3. Analyzing and taking action The final component that makes the process of computer vision application successful is the analysis of the data. The system then acts according to the way it is programmed and notifies the administrator or manager. Computer Vision Basics What is computer vision used for? Is computer vision and machine learning the same? A part of computer vision applies machine learning, and they are both spin-offs from AI. However, computer vision involves tasks like image identification and classification, object detection and tracking which is way different from what ML does. Who is making use of Computer Vision? A number of industries have been using Computer Vision to enhance customer experience, reduce costs and increase security. Some of the major players are retail, manufacturing, surveillance, and weather forecast. Top Computer Vision Techniques Used by Businesses As humans, we tend to start using our vision as soon as we are born. The way we process visuals and process them to understand and act is highly difficult to replicate in machines. And while the field of computer vision has been successful in overcoming challenges so far, there is still a lot that needs to be worked on and optimized. Recent developments in neural networks are deep learning initiatives that have greatly advanced the way these visual recognition systems perform. And while it’s easy to learn about the basics of computer vision, implementing it in the right way irrespective of the industry a business belongs to, is difficult. In such a situation computer vision consulting services seem like the best go-to option to implement this technology. And the top features of the technology that these firms make use of in their decision-making strategies are as follows- Image classification The fact that a computer system can identify, analyze, and act almost like a human sounds great. But for that to happen, the visuals that the input device captures must be classified into a particular category to take action. There are a good number of challenges associated with image classification in computer vision like viewpoint variation, scale variation, intra-class variation, image deformation, image occlusions, illumination conditions, and background clutter. To overcome these challenges, computer vision researchers have derived a data-driven approach to solve this. Instead of trying to specify what one type of image category looks like directly in code, they provide the computer with examples of each image class. Learning algorithms are then developed for the computer to learn about the visual appearance of each class so they can go with the classification easily. Object Detection The task to define objects within images involves placing bounding boxes and labels for individual objects. This differs from image recognition and classification in a way that detection would put objects in a particular box after the image classification has been done. Say for example, if there are multiple cars in an image, all cars need to be detected and put in a bounding box. For classification though, there are just two ways to do it- object bounding or non-object bounding. Object detection is important as it helps the system to understand the image or video and prepare for analysis. The major difference between image recognition and object detection is that the latter has the ability to locate objects within an image or other input visual. This can be applied in a number of ways by businesses or retail stores that implement professional computer vision services for crowd managing, self-driven cars, anomaly detection, face detection, and video surveillance. Object Tracking Object tracking involves estimating the state of the target object present in the scene from the information collected. The process involves two levels. First known as Single Object Tracking where the appearance of the target is tracked. And second, MOT or Multiple Object Tracking where a detection step is necessary to identify the targets that can leave or enter the scene. A major challenge in tracking multiple targets originates from the various interactions between objects that can sometimes also have a similar appearance. In recent years, due to the exponential rise in the research of deep learning methods, there has

Kavika Roy
July 16, 2021
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7 Ways Machine Learning Can Help You Handle Business Travel Risks

Traveling to meet clients and stakeholders is a part of the process for several organizations. A good percentage of day-to-day traveling are businessmen and women flying from one city/country to another for meetings and conferences. The industry has changed tremendously over the years. Air travel has become a preferred choice of most enterprises as it saves time compared to other modes of transportation. Allowing people to check-in from their mobile phones instead of waiting for long hours at the airport is one major development. However, the rise of the COVID pandemic in 2020 has disrupted all industries, and the travel sector is no exception. Flights have been canceled; the borders between countries were closed. Even the borders between the states were closed to prevent people from traveling and spreading the virus. While this led to substantial losses, it further pushed the industry to come up with new ways and promote safe travel for people. Technology has become an inherent part of this change. Be it self-serving machines, contactless transactions, or replacing customer care staff with virtual assistants to contain the spread of the virus, artificial intelligence and machine learning or ML can help you beat business travel risks on a large scale. Safety First When Traveling Employee safety has become one of the primary concerns for airports and airlines. The staff is at high risk of getting infected because of the nature of their work. This directly impacts productivity. At the same time, the health of the passengers traveling on the same flights is also a matter of concern. AI solutions are helping to manage these risks and facilitating safe travel while also ensuring that the staff is not exposed to the virus. Facial recognition systems, predictive maintenance, and contactless luggage screening are a few ways in which AI and ML are being used in the aviation industry. Masks, sanitizers, and face shields are an inherent part of the process to stay safe when traveling. But to arrange for safe travel, it is the technology that comes to the rescue. The Need for Technology – What is Machine Learning? Many airlines and airports are investing in ML to get back on track and increase their productivity without risking the lives of the people involved. But what exactly is machine learning? Is there a need to hire the services of a Machine Learning consulting company to adopt new technology? It is a subset of artificial intelligence. It is a method of data analysis that teaches computers to understand data and make a decision with minimum human interaction. This algorithm allows the system to learn from its mistakes and improve its efficiency to produce more accurate results. However, the vital point is that the airlines can be productive even in risky situations like the pandemic by using AI and ML services. Coming to the second question, it is not compulsory to hire offshore AI consulting companies to adopt the technology. However, revamping the business processes is not easy unless there is the right kind of support. This support comes from experienced consulting companies that offer personalized services to various organizations. Ways ML Can Help You Beat Business Travel Risks There are several ways in which machine learning can increase the productivity of airlines. We will first see how ML can reduce business travel risk and then read about how ML can be used in other areas of the travel industry. 1. Customization of the Trips Travel companies and airlines are now offering customized plans based on your travel history. Based on your previous flying experiences and preferences, a personalized travel program will be charted by the machine learning algorithm. There are already apps that use smart calendars to create a travel plan for the sales personnel. X.ai already allows similar functions. The apps have up-to-date information and can plan your entire travel program more effectively as they also consider the risk quotient, the delays in flights, the difference in time zones, and more. Machine learning can reschedule your itineraries based on your travel requirements and factors that affect your program. This is highly advantageous for business travel as it saves time and money for the enterprises who pay the expenses on behalf of the employees. It also helps streamline the trip to make it successful. 2. Self-Service Check-ins for Contactless Transactions Check-ins with as little contact as possible is a great way to prevent the virus from spreading, isn’t it? AI services let you check-in in advance, not just in the airports but also in hotel rooms. Some hotels in countries like Singapore are offering contactless check-ins. You can check-in via the app and also request any extra services you might need. You’ll be provided with a QR code that needs to be scanned at the hotel so that you can check into your room straight away using the kiosk. There is no need to talk to the reception staff or enter your details anywhere. There isn’t any need to sign your entry and risk touching items that could spread the virus. Singapore’s Changi Airport Terminal 4 is using automation, while the CitizenM hotel group is offering self-service check-ins for guests. 3. Humanoid Robots and Robotics to Replace Humans The Bangalore International Airport in India is planning to use humanoid robots in place of human employees. A local startup has been working on building the humanoid to assist passengers during travel. Sirena Technologies has also built the first Indian Humanoid Robot, Nino that enables a novel and modern approach to education. Similarly, airlines are starting to replace robot-driven aircraft tractors to take passengers to their flights. By reducing the involvement of employees, airlines can continue to fly planes and stay productive even in adverse conditions. There is an idea to use automated kiosks to check the temperature of the travelers along with their travel details, all in one place. This will eliminate the need for employees to handle the passports and other papers of the travelers. 4. Screening Luggage Using Artificial Intelligence Luggage

Kavika Roy
July 13, 2021
1 Comment

How Call Centers Are Being Modified Using Artificial Intelligence?

It has been more than two decades since call centers revolutionized the customer support system for various industries. While some static call scripts and one-size-fits-all strategies may remain unchanged in business outsourcing, technology has played a crucial role in drastically altering the way call centers function. Today, call centers have the unique ability to leverage all available data to drive each customer interaction. These data include various sources like the marketing campaign the customer has viewed multiple times, the type of transaction completed recently, or what a prospective customer looked for in their latest search. Of many technologies that call centers may have leveraged, artificial intelligence is one. Artificial Intelligence in call centers has allowed explosive growth owing to widely available cloud services and machine learning tools. AI in call centers There are two major types of AI in BPOs right now. The first can evaluate vast volumes of data and provide just-in-time insights for the agents to improve their performance on a call. This AI delivers the correct information during a call and ensures the agent or executive is on track with the right information when the caller asks for it. The second type of AI in business outsourcing is conversational AI. It analyses the speech of both the executive and the caller to identify emotions and, ultimately, intent. Conversational AI is mainly used to forecast the impact of a conversation based on vocal ticks, emotional state, and overall engagement level of both the caller and the agent. The sentiment analysis provides valuable real-time feedback on the emotional state of both the customer and agent and intervenes whenever required. Why is AI used in call centers? Ultimately, businesses want to create a personalized, positive experience for customers. And we are all well aware that when it comes to providing a good experience, the credits are always measured in sales, whereas a bad experience can have lasting effects. So how does AI change any of it? Here’s the answer. Automation: AI automatically captures data, routes calls to the suitable agent based on the input and mood from the analyzed data, and creates a profile for future reference that can be used in the call center and other business areas. Analysis: Call center AI provides in-depth analysis of individual calls making it easier for the managers and quality control executives to make decisions. Each call is measured and compared against performance benchmarks to provide a clear picture with an actionable insight of where the agent is performing and lacking. Support: AI is directly integrated with call center service workstations for agents providing immediate insight into the data being captured, the probable outcome of the call, and much more. The result is faster response times, a higher call resolution rate, and happier and motivated call center agents who are now empowered with tools to help them perform better. How is the call center scenario changing with AI? It is no news that call centers strive to provide a seamless and easy experience to customers since they constantly have to face the risk of losing out to a competitor. This report from a survey conducted by American Express found out that prospective or existing customers have bailed from a current purchase because of a poor service experience. To ensure that does not happen, call centers have turned to AI and machine learning solutions to help them take the following best action, turn leads into customers, increase retention rates, propensity product purchase, and much more. Business Process Outsourcing or BPO firms tend to work a lot around data. That is why data-driven call centers look forward to implementing AI solutions to improve customer experience. Here are a few practices for data-driven call centers with AI and big data to enhance selling via a good customer experience. 1. Intelligent call routing One of the significant advantages AI brings to call center finances is saving on human resources costs. Call routing through AI helps get the right customer to the right representative, taking into account the reason for the call and lifetime value and call complexity. A good number of call centers also opt to use skills-based call routing techniques to respond to a promotion. Say, for example, if one team is striving for promotion A and the other for B, AI can quickly identify and analyze the call and route it to the appropriate team. Layering in AI to skills-based call routing also ensures that the customer arrives at the right agent who can guide them in the best possible way. 2. Better analysis for caller feedback Traditional call monitoring cannot pull data from multiple sources in real-time. For example, the performance of call center executives or agents is currently monitored according to human observation. Calls are listened to and analyzed by a small number of ‘human’ managers who may or may not have their own biases that could impact evaluations. The analysis can be inaccurate, directly moving the scoring of agents. If you have ever called a customer support center of an e-commerce firm or any business, you’d be well aware of how they inform you before the issue that the call may get recorded for quality purposes. This indicates that agents receive feedback and support only after the ring, and there is probably no automation or real-time assistance implemented to guide the agent. What gets even more annoying for the customer is that they may have to speak to several agents or keep dialing digits without receiving any fruitful outcome — the result- poor feedback for the agent. AI improves this situation by performing those essential functions like monitoring, analysis, and support in real-time. Much of this function is performed by its branch, machine learning. ML services help in analyzing the mood and response of both the customer and agent on call. When used as feedback, the data enables the agents to respond more effectively, making them both happier, as compared to previous experience. 3. Personality profiling While the main objective of