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Key Technologies That Are Integral to Artificial Intelligence

According to John McCarthy, the father of artificial intelligence, Artificial intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs.

Over the years artificial intelligence has continued to expand its horizon, prompting a large number of established companies as well as startups to commercialize this technology. Artificial intelligence has also become the backbone of several popular sci-fi movies and TV serials. According to industry experts, the investment in artificial intelligence is expected to skyrocket up to 300% in the years to come. Today, AI integrates several inter-related technologies and tools. Here are some of the technology trends that are integral to artificial intelligence:Free demo


Biometrics is one of the most widely adopted technology trends that uses computerized techniques to recognize a person by identifying their unique physical or behavioral traits. Fingerprints and face or eye ‘maps’ are considered the critical identification features for this technique. Biometrics is one of the constituting technologies laying the foundation for proper implementation of artificial intelligence. Some of the common applications of this technology include building access and laptop security for identifying IDs and passports.

artificial intelligence

Natural language processing

 Natural language processing is the communication method for artificial intelligence. It facilitates the communication with intelligent systems utilizing a natural language such as English. This aspect of AI is concerned with the interactions between computers and humans. Statistical and machine learning techniques are devised by this technology to comprehend sentence structure and meaning, sentiment, and intent easily. Natural language processing is currently being utilized mostly for fraud detection and security.

Machine learning

Machine learning is a type of artificial intelligence that has become one of the most popular technology trends in the recent times. It furnishes computers with the ability to learn, without being explicitly programmed. Machine learning facilitates artificial intelligence by providing algorithms, APIs, development and training toolkits, and data.

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Planning scheduling optimization

 Planning scheduling optimization is a branch of artificial intelligence that deals with strategies or action sequences designed to be executed by intelligent agents, autonomous robots, and unmanned vehicles. Due to the complexity of these solutions, they must be discovered and optimized in a multidimensional space. Planning scheduling optimization is considered as an essential link in the AI buildup.


 Robotics is concerned with the study of designing intelligent and efficient robots. It is a mixture of several different domains such as electrical engineering, mechanical engineering, and computer science, which aids in designing and constructing robots. Robotics is one of the most revolutionary technology trends known to man. It uses scripts and other techniques to automate human effort and support efficient business processes. Some of the prominent vendors of this technology include Blue Prism, UiPath, and WorkFusion, Advanced Systems Concepts, and Automation Anywhere.

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Virtual assistants

Virtual assistants provide professional, technical, creative and administrative assistance to clients. Over the last couple of years, we have seen the birth of several unique and simple virtual agents like chatbots or advanced systems that can network with humans. They are being used for customer service and support as a smart home manager. Some of the key vendors of this technology include Amazon, Google, IBM, IPsoft, and Microsoft.

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Retail Revolution and the Power of AI

The use of AI and machine learning in the retail industry is growing at an exponential rate. Retailers have been able to see the results delivered by artificial intelligence systems instantly. AI is expected to become pervasive across customer journeys, merchandising, marketing, and supply networks as it can provide detailed insights to optimize the retail operations. Big data and machine learning have been successfully used by several retailers to achieve substantial increase in their operating margins. Such technologies can enable retailers to deliver personalizedFree demo experiences to the customer in order to increase loyalty and spending. There are various use cases for AI in retail industry that can change the way this sector operates.

Uses of AI in retail industry

Sales and CRM applications

In 2010, Japan’s SoftBank telecom developed a humanoid robot, Pepper, which can interact with customers and perceive human emotions. The robot was used as a customer service greeter and representative in over 140 stores. The company later reported up to 70% increase in footfall in multiple stores. Additionally, an American company developed AI-powered sales-assistant software, Conversica, which identifies and converses with internet leads to enhance sales. The customized sales assistant software is also used for cross-selling and re-engaging existing leads.

Customer Recommendation

Product recommendation tools are adding significant revenues to e-commerce businesses. IBM Watson is one of the most advanced AI systems that exhibits order management and customer engagement capabilities. IBM Watson uses personality insights taking into account users personal information, browsing history, past transaction data, and other dynamic data including weather, location, time, and items in cart to develop its recommendation engine. By calculating respective personality profile, IBM Watson can accurately suggest brands and products users are most likely to buy. For instance, North Face has used IBM Watson’s cognitive computing technology to suggest jackets for the customers based on variables like gender and location.


Long after automation revolutionized the manufacturing industry, AI is set to be next wave of change in this sector. AI can help companies keep inventories lean and reduce the cost. For instance, GE’s Brilliant Manufacturing software enables manufacturers to predict, adapt, and react more effectively by incorporating SCADA, MES, and analytics. It empowers decision makers with deep operational intelligence and real-time visibility to reduce unplanned downtime and inventory.

Logistics and delivery

Domino’s Robotic Unit (DRU) has developed a prototype delivery robot that can keep food and drinks at an appropriate temperature. Its sensor also helps the device to navigate the best travel path for delivery. Alongside DRU, Amazon’s Prime Air is expected to be the future of delivery systems. Such drones can deliver parcels up to five pounds in weight in less than 30 minutes. The autonomous delivery of goods can significantly improve the performance of the retail industry and increase customer satisfaction.

Payment services

With a view to reign in the retail industry, Amazon introduced its brick-and-mortar store, Amazon Go which enables check-out free technology that allows customers to shop and leave. Their check-out free shopping experience uses the same kind of AI technology used in autonomous cars including computer vision, sensor fusion, and deep learning. It automatically detects when the products are taken from or returned to shelf keeping track of it in a virtual cart. When customers are done with their shopping, they simply check out of the store, and Amazon will deduct the amount from their Amazon account. With regards to payment services, AI is also showing its potential in payments fraud. Payment fraud is a matter of concern in the e-commerce space, where fraudsters are using stolen accounts to make purchases. AI technologies can study thousands of purchase patterns to differentiate between payment made by the genuine user and a fraudster.

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Five Interesting Applications of AI in the Food Industry You May Not Have Known

AI and machine learning have been the buzzword for quite some time now. The use of such tools was previously limited to the digital world and computer science. However, recently it has made its way into multiple sectors including healthcare, education, finance, marketing, media, transportation, and gaming. Similarly, the food industry is also catching up to the developments in AI. From cooking to detecting food freshness, artificial intelligence has a lot of applications in the food industry. It can help the players in the F&B industry to improve offerings, deliver a better customer experience, and optimize operations. The growing use of AI in the food and beverage industry can be attributed to increasing customer expectations for foods that are healthy, unique, and hanRequest Solution Demodmade. Also, food manufacturers are constantly battling it out to find the perfect mix of taste, appearance, healthiness, and cost. That’s the exact reason why food companies are investing heavily in raw materials, safety and quality controls, and storage and distribution.

AI in the Food Industry


How Is AI Technology Going to Turn Tables for The Banking Sector?

Artificial Intelligence (AI) is no new term in the banking sector; in fact, many financial institutions have already found success with the help of AI. But how can this technology be leveraged for banking functions, you wonder? Artificial intelligence helps improve customer personalization, identify connections, and patterns that cannot be quickly figured out by humans and provides answers to several banking issues in real-time. But that is not all, here are some of the few other benefits that AI promises to offer to the banking sector:

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Top Challenges to Overcome in the Financial Services Industry to Maximize Profits

The financial services industry is a highly dynamic market characterized by high-volatility, high-risk, and billions of dollars’ worth of value. The companies turn to experts in the financial service industry to manage their portfolio, brokerage a deal, or check compliance issues to minimize risks and maximize profits. However, from a financial service providers’ perspective, financial management is not a run-of-the-mill job. A small error can cause millions Free demoworth of damages and push the company to the brink of bankruptcy. Here we take a look at some of the top challenges that are influencing the financial services industry.

Managing Artificial Intelligence, Machine Learning, and Quants

AI, machine learning, and quants far exceed human capabilities in processing massive data sets to derive sense out of it for decision-making. But, eventually, such quants are programmed by humans themselves. Algorithms that are not optimized can yield models that can be completely random. For instance, in late 2007, in an event termed “quant-quake,” Goldman Sachs’ QIS division lost millions of dollars due to the inaccuracy of automated investment algorithms.


Blockchain has been touted as the “next big revolution” in the financial industry as it provides robust security and transparency in financial trading. Blockchain promises immense savings opportunity to early adopters regarding robust security, cost-efficiency, and time savings. However, top players in the financial services industry are facing complications regarding governance, standards, and technical issues.

Regulatory Environment

The financial market is a highly regulated market due to its ability to influence the global economy. Such regulations limit the functioning of the financial services industry and also creates uncertainty due to periodic changes in regulations. Vendors in the financial services industry constantly have to cope with regulatory issues pertaining to risk governance, sustainability, compliance, capital management, cross-border standards, data privacy, frauds, and new market entry.

Enter Quantzig:

Today, managers have access to a large stream of data, and decision-making on the basis of gut-feeling, the rule of thumb, and guessworks are largely eliminated with the advent of data analytics.

“Without big data analytics, companies are blind and deaf, wandering out onto the web like a deer on a freeway,” said a leading data analytics expert from Quantzig.

For more than 14 years, we have assisted our clients across the globe with end-to-end data management and analytics services to leverage their data for prudent decision making. Our firm has worked with 120+ clients, including 55+ Fortune 500 companies. At Quantzig, we firmly believe that the capabilities to harness maximum insights from the influx of continuous information around us is what will drive any organization’s competitive readiness and success. Our objective is to bring together the best combination of analysts and consultants to complement our clients with a shared need to discover and build those capabilities, and drive continuous business excellence.

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Is the World of Artificial Intelligence Crying for Better Data Governance?

As artificial intelligence (AI) makes inroads into various business platforms, industry experts are diligently looking for avenues through which AI solutions can be enhanced. With loads of data being generated, thanks to evolving technologies, there is a pressing need for filtering of data for effective analysis and impressive strategy building. It takes a robust system of data governance to chaff away from the huge data complex and applies AI for enhanced solutions.

Better data governance: What goes into its making?

The topic of data governance came into the news this month with the report from the Royal Society and the British Academy calling for the establishment of an independent body to build a new framework for governing the data use. The goal of this framework is to leverage the potential benefits of data use for boosting innovation in public services, improving business plans, and providing efficient healthcare. Data governance can be improved by:

  1. Improving the existing democratic governance
  2. Protecting the individual as well as collective rights
  3. Research on good practices and adapt accordingly
  4. Work towards a transparent, accountable, and inclusive data use and data management

The ‘ABC’ of improved data governance, as pointed out by the Royal Society are:

Anticipate, monitor, and evaluate: considering alternative futures, managing risks, keeping pace with changes, and reflecting on performance.

Build practices and set standards: enabling and continuously improving well-founded practices that can be spread quickly across relevant sectors and uses.

Clarify, enforce, and remedy: ensuring sufficient arrangements for evidence gathering, debate, and decision-making, and for action in the forms of incentives, permissions, remedies for harm, incentives, and penalties.

Quantzig Speaks

In the complex maze of data generation, industry experts are already questioning the different sources through which data is being generated. At the same time with technology being the lifeline of many of our day to day activities, any kind of data leakage – even if unintentional – can compromise the privacy of the user. A strong and well-framed data governance body is sure to enhance the quality of data generated and act as a double protection against miscreant activities.

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Role of Artificial Intelligence in Improving Pharmacovigilance

Greater emphasis on drug safety is one of the key drivers of the growing demand for pharmacovigilance (PV) software. With the focus on all the major drug regulations being on improving the drug quality and prevention of drug-related problems, pharma companies are increasingly becoming dependent on PV tools. However, the task becomes rather difficult in situations where huge amounts of patient data are being generated on a regular basis. Consequently, a major pain point for most pharma companies face is the execution of foolproof digital vigilance on the data generated.


The lack of efficient and coordinated systems which can seamlessly integrate various data into actionable insights is a concern for most in the pharma industry. It is in this context that artificial intelligence (AI) comes into play. By employing data mining and taking electronic health records (EHRs) into consideration, AI facilitates PV to provide better medication management and improved health assistance.

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Top 5 Trends in Retail Analytics to Watch Out For in 2017

The highly competitive sphere of retail business often has to deal with the challenge of managing the budget and limited people resource in the most productive and cost-effective manner. While tools like AI are already being used to improvise upon the existing marketing strategies, it is interesting to observe how technology and analytics can boost the profits of those in the retail industry. It is in this context that retail analytics has emerged as a viable solution for improved operational insights and better customer intelligence.

Retail Analytics: Anticipate market demands, connect with customers

Contrary to popular belief, retail analytics is not just a mechanism which keeps track of various supply chain activities. Rather it is an on-going process which works by dynamically integrating numerous data sources to provide a holistic view into various business operations. By effectively collecting and analyzing data on a real-time basis, retail analytics not only connects retailers to their customers in a better manner, but it also enables them to anticipate demand for their products and services. The final result is better management of supply chains and greater optimization of marketing strategies.

Top Trends: Adding more power to retail analytics

According to our industry experts, following are the trends in retail analytics which will change the face of retailing through 2017 and beyond:

#1 Adoption of IoT to decipher data

Use of beacons, Wi-Fi, and RFID tags is very common in the retail sector. But here is the twist, all these technological improvisations not only enable better tracking of the products across the supply chain but also helps gain a clear understanding of the customer behavior. The data collected by these sources can be leveraged to understand the loopholes in the existing business plans and building of improved marketing strategies. For instance, through IoT, the data collected through RFID tags and beacons help get a clear picture of popular products and overall sales performance of an organization.

Retail analytics

#2 Micro-segmentation is the new ‘cool’

No doubt segmentation is highly critical for inventory management and pricing strategy, but retail analytics is now paying increased attention to micro-segmentation of the work process. By considering parameters like product and store attributes, demographic segmentation of the customers, and various other micro-features, retailers can identify clusters that  can be scaled for better decision making.

#3 Enhanced customer experience with better management of assortments

Outstanding customer experience is the key to better customer retention. And, the latest trend towards achieving this goal is working on assortment planning. For instance, several major retailers are now using predictive analytics to identify the demand pattern of their products in a particular locality. The result is better inventory management, optimization of costs, and unique customer experience.

#4 Spiking sales figures with dynamic pricing

Talk about dynamic pricing, and the one company which is playing it the best is Amazon. Through retail analytics solutions, entrepreneurs can take aspects like price elasticity into consideration to plan their sales and marketing strategy.

#5 Deep learning algorithms

Though AI is already being used in the retail sector, industry experts believe it is deep learning which will provide an edge to AI data. This is the reason why major providers of retail analytics solutions have integrated deep learning to their system. Insights gained from deep learning are robust and highly scalable making it easier for retailers to route their advertising and promotion spends efficiently.

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Add the ‘Smart’ Element to your HR Analytics with AI

What is human resource without the human element? The critical role played by technology in simplifying and enhancing the entire HR process cannot be denied. As wearables are being increasingly used to track workplace productivity and organizational health, it becomes even more interesting to see how virtual reality can influence the nature of the workplace. Machine learning and AI, thus have both disrupted as well as enhanced the entire system of HR analytics.

What happens when AI and HR analytics meet?

With the growing use of big data, the HR industry is looking for an efficient technology which can simplify their work process and provide valuable insights for a better decision-making process. This marriage between AI and HR analytics results in:

1. Workflow automation

A lot of time and efforts go into scheduling and rescheduling the hiring process. If one comes to think about it, a HR resource who has been dedicatedly doing this job can be intelligently used for other engagements. It is here that AI comes into play. By automating the hiring process, AI not only takes away the load off the HR team but also saves time and effort.

2. Personalization and better recruitment

Personalization is the key to an improved performance of the workforce. By capturing meaningful employee data, AI enables enhanced understanding about how the training modules can be tweaked to give a personalized experience to the employee. This will not only enhance the quality of the training given but will also improve the overall productivity of the organization.

Secondly, let’s accept it that recruitment is one of the tougher jobs. Finding the right person for the right job comes with its own set of challenges. And even though HR is one of the most human-centric industries, it is rather tough to filter out the right candidate. AI empowers the HR analytics to gain base-level data of every individual, making it easier to choose the most suitable candidate thereby speeding up the entire recruitment process.

3. Enhanced employee relations

Improving the employer-employee relation is something every organization is working towards. A chatbot which is powered by AI helps in responding to the commonest of all HR queries and tracks learning patterns and performance of the workforce. By bringing all the elements of a typical workplace interaction together, AI adds the ‘smart’ element to the HR analytics.

Recently a cloud communications provider named Knowlarity added AI to its HR analytics to engage employees and forecast attrition. This is just one of the many examples on how organizations gradually realize the importance of AI in HR activities. The only precaution which the management needs to focus on is that AI does not replace the HR workers. Instead, it should be used to help the HR team focus on issues which cannot be resolved exclusively by  using  the available software.

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Predictive Analytics – Making your Sales Game Stronger

The smartest way to build your sales and marketing strategy is to know beforehand what your target audience is looking forward to. And nothing can make your task easier than predictive analytics when it comes to forecasting future decisions and behavior. Designed to track and provide a clear picture on the dynamics of the buyer’s landscape, predictive analytics plays a rather critical role in giving power to your sales strategy.

Predictive Analytics Opening the Path for Predictive Sales

Rather than gut instinct and informed guess, those into sales strategy are now employing the available data sources to draw a correlation between customer behavior and buying patterns. With the help of AI and machine learning, insights can be gained on what to sell, how to sell it, and whom should it be sold to. This is how predictive analytics has opened the path for predictive sales. By using all the tools of predictive analytics, predictive sales cover all the probabilities which might arise in the future sales scenario.

Advantages of Predictive Selling


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How can Predictive Analytics Mobilize your Sales Strategy?


As already mentioned, predictive analytics analyses the buying signals of your target consumer group and builds models which determine the areas that need to be worked on, and those that demand higher priority. Thus, what the sales team gets, in the end, are inputs on realistic budget spend.

Similarly, it is often seen that untargeted tactics lead to immense waste of time as well as resources. Predictive analytics helps you bring down your sales spend by accurately identifying targets, which the sales team can connect with through CTAs and relevant messaging.

Another major way in which predictive analytics can boost your sales strategy is by taking the past performance of your organization into consideration. It has the capability to take the mathematical trends and patterns in your sales data into account, based on which future possibilities can be anticipated. Thus, it gives due importance to the implications of the historical data to arrive at most effective sales predictions. This way the sales team not only learns from the past but also gets clear-cut guidance on the future strategy building.

Predictive selling is a by-product of predictive analytics. Only recently have organizations realized its importance in empowering their sales strategy. Being one of the market leaders in predictive analytics, Quantzig can help you position the sales budget to your organization’s advantage.

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