Artificial Intelligence in Transportation 

Transportation, the process of relocating people and goods from one location to another, has now undergone numerous studies, investigations, experiments, and refinements to reach the position where it stands now. Artificial Intelligence (AI) is the leading technology that backs the working of the transportation industry in many ways and has proven to be the technology that is capable of bringing tremendous revolutionary changes in the industry.

Apart from the fact that AI changes our life easier, it helps in making human transportation safer and more efficient through all modes. With AI being implemented, the industry has seen great progress at such a level that transportation is taking place without human intervention or assistance. AI has the capacity to enhance traffic efficiency, relieve traffic congestion, free up driver time, make convenient parking facilities, and boost ride-sharing features. The application of AI in transportation can also eliminate unnecessary fuel consumption caused by non-moving vehicles, as well as improve air quality by enabling traffic flow.  

Why AI in transportation? 

The benefits and uses offered by artificial intelligence services in the transportation industry will explain why it is important or what makes AI important in the industry. Here are some points that prove the importance of AI in transportation: 

Driverless cars preventing accidents  

It is already clear that AI aims to replace human intervention and thereby results in errors in the transportation industry. Self-driving cars that operate without human drivers are one of the best examples of AI use in the industry. Driverless vehicles are designed to follow traffic regulations with the help of sensors, prevent accidents by stopping or slowing down the vehicles whenever an object or person is detected, drive carefully while taking turns, etc. The auto-pilot car launched by Tesla shows the successful implementation of AI in the automobile and transportation industry. Companies like Toyota and Hyundai are also planning to introduce self-driving cars in the near future. 

Parking management  

Hassle-free parking is something every driver looks for and never gets most of the time. AI has put an end to the struggle with parking vehicles. Sensors have been installed to identify the parking space in vacant lots and they can calculate the space occupied by a vehicle parked in one place. Since sensors alone cannot scan license plates, it operates in coordination with cameras, parking meters, and computer vision. The cameras utilize computer vision to detect meterless spots. With the help of automatic number plate technology, they can locate parked vehicles and measure the time they are parked, thus assisting the computer vision to give live updates regarding all the available spaces. This technology also helps drivers to spot all the parking slots which are free using the map on their smartphones. 

Road condition monitoring  

Drivers in the United States spend almost $3,000,000,000 a year as a result of pothole damage, which is a primary concern of the citizens. AI-enables computer vision allows the transportation industry not only to detect damages but also to identify any change in asphalt and concrete to evaluate the surrounding infrastructure. These AI-driven algorithms can detect potholes and the intensity of road damage around the area, thereby helping relevant authorities to initiate actions for road maintenance. The algorithms function by taking the image data before moving to generate automatic crack detection and classification systems. 

Ensure safety  

Safety is a main concern in the transportation industry and it is not just limited to drivers or passengers. The safety of every road user including passengers, pedestrians, and drivers has always been paramount in the transportation industry. Implementation of AI technology is much more than reducing the number of accidental errors made by humans; transportation analytics helps reduce the impact of driving accidents, especially in congested urban areas while keeping an eye on compliance with safety regulations and reports on vehicle maintenance. 

Forecast and monitor traffic  

What would be the most difficult obstacle during transportation? The answer is traffic that causes delays, over-consumption of energy for no reason, wastage of fuel, accidents, etc. However, forecasting techniques help you to predict traffic conditions and automatically find alternative routes using information from traffic monitoring data that can spot events or any construction that causes traffic in the city. Traffic monitoring data provides a smooth and efficient transportation experience for the passengers and drivers, other than saving a lot of time and fuel. 

Efficient planning and schedule  

Issues of intermodal logistics have always been significant to businesses with a large fleet, intricate infrastructure, and multiple links in the cooperative network. Only cutting-edge technology can solve these issues and enhance performance: optimal route scheduling with minimal waiting time, real-time traffic detection to adjust route-setting, and timely controls. Combining AI data analytics with logistics gives an idea of routes, driver behavior, updated transportation planning process, reduced resource wastage, and enhanced security, based on the data. 

Reduced waiting time  

Nowadays the use of online taxis and rides has increased to a greater extent. Passengers are no longer patient enough to wait for hours to get a transportation service. You can simply book a taxi from your location and it will take you to any location you have entered. Transport service providers can use the route map on their phones to locate the customer and reach the location without any hassle. This automatically reduces the waiting time of customers. All these are made possible with AI algorithms and computer vision that helped the service providers to make data-driven decisions. 

Wrap Up 

Artificial Intelligence (AI) in transportation utilizes relevant cutting-edge technologies like machine learning and big data to ensure better safety and greater efficiency, thereby reducing the number of road accidents, and improving compliance with law and order in cities, towns, and as well as in smart cities. If AI can deal with important issues in the transportation industry like a large number of unwanted deaths, interruptions, and damaged roads, it can automatically improve safety and efficiency. Then addressing the second level of problems like human errors, carelessness, over speed, etc. will become easier. 

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Artificial Intelligence in Insurance 

The insurance industry relies primarily on solving the risks associated with a business or an individual. Therefore, insurance companies need a systematic and smooth way to deal with any risk in order to gain more revenue. The advent of Artificial Intelligence (AI) can transform the insurance industry from a ‘detection and repair’ to a ‘predictive and preventive model.  The implementation of Artificial Intelligence with the help of an AI development company in the insurance sector has helped many insurance players to reap a lot of benefits such as efficient claim management, less fraud, and more. Now let us take a close look into the uses or applications of artificial intelligence in the insurance sector. 

Applications of AI in Insurance 

Insurance claim management and investigation 

For insurance companies, claims management is one of the crucial operations. Insurance companies can make use of AI tools to make their claim evaluation and settlement process efficient. AI controls every process in the insurance industry such as claims generation, data capturing, approvals, payment tracking, etc.  It can also perform these tasks with less time and minimal effort. When AI is integrated with other applications like fraud detection, it enables companies to run an efficient, data-based, and automated end-to-end claims processing environment free from human errors. 

Fraud detection and prevention  

There is a loss of USD 40 billion each year due to insurance fraud alone in the United States. This figure gets multiplied when it comes to global scenarios. The traditional investigation method used to recognize and locate suspicious claims involves a great deal of voluntary effort and time. Claimants of suspicious activity need to be closely monitored and pursued. Due to the lack of time to complete the job, this approach is not welcomed in such a competitive industry as insurance. 

Many surveys have shown that a significant number of insurance providers are depending on technology to a greater extent in order to identify and prevent such fraudulent activities. AI can play a vital role in detecting patterns in past data, helping to detect fraud early and prevent it from occurring. As a result, insurance businesses can make a comprehensive risk evaluation before providing their services. 

Better loss estimation for low claims leakage  

The introduction of most modern technologies such as AI, deep learning, and image recognition systems have helped transform the insurance industry. With the help of machine learning capabilities, insurers can evaluate and predict the damage from a photo of the object, thus proving to be the best technology to be used in claim management. It helps to assess possible damage and recommends parts that have to be replaced. This makes the loss calculation process faster and more efficient. 

Claims Reporting  

Insurers can report, track, sort, and assign claims with or without human mediation, depending on the advanced AI systems they use. Chatbots in the insurance sector can effectually deal with the First Notice of Loss (FNOL) reporting process when combined with Natural Language Processing (NLP) and Automatic Speech Recognition technology (Conversational AI). This makes the process simple and boosts productivity. Moreover, there are several benefits of leveraging conversational AI in insurance companies. Automated chatbots can streamline the claim reporting process efficiently and can be used by customers to report incidents from anywhere, anytime. The AI-powered chatbot can then spread the information to the person involved for further processing. 

Benefits of using AI in insurance 

So far, we have covered the application of AI in the insurance industry. Now let us talk about the benefits AI offers to the insurance industry. 

Usage-based insurance  

AI played an important role in bringing up the idea of behavioral policy pricing. The conventional method of reaching insurance premiums on the basis of statistical records has changed with the introduction of AI and resulted in using analytical and personal data to determine the exact premium.  

Better customer experience  

Consumers are not looking for technical jargon, they need simple information that adds value. AI-driven chatbots are useful in providing simple solutions to the intricate questions of a customer. Chatbots have come a long way from giving automated answers to frequently asked questions to recognizing customers’ faces and giving them personalized policies. AI-driven chatbots can improve customer service and minimize the costs of delivering different services to various customers. Companies are utilizing AI to give customized experiences not just to the customers but also to the business clients in order to create more consistent engagement with clients for higher sales, and greater loyalty, and also to evaluate more data from different sources to make better predictions. 

Efficient underwriting  

AI tops in performing data analysis than human analysis especially when it comes to huge amounts of data. AI applications facilitate the underwriter to give more focus on strategy and portfolio management rather than manually checking data. AI’s algorithms are capable of performing monotonous underwriting tasks efficiently.  

Building trust  

Trust and loyalty are the two most relevant factors that play a significant role during an insurance process. Essentially, policyholders want a faster claim process. Two important criteria by which a customer rates an insurance company are the time it takes to settle a claim, and an effortless claim settlement is the important one. Once the claim is settled, the customer will assess the performance of an insurance company.  

Faster claims settlement 

AI helps in automating labor-intensive tasks related to inspections because it has especially proved its worth in times of pandemic through remote inspection. If a vehicle meets with an accident, the insurance provider can assess damages remotely through image sensing. It helps save time and costs during the entire assessment process. 

Wrap Up 

Artificial Intelligence has grown enough to shake the insurance industry from top to bottom. Customers can have a smooth and easygoing experience at affordable rates. The possibilities AI offers are limitless – insurance will become more personalized as insurers will be able to understand the needs of customers using AI technology. AI will also pave the way to gain new revenue in the future while helping insurers to save costs by accelerating workflows. 

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Examples of Artificial Intelligence in Everyday Life

The applications of artificial intelligence (AI) are numerous because this area is continuously evolving day by day. Artificial intelligence refers to technology that educates machines to think, analyze, and learn from data in order to make intelligent decisions. It dominates the world of technology with the promise of bringing many impossibilities more realistic every day.  

Although the engineering and medical fields rely on AI to a greater extent, it has been used widely even in our day-to-day activities directly or indirectly. Here we are going to discuss several examples of artificial intelligence which are a part of our daily lives today. Let’s dive more into some of the best examples of artificial intelligence in everyday life. 

Examples of AI in everyday life 

Face recognition  

Most people use the face recognition feature in their Android or iPhone as it is one of the best safety features available on your device. Moreover, it is easy to use and does not ask for patterns or passcodes when you are in a hurry. This easy and smart feature is the result of artificial intelligence. Humans recognize their co-existing species with their face and voice biometrics, similarly, the AI facial recognition feature uses different types of biometrics to identify a person. This machine learning (ML) technology allows AI to study and save the facial coordinates of a human face from recognizing the user.  

Smart cars  

With the increase in the number of accident cases, many might have dreamt about cars that save drivers and passengers from getting killed. A car that drives on its own stops at traffic signals, takes turns and difficult roads carefully – all that we thought of as a dream has come to reality because of AI. Tesla has already laid its hands on self-driving cars and there are almost 500,000 – tesla automatic cars running in the United States, which is supposed to become double by the coming year. The machine learning capabilities of AI have made the idea of fully automated cars possible. These automated cars are programmed to stop at signals, slow down or stop whenever an obstacle is detected, control the speed limit, etc. 

Digital assistants  

Who wouldn’t want to have an assistant do certain tasks or activities that can be controlled using a fingertip? Digital assistants serve the purpose for you. Sometimes you may want to hear soothing music but you may not have the energy to find it and play from your playlist, then there are situations in which you don’t want to get up and turn the fan or air conditioner off or on. This is exactly what digital assistants can do. They will do all such things for you just with a command. You just need to tell them to switch on the fan or play music or turn off the light and they will simply follow your instructions. Amazon’s Alexa and Apple’s Siri are the two best examples of this far-fetched technology. They not only understand our commands but also respond to our queries or questions, make appointments, manage calls, send emails, set alarms, etc. They keep analyzing and learning from interactions in order to deliver improved customer experiences based on our preferences and search history. 

Entertainment and social apps  

Social media platforms are using AI to give a customized experience to users. One of the best examples is the friend suggestions we receive on Facebook, Instagram, Twitter, and other platforms. These apps show a list of people we might know or those who are on our contact list. How do apps get such information? How do they know that we know the people in the suggestions? Well, that is the magic of artificial intelligence. AI and ML evaluate your usage patterns and recognize your friends from photos and news feeds. Similarly, entertainment platforms like Netflix and Prime Video give many suggestions on what to watch next. This is done using an efficient content rating system that provides hundreds of metrics learning from the user’s previous monitoring history, this way the basic algorithms work well on the recommendation system. 


The banking sector has fully digitized with artificial intelligence. Today, we don’t have to stand in a long queue to withdraw money or transfer it to another account. Customers can initiate transactions without being present at the physical location as AI chatbots are automated to perform all the processes in digital banking. Banks seek the help of artificial intelligence chatbots to offer improved customer experience by enabling seamless and 24/7 customer interaction. From opening an account to making e-payments, the banking sector has become more convenient to users with the use of AI chatbots.  Banks implement different types of chatbots to assist with the middle-office functions in identifying and preventing fraud, improving the anti-money laundering (AML) process, and ensuring the Know Your Customer (KYC) regulatory checks process. This eliminates risk and facilitates a smooth banking experience for customers as well as for institutions. 

Google predictive search algorithm  

You might have noticed several suggestions automatically appearing on the screen before you finish typing what you are actually looking for. This is because of the autocomplete feature of google that predicts the search term by the characters you enter one by one. Google uses artificial intelligence, machine learning, and deep learning to run prediction algorithms in order to make the searches effective.  


Why do people nowadays shift to online sites or apps to make purchases? This is because we can filter out what exactly we want to purchase using certain keywords and filters. For example, if you are looking for a white shoe, the site will show a collection of white shoes of different ranges. You can still filter using a price or pattern you prefer. It takes seconds or minutes only to get a choice of your interest. But how is that possible? This is made possible by the basic AI algorithms that routinely classify product searches for efficient cataloging. 

Wrap Up 

Artificial intelligence services are found to imitate human actions and logic, but that is not the end of it. Today AI transcends human understanding and reason and proves that it is profitable for everyone. The efficiency of machines when integrated with AI is more precise and efficient. They complete the tasks assigned in minutes, which can take days if a man does. This is why the use of AI has been not limited to any particular area, instead, it is made accessible to every human. From personalized digital assistants to self-driving automated cars, AI has covered a long journey along with mankind. 

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Artificial Intelligence in Banking 

Industries are going behind new methods to adapt to changing preferences of the customer and to cope with the pace of emerging needs and demands. The banking sector is going through digitalization at a much faster pace. The flow of the operational process is clearly integrated with Artificial Intelligence, Machine learning, and Robotic Process Automation solutions. The end result of incorporating these technologies brings significant gains because AI is capable of improving processes virtually in all areas of banking. From back-end automation to front-end operations, AI offers significant benefits to the banks that implement it. AI includes a range of technologies, from fully machine-powered or autonomous intelligence to aided or enhanced intelligence. Some relatively simple AI tools such as Intelligent Process Automation (IPA) have already been implemented in banks.  

Uses of Artificial Intelligence in Banking 

Here are some of the major use cases or applications of artificial intelligence in the banking sector. 

Fraud detection and cyber security  

Fraud detection is one of the most relevant functions of artificial intelligence. Every day, a large number of digital transactions take place, with customers paying bills, withdrawing money, and depositing checks, and there is another bundle of transactions happening through apps or websites. Therefore, there is a growing need for the banking sector to increase its cyber security and efficiency to detect fraud. Since banking facilities have become more accessible with advancements in technology, it has also increased the chance of fraudulent activities occurring in these services.  

The machine learning capabilities of AI enable easy detection of fraud more accurately than any expert professional, thus offering security in online banking services. AI can track any loophole or unusual activity in the system and notify customers and banks to eliminate risk or damage associated with it. The monitoring capabilities of Artificial Intelligence also enable banks to deal with cyber-attacks in the banking sector before it reaches the internal system, employees, or customers. 


Maintaining an effective customer relationship is very important for many businesses and the banking sector is not an exclusion. It will not be easy to deal with customers, answering every single query of them, especially when there are more important tasks to be done. But with a chatbot, all these things can be managed easily as it significantly eliminates the need to maintain and operate call centers. Chatbots, once deployed, will be able to work 24 * 7 as they will not get tired or take recess as human employees do. These bots easily understand the needs of a user efficiently and provide improved customer support. Chatbots learn from customer interactions and train themselves to improve their efficiency. Integrating chatbots with banking services ensures that customers can reach out to the bank’s support at any time of the day. Moreover, chatbots deliver personalized support to each customer after analyzing their behavior and suggesting the best financial services according to their requirements. 

Track marketing trends  

A large volume of data is always a part of banking institutions and it is artificial intelligence that helps banks to process such a huge volume of data AI also makes predictions regarding emerging trends, currencies, and stocks. The AI-based machine learning technology in banking helps to assess market sentiment, recommend options to make investments, and suggest the best time for investing in stocks. and warns if it identifies any potential risk. Because of the great data processing capacity of AI technology, it helps to accelerate decision-making and facilitate smooth business for banks and their clients. 

Loan and credit support  

Banks have begun to integrate Artificial Intelligence solutions to make loan and credit decisions more secure, informed, and profitable. Many banks still take account of credit history, credit score, and references in determining the creditworthiness of a person or an organization. However, sometimes these credit reporting systems are delivering inaccurate and riddled data with errors. There are chances for loss of original transaction history and misrepresentation of creditors. AI-based loan and credit systems decide the creditworthiness of customers by analyzing the behavior and pattern of their credit history. In addition, the system sends alerts to banks about certain customer behaviors that show the likelihood of default. 

Risk management  

The banking sector is often affected by many external global factors like natural disasters, political disparities between countries, fluctuations in exchange rates of currencies, etc. Making crucial business decisions requires extra cautiousness, especially during such complex and unpredictable situations. AI-based analytics will help you to get a clear idea about things that are yet to come, to remain prepared, and to make wise decisions. AI identifies risky applications by assessing the likeliness of a client failing to repay a loan. This kind of prediction is made by analyzing previous behavior patterns and data in the client’s smartphone.  

Regulatory compliance  

If we list the most regulated sectors of the world economy, the banking sector would be holding the top place. Governments have introduced a certain kind of regulatory control over banking institutions to make sure that customers do not take advantage of banks to commit financial crimes. Such compliances also ensure that banks maintain acceptable risk profiles to rule out large-scale defaults. Most banks have comprised an internal compliance team that handles such issues, but these processes are more time-consuming and need large investments when it is performed manually.  

The compliance regulations on banking institutions are subject to change from time to time, and banks are required to comply with these regulations and update their processes and workflows regularly. It is true that AI Banking cannot take up the role of a compliance analyst but it will assist them in making operations faster and more efficient.  

Wrap Up 

Every business is going through a digital revolution that is affecting the functionalities of many sectors in different ways. Just like any other sector that focuses on embracing the revolution to gain profits, banking is also in a position. the uses or applications we have discussed here give a clear picture of what artificial intelligence has in store for banking. Though it mainly aims at improving customer relationships and automating complex processes, AI never fails to attend to the challenges faced by banking institutions due to digitalization. 

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Artificial Intelligence in Finance 

Artificial Intelligence (AI) in finance indicates the use of financial software developed by an artificial intelligence company using machine learning algorithms that enable financial institutions to automate day-to-day operations. New approaches have been taken up to optimize AI business operations in the financial sector and avoid security issues, thereby increasing the confidence of customers in financial institutions. The application of artificial intelligence in the financial sector has provided customers with a safe and smooth way to save, spend, and handle their money.  

Financial professionals and clients are more likely to have frequent AI encounters, as most common service-related problems are managed or solved to some extent through automation powered by artificial intelligence solutions. The chances for an acceleration in this trend are high as it has to meet the growing customer demand to offer a quick, convenient, and safer financial experience. 

Applications of AI in Finance 

There are many ways through which artificial intelligence is leveraged with finance or financial institutions. Let us talk about some of the challenges addressed by AI when implemented properly in the financial sector. Below are some of the main use cases or major challenges addressed by artificial intelligence in the financial sector.  

Risk management  

Emphasis is placed on appropriate risk assessment in the financial sector as risks can be devastating to any financial institution. Prompt and accurate predictions related to possible threats are the crucial factor responsible for maintaining the safety of financial institutions. Human employees can make mistakes as they may not pay attention to potential risks that might cost the business a lot. In such cases, the chances of a machine learning model going wrong are very low as they are constantly learning from a large dataset to make precise decisions.  

In addition to helping with decision-making, these models aid in automating the whole process, thus saving time. Considering anomalous data while evaluating and managing risks is very challenging for human employees. AI’s ability to navigate through huge volumes of unstructured and structured data makes it possible to make precise and prompt predictions about potential risks. It enables financial institutions to actively work in order to eliminate the possible losses that come with the risks. 

Fraud Prevention  

With the world transforming from liquid cash to digital money for almost every activity ranging from shopping to bill payments using a smartphone, financial institutions have become more vulnerable to fraudulent activities. The digitization of such financial activities has resulted in the emergence of cybercrimes including security attacks. These institutions have specialized departments for detecting and preventing fraud and they also invest huge resources in such departments. Any negligence cannot be welcomed there when all activities are closely monitored to ensure transparency. 

Artificial intelligence plays a crucial role in detecting strange behaviors, thereby ensuring the safety of transactions conducted online. This is because the AI finance system analyzes the scale of previous transactions and alerts the system in case of any unusual transactions. For instance, when a user uses a card from one geographical location and a strange amount is withdrawn using the same card from another location within a few minutes, it is identified as unusual activity. Such suspicious transactions are noted because of the previous history of the user that reveals he/she can’t reach a geographical location in a matter of minutes. As AI finance systems are constantly learning from each activity of the user it is possible for them to recognize an activity even if a normal and authentic transaction is taking place. 

Digitizing paper records  

There is a huge volume of historical data in financial institutions, especially in the form of paper documents. Since the records are prone to damage or losses and are chaotic in nature, they have been stored in drawers, shelves, cabinets, or in other storage spaces. Manual digitization of such paper records is a challenging task, especially if the organization has been in business for a long period of time. The combination of all these components requires a step to digitize all paper documents for ease of use. 

 AI always relies on historical or previous data during training in order to make effective decisions. Artificial intelligence also helps in digitizing these paper documents rather than entering the data manually, thus enabling the employees to scan and upload the document to the software. These AI-driven software packages can obtain every piece of information from the document as they are trained to identify the characters in the document. OCR (Optical Character Recognition) and ICR (Intelligent Character Recognition) are 2 such A-driven technologies. 

Automated Financial advisory  

As the financial industry is leveraged with Artificial Intelligence, we are witnessing the proliferation of Robo-advisors. These Robo-advisors use AI and machine learning algorithms to deliver financial advice to clients with least or no human intervention. The robot advisor uses an online survey to collect data in order to analyze the financial status and future goals of the clients. This data is later used to provide financial advice to clients. In addition, this Robo-advisor helps clients save their time and money as it delivers precise and data-driven advice. It also allows the integration of human intelligence with AI to provide the best financial advice with future insights. 

Insurance claim automation  

AI chatbots and AI-driven software facilitate the automation of the insurance claim process and minimize the risk associated with overpayment. There is an internal mechanism within the insurance companies to process these claims, but this may take a long time as there are many people claiming their insurance. In addition to this sluggish insurance claim process, too many claims can make it difficult for insurance companies to identify important patterns in claim data. 

AI-induced systems can easily manage the claim process by minimizing the amount of time it takes to identify patterns and process each claim. Many organizations have started developing software and chatbots for insurance companies that utilize the features of AI and predictive analysis to minimize the occurrence of overpayments. Moreover, AI-driven software can identify inaccuracies in an insurance company’s payment behavior based on previous data. 

Wrap Up 

Modern finance has started implementing Artificial Intelligence for diverse uses. Though the influence of AI has universally affected many sectors and industries, the financial industry is considered to be one of the earliest sectors to embrace this amazing technology. It is the best example of one of the most benefitted or profited sectors because of AI. Therefore, many large financial institutions are investing a good share of their resources in AI due to the undeniable benefits it offers. Since the financial industry is one of the core industries that many businesses rely on, this prompts many organizations and industries to take up artificial intelligence for their benefit.  

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Use of Artificial Intelligence in Marketing 

Artificial intelligence in marketing refers to the use of consumer data from online as well as offline sources to predict different consumer behaviors while using applications or websites. This helps businesses to distribute specific content to their target audience through the proper channel at the right time. Digital marketers can use big data statistics to improve the performance and ROI of their ventures while not putting too much effort on their part. Though this description gives an overview of Artificial Intelligence development in marketing, there is a lot more to explain. The application of AI also helps to make campaigns more effective and to achieve their goals by analyzing prevailing data from previous campaigns to give recommendations for tuning campaigns depending on different characteristics. 

Benefits of AI in marketing 

Given the benefits of AI in marketing, many brands have embraced it as a way to not only improve the shopping experience for consumers but also enhance their brand recall. Let us consider the above scenario and explain what the marketing department has been able to reap from using AI.  

Predicting consumer behavior  

Predicting customer behavior would be a powerful solution for many businesses as no business is likely to survive without customers. Knowing how the products have been used by your customers is very important in measuring the success of your business, but it would be better if you could figure out where things are going based on the little trends, we might not be able to identify from the ground. Here is where artificial intelligence comes up with an aid to show how our customers actually behave. AI helps businesses to push away the boundaries of possibilities in online retail. On the other hand, AI analyzes and predicts the leaving behavior of customers from a specific market or business, thus helping companies to focus on the causes of deviation and find a solution at the beginning itself. 

Improved customer experience  

With AI-driven chatbots and virtual trials, AI technology has taken the shopping experience of customers to a greater level. Virtual trials permit customers to check the product they want to purchase and to have an idea about what or how they look like. This is an opportunity for the customers to get detailed insights about a product, thereby eliminating the need for an exchange or return and providing satisfaction with the product. Meanwhile, chatbots help bring customers close to the brand by interacting with them and giving responses to their questions and queries. The more you satisfy your customers by engaging them in your business, the more they invest their interest in your brand. 

Minimized load times  

There must have been a situation at least once in your life where you couldn’t buy a product you were looking for, just because the website took a long time to open up or respond. Customers usually don’t have the patience to purchase a product from a site that takes a long time to load. This definitely affects the sales of products on such websites. AI-based algorithms can easily determine the chances for a user to click on a particular link or product and decrease the loading time of the page in advance. This way the user is driven faster to the product they are looking for, which in turn increases sales as well.  Hence an in-depth understanding of the customer’s behavior and loading their preferred content leads to a significant reduction in load time. 

Content creation  

Companies are beginning to extract benefits from AI as it helps them develop content for the promotion of their brands. Artificial Intelligence was mainly employed in businesses to create reports about the performance and marketing data of organizations. But today, artificial intelligence can create blog posts for companies that increase traffic to their site using SEO strategies to boot.  

Track marketing trends  

Market trends keep changing within a regular period of time, mainly because of the changing customer needs and demands. It is not that difficult to track the trend but coping with the trend and analyzing the reaction of the audience to a trend is quite difficult. Here, Artificial Intelligence allows you not only to track the market trends but also to understand the reaction of the audience to the brand. Hence, you can use those insights in your marketing strategy and increase the reach of the product. Remaining at the top of the market trend will benefit you with a group that wants to purchase your product in line with the trend. 

Ad and search engine optimization  

When AI is incorporated into your marketing plan, it not only helps make relevant corrections but also results in delivering good-quality content for your brand promotion. This is applicable to your ads and SEO. AI has proven the ability to analyze and finalize appropriate keywords for your brand which ensures that you are included in the recommendations of a broad audience than just a comparatively small target group. Taking into account the benefits of AI and the extent to which it is evolving, we know that it will survive here. At the same time, the way we use AI in our marketing plans is to maximize results with an increase in possibilities. 

Wrap Up 

In the initial days, the possibilities of artificial intelligence were confined due to inadequate computing powers. But today with the growth of technology, AI has become the next ‘ray of hope’ in the world of technology. In this digital world, everything is driven by data, especially with the support of AI and machine learning. Markets are no longer defined by boundaries, but by the perspective of a company. The world itself has become a market with a lot of possibilities and competition. Artificial intelligence can be just a new addition to the current modern world but it will continue to exist and evolve over time with the increased adoption of technology in different areas.  

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7 Real-World Applications of Deep Learning

What Is Deep Learning?

Deep learning belongs to the extensive family of machine learning methods that enable computer systems to display the same behavior as humans during specific situations, thus promoting complete automation by eliminating human intervention in many activities.  

Deep learning allows computer models to understand and do categorization tasks directly from pictures, text, or sound. Deep learning models can exceed the performance of humans and can acquire cutting-edge accuracy in many operations. Being a booming industry with numerous possibilities, deep learning continues to power up businesses as well as client satisfaction. Many technological innovations like self-driving cars, virtual assistants (Alexa, Siri), etc. wouldn’t be here today without deep learning.  

Deep Learning Applications 

Deep learning is a machine learning technique that can be applied in many areas to create huge changes in the field of technology. Here, let’s take a look at some of the major deep-learning applications:  

Self–Driving Cars  

Deep learning is a technique that breathes life into self-driving cars. Millions of data sets are integrated into a system to develop a model, upskill a machine, and then check the outcome in a safe environment. Usually, it follows a routine cycle of testing and application of deep learning algorithms and exposure to billions of different situations in order to ensure safe driving. Self-driven cars make use of the data obtained from cameras, sensors, and geo-mapping to develop concise and complex models for navigating traffic and identifying lanes, signages, pedestrian-only paths, live components such as roadblocks, and volume of traffic. 

Fraud Detection  

In this era where everything is digitized, financial and banking institutions have no other ways but to go with the flow. Since we have begun to witness a digital transformation in the banking and financial sectors, cyber threats to the banking sector have become a concern, which in turn demands the necessity of fraud detection. Deep learning comes to help this area in identifying transaction and credit score patterns. Deep learning uses fraud detection algorithms and throws light into areas where unusual behavior happens, thus detecting fraud and preventing it. Additionally, deep learning can be used in news aggregation to strengthen efforts to deliver customized news to readers. With the internet being the source of all types of news, whether it is fake or real, it is very difficult to detect fake news as it gets copied across the channels automatically, by the bots. Deep Learning enables you to create classifiers that identify fake or biased news, delete such news from your wall, and notify you about any potential privacy breach.  

Virtual Assistants  

With the help of deep learning technology, virtual assistants and chatbots grasp more knowledge about different topics like the preferences of the user, their favorite songs, places visited, etc. Virtual assistants and chatbots are automated to comprehend the commands given by the user and analyze natural human language. Virtual assistants are also capable of translating your conversation into text, booking appointments, making notes, etc. Amazon’s Alexa, Apple’s Siri, and Google Assistant are some of the most common examples of deep learning applications in virtual assistants. Whenever a user interacts with the assistant, it facilitates them the chance to comprehend your voice and pronunciation, thereby offering a secondary-level human interaction experience. 


Deep learning in healthcare not only reduces costs but also helps in eliminating health risks. The application of deep learning in medicine has been proven to be valid in clinical research. Deep learning can be a helpful diagnostic companion for rehabilitation and gives alerts whenever there is a high-risk situation like respiratory failure or sepsis.  Precise and rapid diagnosis of critical diseases augmented clinicians attending patients in the absence of quality health care professionals, and the standardization of pathology results and course of treatments as well as predicting future health risks using genetics are a few of the deep learning projects moving faster in the healthcare domain. 


Deep learning is the reason why most over-the-top (OTT) platforms are able to deliver customized content to the audience according to their preferences. It can even suggest particular shows or movies to a specific user, based on their previous watch history. Even VEVO has employed these deep learning techniques to build state-of-the-art data services not merely for offering customized experiences to its users but for companies, artists, and other business groups to create statistic-based performance and fame. Content editing, transcriptions, audio-video syncing, tagging, etc. are some other major applications of deep learning in the entertainment sector. 

Visual Recognition  

Deep Learning enables sorting or categorizing images by identifying locations,  faces of people (even in a group picture), dates on which the photo is taken, occasions, etc. Searching and identifying a photo from a large dataset requires ultra-modern technology with visual recognition systems including multiple layers from standardized to advanced, to detect components.  

Natural Language Processing  

Teaching machines the entire structure of a language ranging from semantics to pronunciation is not a simple task. Even humans take years to be a thorough expert in a language they use every day. Natural language processing is the practice of training machines to learn human language and give proper responses in every situation, with the help of deep learning. Responding to questions, sorting texts, emotion analysis, and language modeling are all subdivisions of natural language processing that accelerate the use of deep learning. 

Wrap Up 

Artificial intelligence development can help businesses reap tons of benefits when Deep Learning and AI are implemented in the right way. For those who are familiar with or proficient in deep learning, the opportunities, and possibilities they have are endless. Only those who are aware of the capabilities and impact of machine learning can understand how the application of deep learning works in different industrial sectors across the globe. Deep Learning continues to explore new unsolved areas, offering a solution to human problems all around the world. It is always a safer and better option even for governments to achieve success not just in administration but also in the military and other physical forces. Deep learning is not just limited to the above-explained areas it has a border area of application.  

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Artificial Intelligence in E-commerce

We are already aware of Artificial Intelligence (AI) as a cutting-edge technology, in which machines imitate human intelligence artificially. Things are becoming more interesting with artificial intelligence being implemented in different business sectors. It is not surprising that the e-commerce industry is using artificial intelligence not just for increasing sales but also to provide a better customer experience. Even e-commerce giants like Amazon have also adopted artificial intelligence applications in product recommendations and this resulted in an increase in Amazon’s overall revenue by 35 percent. Hereby, it is proved that the application of AI in the e-commerce industry has contributed to many benefits.  

AI applications in E-commerce 

The E-commerce industry is facing a great transformation with the use of artificial intelligence in online shopping, as it predicts the shopping pattern based on the customer’s purchase. Here are some major applications of artificial intelligence in e-commerce. 

Chatbots and virtual assistants 

Today there are no time restrictions or limitations in shopping as whatever we need can be ordered online and will be delivered to our doorsteps. We don’t have to wait in a long queue to get customer support. E-commerce platforms are providing 24/7 customer support and a hustle-free shopping experience with the help of digital assistants or chatbots. Since these support assistants are built using AI technologies, they are artificially trained to assist customers in the same way human agents do. Chatbots improve customer service. Other than customer support, they also offer other benefits, like: 

  • Comprehend voice-based commands from customers using Natural Language Processing (NLP). 
  • Address customer requirements through in-depth insights. 
  • Improves functionality through self-learning. 
  • Give customized or selected offers to consumers. 

Product recommendations  

One of the notable applications of AI in the e-commerce industry is personalized product recommendations to consumers. AI gives customized product recommendations to customers based on their previous search history, purchases, most browsed products, and browsing habits. As a result, the conversion rate has spiked up to 91.5% and the average order value to 3%. The benefits of product recommendations include: 

  • Increase in the number of customer returns. 
  • Increase in sales and customer retention. 
  • The customer gets a personalized shopping experience. 

Inventory management  

Effective inventory management involves the exact level of inventory maintenance that can meet market conditions without moving to unused stock. Unlike the conventional form of inventory management which was restricted to the prevailing stock levels, AI-induced inventory management enables you to keep track of data based on: 

  • Previous year’s sales trends. 
  • Expected or projected changes in the demand for products. 
  • Issues related to supply may affect the inventory level. 

Other than inventory management, AI also facilitates warehouse management where automated robots are being built to improve the future of the e-commerce industry. 

AI personalization  

When it comes to e-commerce marketing, personalization plays a major role and is rated as one of the most effective tools. Based on specific data collected from each user, Artificial Intelligence and Machine Learning in e-commerce gain important user statistics using this customer data. Boomtrain is an example of such an AI-enabled tool that is capable of analyzing customer data from different platforms like email campaigns or websites or mobile applications. These statistics help e-commerce retailers provide relevant product recommendations and a stable or uniform user experience throughout every device. 

Benefits of AI application in e-commerce 

As we have discussed the applications, here are some important benefits that accompany AI applications in e-commerce.  


With the help of AI technology and algorithms, robots allow retailers to deliver to the customers whatever they want whenever they want something. With the expansion of companies and businesses all over the world, the e-commerce industry continues to grow with a large number of customers. This growth automatically increases the number of repetitive job launches of new products in multiple channels, providing discount offers to customers, etc. which in turn demands automation to be taken as a top priority feature that needs more investment in e-commerce stores.  

Retarget potential customers  

As per the reports of Conversica, the sales team fails to follow a minimum of 33% of marketing leads. This indicates that many of the potential customers interested in a product are left behind and due to excess unmanageable data, most teams are unable to perform relevant actions. This is where AI becomes a game-changer. Artificial intelligence helps in developing an impactive sales message that grabs the attention of your customers at the right time and designing solutions for your problems, thus increasing the sales cycle.   

Filter fake reviews  

Customers always go through the reviews before making a purchase. This is a common customer behavior through which customers try to analyze the value of the product they are planning to buy. Fake reviews are always a headache for online as well as e-commerce shops. It is found that negative reviews have influenced almost 86% of customers’ decisions. The possibilities of fake reviews put by competitors, bots, and paid review writers are very high. All of these affect customer purchases. Artificial intelligence is used by many e-commerce businesses to defend their businesses with more focus on verified reviews. One of the best examples is Amazon using artificial intelligence to counterfeit fake reviews and increase their 5-star ratings. They focus on important reviews of purchases made by verified customers and promote them.  

After-sales service  

Selling products and increasing sales alone cannot help your business grow. You need to extend your service even after the sale. This includes giving assistance in returning a product, replacements, and installations, answering customer queries, checking customer satisfaction, etc. All these procedures can be easily managed by artificial intelligence. AI identifies whenever the delivery of a product, sends feedback forms to customers, notifies them to fill in the form, and asks them to rate the purchase and review it to offer better services in the future. 

Wrap Up 

Artificial Intelligence has proven its capability in many other areas before, but implementing AI technology in retail or e-commerce can bring out the best outcomes for growth in the overall business revenue as well. As per the reports revealed by a Forbes article, more than half of marketers are planning to adopt AI technology in their business within a few years. 

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Reasons For Robotic Process Automation Failure And How To Avoid Them

Robotic Process Automation (RPA), as a software tool, has brought enormous changes in the field of technology. RPA took the regime of technology to new heights with its capabilities to automate repetitive and monotonous tasks not just in the IT sector but also in various other business sectors including banking, accounts payable, education, health care, Insurance, telecommunications, logistics, real estate human resources, BPO and the list goes on. Completion of tiring and repetitive tasks using manual resources or human employees took a lot of time, effort, and money. RPA along with other sophisticated technologies like Machine Learning (ML), and Artificial Intelligence (AI) has put an end to such manual labor.   

So, if such an innovative technology like RPA fails, can you imagine what would be the reason that might have resulted in RPA failure? Let us discuss the reasons for Robotic Process Automation Failure.   

Robotic Process Automation Failure: Reasons   

If Robotic process automation projects fail, it’s because most people who try to automate their tasks using RPA do not have a proper idea about their work processes. RPA becomes ineffective without a properly defined work process and this is a commonly found challenge while implementing robotic process automation. Mainly there are 2 situations for the failure of RPA  

  • Either the automated process is not fully robotic as identified or,  
  • Automation works in a way that is more dynamic than previously defined.  

Before implementing RPA in your business, think of the process that needs to be automated and if it needs to be more dynamic or not. It is important to consider these factors to avoid facing RPA challenges later in the future. Here are some elaborate reasons for RPA failure:  

The RPA process is more dynamic than expected  

If the activity is more dynamic, it should always be checked to see if it is operating in a changing environment and producing precise results. In case of such problems, an organization is expected to seek assistance from an RPA implementation strategy and consulting service, which can fix the problem. If each case requires decision-making on a separate basis, you should include humans and reduce the RPA failure rate.  

Lack of clarity in the use of RPA strategy  

If any RPA project demands to increase the efficiency of its process without defining how RPA should be used, the risk of it becoming an isolated business activity is very much higher. Therefore, it results in the failure of RPA implementation in the future. Creating a clear picture of the use of RPA software will be helpful as it meets the requirements and expectations of many business functions.  

Unrealistic ideas about automation  

Technology itself is a work in progress and Robotic Process Automation is one among the many technological innovations that are striving to achieve the desired results. But people fail to realize this and expect their software bots to fix all existing process issues. The truth is that most of the time, all these unrealistic ideas are the major reason behind the failure of RPA. For example, RPA is used in telecommunications but as of now, no software bot can pick up a call and talk to people like human employees do. What bots can do is record the conversations via calls and chat, hand it over to customer care executives when they can’t solve it, and manage post-call processes. Successful implementation of RPA starts with understanding its capabilities and way of functioning.   

Use of the wrong RPA solution for a work process  

Initially, most software robots worked in the back office unnoticed. They were working according to the pre-determined rules, doing routine jobs behind the scenes with zero supervision. RPA software was mainly built for this purpose and was compatible with most projects of the time. Things changed when robotic process automation was brought to the front office and started collaborating with new RPA vendors and solutions. RPA works best in areas where activities are monotonous such as in the retail or packaging industries where human interference or judgments are not required. RPA is not the perfect fit for processes that need human intervention to complete the task and if such scenarios happen, that marks the failure of RPA projects.  

Lack of instructions and memo  

RPA is really good when it comes to following the instructions given to the bot for each process concerned, but it lacks behind in its ability to understand and learn things on its own and react to unexpected scenarios. Therefore, we need to witness the growth of work processes where RPA is deployed. RPA bots are incapable of working in situations or projects in which they are not given instructions or training. This mostly results in the failure of the project.   

Poor change management communication  

What most companies fail to think before starting on their RPA journey is about their culture of adapting and supporting changes in their working environment. Adopting new technologies, especially RPA, not only changes the way individuals do their work but also changes the overall behavior of the company. So, there is a need to communicate the changes brought to the company and address employee concerns, which are essential for the success of RPA implementation and further growth of the company. Another important thing is training the skills of the staff and helping them to navigate the newly adopted human-bot culture in the organization. It is important to make sure that your staff understands the working of RPA technology, the development of bots, and their performance efficiency.  

Wrap Up  

In most cases, the vast majority of robotic process automation (RPA) project failures are caused by people. The inappropriate way of understanding the functioning of RPA and the way people execute their operations are some of the most common causes of Robotic Process Automation failure. The aforementioned reasons made it clear that RPA trying to automate complex and difficult processes within a single solution can result in making various errors in businesses. Therefore, a proper approach is essential.