Artificial Intelligence Archives - Page 9 of 12

The Ultimate Guide to AIOps: The Future of IT Operations

The modern world is gearing up for the Fourth Industrial Revolution, with smart devices in its hands and rocket-fast data processing capabilities empowering business operations. Amidst the trends, automation and digitization emerge as the two major agendas that exist on the majority of business portfolios. More than 80% of business organizations are going to increase their spending on automation. And recent market studies predict that the digital process automation market will hit the 16.12 bn USD mark by 2026, demonstrating a CAGR of 12.98%. As the automation needs in modern workplaces are reaching new heights and IT operations are looking for smarter, more intuitive, and reliable ways to automate and digitize routine tasks, people are turning to AI. And promising to fulfill such a diverse set of process automation requirements in IT is AIOps. Coined by Gartner in 2016, AIOps stands for Artificial Intelligence for IT Operations and combines ML and big data for process automation in IT operations, such as causality determination, anomaly detection, event correlation, etc. In upcoming sections, you can learn about AIOps in detail, why and how you should consider them for your business, and major ways AIOps will change the face of IT operations in 2022. What is AIOps? AIOps is an industry category that uses AI and ML analytics for automating, streamlining, and enhancing IT operations analytics. AIOps provides a real-time understanding of any type of underlying issues in the IT organizations and real-time insights into various processes. It also offers predictive business intelligence in various forms and across a wide range of business processes because of its smart analytics capabilities. Increasing complexities of IT systems, organizational data growing at an extreme pace, and frequent industry disruptions, such as remote work, new trends in technology, rapidly changing markets, etc., have fuelled the demand for AIOps. Why AIOps: Exploring the Need for Futuristic IT Ops Tech AIOps software not only empowers the organizations to manage the heaps of data they generate, but it will also help in harnessing the true power of this data via analytics. You can identify any hidden patterns for growth, cost efficiency, redundancy, productivity, etc., and gather actionable insights from them. Apart from the data insights derived from historical organizational data, you can also use predictive analytics to have futuristic insights into various segments that are reliable and data-driven. But then again, mere insights cannot solve the massive automation and digital transformation needs; right? Hence, AIOps also caters to the team collaboration needs and provides inferences drawn from the data insights, such as: Finally, AIOps aids in automating the response and remediation in various instances, to facilitate precise and quick process automation across your business model. The following image shows some of the core functionalities of AIOps, as discussed above: Now that we have had an overview, let us move on to discuss some key use cases of AIOps. AIOps: Key Use Cases While there can be n number of business use cases for AIOps, we are discussing the top 5 core use cases that target the key problem areas when it comes to operations, processes and automation. 1. Big Data Management While big data is a powerful technology to manage, process and make sense of the tons of organizational data that is generated, managing the volume, variety, velocity and efficiency of big data usage is also a challenge. As more and more firms are using big data analytics, getting the best value out of these efforts is also a must. Hence, proper management of big data is one of the most crucial use cases of AIOps. 2. Performance Analysis Organizational performance analysis is an extremely daunting task as it requires micro-monitoring apart from micro-management. Now, you might think that using time tracking and employee tracking software can do the job to a greater extent. However, when it comes to getting granular insights about the results, driven by individual employee performance, this software crumbles down. Having impeccably smart capabilities stemming from AI, ML, NLP, etc., AIOps is your eye to the nitty-gritty details related to performance analysis in any department and for any number of employees. It can rapidly gather and process the vast amount of event logs and data to drill down to the root cause of factors that are obstructing the overall productivity of your organization. 3. IT Service Management IT Service Management or ITSM refers to designing, building, supporting, delivering, and managing IT services in an organization. All the processes, policies, and procedures that facilitate the delivery of IT services to the end-users in an organization also come under ITSM. AIOps uses artificial intelligence to discover any underlying issues in ITSM and helps fix them quickly, thereby improving the efficiency across all the departments. AIOPs also find usage in data monitoring and infrastructure process management. It can make more accurate predictions for planning capacity, allowing you to maximize your resource and inventory space utilization. You can also identify, predict, analyze and prevent any underlying IT service issues. 4. Event Correlation and Analysis When it comes to event storms, most of the traditional analytics software fails because they lack the ability to provide insights into these storms. For example, suppose your event correlation report suggests a delay in project delivery for 2 of your crucial clients, as a designer has given a notice period and you still have not hired a new designer. Now, even if you “know” what is going to happen, there is no suggestion or insight to rely upon. AIOps, on the other hand, can help you make the best decision for remedying the situation by grouping similar events. So, you can either get an insight about a solution that came to your rescue in such a situation sometime earlier, say a few months, or you can get to know which one is the best option – outsourcing, a quick hire, or assigning the work to other designers in the company. So, AIOps reduces the burden of the IT teams when it comes to managing organizational events and offers

Artificial Intelligence in Oil and Gas Industry: Remarkable Transformation

Using AI or artificial intelligence in oil and gas industry helps analyze historical and real-time data, run analytics, plan and schedule production, optimize the supply chain, and use resources to increase returns. Let’s learn more about how artificial intelligence impacts the oil and gas industry operations. The oil and gas industry falls among the prominent industries in the global market. As enterprises and companies from other sectors adopt new technology, it’s noteworthy to mention that the oil and gas industry is not lagging behind. Artificial intelligence is playing an important role in streamlining the on-field and off-field activities in the oil and gas industry. Whether it is using AI tools for oil production, transportation, or improving the safety standards at work, artificial intelligence and machine learning are helping companies solve challenges in various areas. The Middle East produces one-third of the world’s oil and is among the biggest suppliers of fossil fuels. This is because the largest oil reserves are found in the Middle East, thanks to the geographic changes that occurred over the centuries. Most of the oil-producing states in the Middle East come under the Organization of the Petroleum Exporting Countries (OPEC). Statistics show that around 39% of the crude oil produced by OPEC countries comes only from the Middle East region. In fact, Saudi Arabia is the largest producer, with more than 12.2 barrels of production per day. Moreover, the Middle East region accounts for 48% of proved oil reserves, 40% of proved gas reserves, and 18% of the global gas production. What role do AI and ML have in the oil and gas industry, especially in the Middle East? How can the global oil and gas companies use artificial intelligence applications to revamp their systems and increase returns? Let’s read to know the answer. How is Artificial Intelligence Used in the Oil And Gas Industry? Managing capital and investments, attracting shareholders, and increasing operational efficiency are some crucial aspects to consider. The role of artificial intelligence is to help oil and gas companies overcome their problems with exploration, production, processing, logistics, and to streamline backend (or office end) services. In short, AI is used in just about every stage in the energy and utilities industry. Exploring and Producing AI is used to forecast the reserve volumes that can be recovered, analyze the reservoir data, help create well-developed plans on the field, choose production models based on advanced analytics, and make decisions based on market trends and changes. Drilling and Refining From designing to execution of drilling, artificial intelligence helps oil and gas companies to find ways that optimize the returns. Risk prediction, daily, monthly, and lifetime production forecasting, detecting defects and anomalies, etc., are done using AI and ML systems. On-Field Equipment & Services Artificial intelligence assists the on-field managers and workers in scheduling drilling activities and optimizing fleet management and supply chain. Equipment maintenance, improving workplace safety, and forecasting consumer demands are done through the latest AI systems and tools. Office Software AI plays a role in many areas beyond the ones where major action occurs. The backend and corporate offices also use AI and ML to empower employees with automated software to increase productivity. The top management makes decisions based on the reports generated through AI applications. How Will Artificial Intelligence Impact Upstream Oil and Gas? Globally, the oil and gas AI market is valued at $2 billion in 2019. Statistics show that it is estimated to touch $3.81 by 2025 at a CAGR (Compound Annual Growth Rate) of 10.96%. Machine Learning and artificial intelligence in the oil and gas industry are being used for data interpretation and analytics to help forecast future trends, discover new reserves, and increase production in existing reserves. While AI, IoT (Internet of Things), and cloud-based technology are gaining acceptance in the oil and gas sector, the lack of professional expertise is slowing down the adoption rate. The oil and gas companies have to hire AI professionals or train their senior employees to use AI and ML tools as a part of their day-to-day work. Generally speaking, the focus of global oil and gas companies is on simplifying and streamlining the exploration and production processes using AI. Digitalization, automation, and data analytics can help companies improve their operations while aligning their goals to the market trends. The Growing Role of Artificial Intelligence in Oil and Gas Industry The Oil and Gas Authority (OGA) has begun using artificial intelligence in 2019, thanks to the National Data Repository (NDR) in the UK. The pandemic has caused a slump in the industry in 2020 (oil prices decreased by about 50% during the lockdown). However, many companies have been actively investing in artificial intelligence to: It’s no surprise that early adopters have the advantage of protecting their assets and attracting more investors. The early adoption of artificial intelligence in the oil and gas industry is delivering similar results as with other industries, i.e., empowering the companies to retain their market positions and expand their share by gaining a competitive edge over others. Applications of Artificial Intelligence in Oil and Gas Development Geological Assessment Geological assessment is also known as surface analysis, where AI robots can detect oil seeps and reserves deep inside the earth or the ocean. The Wadia Institute of Himalayan Geology (WIHG) came out with a new AI technique in 2020. This technique helps analyze data from semantic waves and helps discover the presence of hydrocarbons (like oil and gas) with minimum effort and in quick time. AI tools analyze and assess geophysical data to increase accuracy in mapping the natural oil deposits. Tracking Assets Asset tracking and management include project planning, managing the product lifecycle, and aligning the process to match the market demands. Digital twin technology is being used by the oil and gas companies to deal with the uncertainties in the market (especially due to the pandemic) and to add more flexibility to their system and approach to work. Automating Equipment Maintenance A vital role of artificial intelligence applications in the oil and gas industry is to reduce

Mixed Reality and AI for Safer Surgeries

Mixed reality and AI can help make surgeries safer by assisting surgeons during the process. From providing 3D imaging to handling instruments, AI is a vital part of the operating room. Here, we discuss what mixed reality means and how AI is taking surgeries to the next level. Artificial intelligence, machine learning, and computer vision are becoming an essential part of the healthcare industry. AI is helping doctors, nurses, and the hospital administration streamline patients’ records, accurately diagnose the medical condition, and provide better treatment. The COVID-19 pandemic has increased the focus and adaptation of remote patient care and telemedicine to ensure that patients don’t suffer due to lockdowns. Doctors increasingly rely on AI-based technology and tools to provide patients with quality healthcare. Technology like augmented reality (AR), virtual reality (VR), and mixed reality (MR) are being used in hospitals to assist doctors and train medical students. These are designed to assist doctors during surgeries to make the process safer and increase the success rate. The market size value of AI in healthcare is $10.4 billion in 2021 and is estimated to reach $120.2 billion by 2028 at a CAGR (Compound Annual Growth Rate) of 48 percent. North America has shown the highest adoption rate of AI in healthcare at 58.9 percent in 2020. The same report suggests that the use of AI in the healthcare industry will grow rapidly in the Asia Pacific during the forecast period. What is Mixed Reality? Mixed reality is a combination of virtual reality and human vision. The AR filters used in mobile apps such as Instagram, Snapchat, etc., are a simple example of mixed reality solutions. Surgeries require a lot of planning, practice, and precision. Doctors cannot afford to get distracted or lose focus when operating on a person. The use of AI in surgery aims to support doctors and supply them with the necessary information and surgical tools without disturbing them at any point. Mixed reality makes it possible to use technology to assist doctors during surgeries and minimize risks. Paul Milgram and Fumio Kishino first introduced the term mixed reality in 1994 in their paper titled A Taxonomy of Mixed Reality Visual Displays. MR combines computer vision, cloud computing, graphical processing, etc., to blend the physical and virtual worlds. Many companies have been developing MR applications that can be used in various industries. MR and Healthcare MR in healthcare can be explained using a head-mounted device that gives doctors a clear and better picture of the patient’s data chart and related images. The images of the affected area where the surgery has to be performed can be displayed directly on the patient. This increases accuracy and makes it easy for surgeons to be as precise as possible. MR devices are also changing the interaction method between doctors and patients. Data is more easily accessible and is used to understand the patient’s condition in-depth. Microsoft’s HoloLens is a prime example of MR Headgear that creates realistic images and holograms to help medical students understand human anatomy. Medical schools have begun investing in MR devices to empower their students. Mohammad Abdul Mukit, an MS student in electrical and computer engineering, a graduate fellow and research assistant at the University of Oklahoma, and Dr. Christian El Amm, a plastic and reconstructive surgeon, have collaborated to create an AI-based device (that accepts voice commands) to increase safety and efficiency during surgical reconstructions. SentiAr, a digital health company, has developed a holographic AR platform to provide surgeons with 3D images of a specific anatomy and data visualizations of the patient’s medical chart. The adoption of MR in the healthcare industry is still in its early stages. Technology can bring many more positive changes to improve healthcare facilities and surgical procedures. Use of Mixed Reality in the Healthcare Industry Mixed reality can help doctors, nurses, and healthcare professionals in several ways. From providing real-time information to developing realistic 3D images, here’s what MR can do. Resource Optimization Mixed reality reduces the dependence on nurses and medical assistants. Data is automatically collected, processed, and projected as images and charts whenever the doctor requires it. This saves resources and reduces expenditure without compromising the quality of healthcare services. Real-Time Data Access The mixed reality headsets can provide data in real-time to surgeons. For example, if a surgeon wants crucial data about the patient during an emergency, the MR device can immediately pull it up from the database it is connected to and display it for the surgeon. AI works in the backend to make this possible. Live Streaming Surgeries The MR devices can record and live stream surgeries without interrupting the process. These videos are used to train medical students to become proficient at surgeries. It also helps doctors rewind and replay complex surgical steps and explain the processes clearly to the students. Remote Patient Care Patients can continue to be under home care and still get excellent treatment from their doctors. The latest data is updated to the systems to help doctors understand the patients’ current condition and prescribe medication accordingly. AI has made it possible to deliver quality patient care remotely during the pandemic. Natural 3D Imaging Mixed reality is known for creating realistic 3D images with depth and layers of detail. This feature is very useful when surgeons want to know how deep the infection is or when to stop the incision. Natural 3D imaging increases accuracy and precision in surgeries. Virtual Surgery Intelligence This is used to educate patients about what happens during the surgery. Being aware of the procedure will give patients more confidence and prevent them from being stressed or worried about the results. Remote Assistance in Surgeries MR headsets like Hololens come with microphones and sensors. Surgeons can directly interact with other experts and take their advice during a complicated, high-risk surgery. Experts can provide remote assistance to surgeons in the operation theater. Simplified Data Sharing Critical medical information is stored in a unified format and can be shared with the physicians without any delay. Doctors

How a Perfect AI Strategy can Help Your Business – 25 Industry Experts Share Their Pro Tips

A comprehensive artificial intelligence business strategy can boost business and make the enterprise an industry leader. Let’s look at the round-up of pro tips shared by leaders in the AI industry. The current market scenario is proof that businesses need to adopt the latest technology to survive the competitive markets. Artificial intelligence has become a necessary step in digitally transforming the business and creating new growth opportunities. However, the success of an enterprise depends on how well it plans and implements the AI strategy within the business. Wouldn’t it be helpful to have pro tips and advice from experts who’ve been in the industry for years? This round-up post contains the top tips from twenty-five AI experts known for their domain expertise in various subfields of artificial intelligence. Worth noting strategies by 25 AI Experts Bernard Marr Internationally Best-selling Author | Business, Tech and Data Advisor A separate AI strategy is essential even if a business has a data strategy. AI-enabled services are a better option for small businesses instead of customization. Don’t ignore change management challenges and HR issues. Managing company culture is a must. Allie K. Miller ML Business Development Global Head, AWS Start with the pain point and expected outcome. Add a buffer amount to the budget and work with it. Focus on edge cases and minimize data noise. Don’t expect perfection the first time. AI should be integrated within business workflow. Ankush Sharma CEO & Co-Founder, DataToBiz Start at the beginning and understand the requirements and challenges for AI adoption. Get feedback from customers and make necessary changes to the system. The AI business strategy needs to be constantly evaluated and edited to fine-tune the systems. Dennis R. Mortensen CEO and Founder, x.ai AI should make users more capable while sounding as human as possible when interacting with customers. Automation is the primary goal of using vertical AI agents in the enterprise. Bots and virtual assistants can increase the worth of the employee and the business. Gregory Piatetsky-Shapiro Founder and President, KDnuggets | Data Scientist Data scientists should stop overfitting the data, which defeats the purpose of using the algorithm. Identifying the right problems to tackle within the business is essential for AI implementation to be successful. AI will keep evolving, and data scientists and analysts need to learn and upgrade their domain knowledge. Tamara McCleary CEO, Thulium AI uses reactive and proactive user experience to provide real-time data analytics for marketers. The insights require a huge volume of accurate data to be fed to the algorithms. Issues with business scalability can be solved using AI to structure the business model accordingly. Marketing is much more than human-to-human interaction, and it’s necessary to seamlessly use algorithms, bots, and humans to get the desired results. Andrew Ng Founder and CEO, Landing AI | Founder, deeplearning.ai No need to invest heavily in AI projects in the first stage. Bet on safer and smaller AI technologies that guarantee results. Businesses already have valuable data sitting in their systems. They only need an AI team to work on it. Building AI systems is similar to building a rocket ship. It won’t take off unless the elements are in the right proportion. Ronald Van Loon Principal Analyst and CEO, Intelligent World AI works in combination with IoT (Internet of Things), Big Data, Augmented Reality (AR), and cloud computing to deliver results. Align and organize data around the data layers for accurate insights. Move on from older data storage systems. Start by using ML solutions that are already integrated into the systems. New models can be developed later. Kirk Borne Chief Science Officer, DataPrime Hire a group of data analysts to decide which AI and ML systems to use. Don’t rely on the judgment of one person. Cloud-based infrastructure will reduce analytical costs for the enterprise. It will take time to make the most out of AI systems. Even the leading companies have a lot more to do. Andriy Burkov Director of Data Science, Gartner | Author Testing and perfecting an ML model takes months and years. Hire more ML engineers to speed up the process. Machine learning doesn’t mean the machine is learning. It only provides the necessary numbers at a great speed. Implementation errors in ML can be impossible to find unless various baseline models are implemented which beat the new model and show the error. Hilary Mason Co-Founder, Hidden Door Focus on ‘boring’ business problems to know how much AI can help find an easy solution. AI should be used to overcome challenges rather than as a prestigious investment for the enterprise. The spend-first-plan-later approach by businesses will lead to a higher risk of AI failure. Steve Nouri Chief Data Scientist and Co-Founder, iN2iTY Lab | Founder, AI4Diversity Chatbots and virtual assistants empower employees with self-servicing and increase customer experience. Messy datasets are the ones that deliver results. Businesses should train their data analysts to work on similar sets. The data-centric approach is a great way to train AI systems to be more accurate in their predictions. Kristen Kehrer Founder, Data Moves Me | Senior Manager, Engagys LLC Data literacy training is compulsory for employees to ask the algorithms the right questions. It should be continuous, individualized, and asynchronous. Enriching the data in the enterprise will make it easier to build an ML model. Randy Olson Senior AI Scientist, Absci | Data Science and Machine Learning Advisor, FOXO Technologies Choosing the right machine learning model is the responsibility of humans. GUI-based data visualization tools have become more popular over the last few years and deliver better reports. ML algorithms can be used to analyze most types of data. Ganapathi Pulipaka Chief Data Scientist and SAP Technical Lead, Accenture Chatbots, virtual assistants, and tellers are a great way to manage customer service in various industries like banking, retail, etc. Drones can be effectively used for smart services and linked with IoT to share real-time information. Smart services are the way to drive the economy for an enterprise. That’s possible with AI technology. Mark van

AI for Law: Can AI Replace Lawyers?

How do we describe law? It’s complex, isn’t it, just like how algorithms work? There are a set of laws, regulations, and rules on one side and cases that need to be solved on the other. The lawyers and judges work within a framework using a process similar to how computer programming or machine learning algorithms work behind the scenes. Of course, the law cannot be that easily categorized into algorithms, but ML algorithms sure can be used to help implement the law and deliver justice. Before we talk about artificial intelligence in law firms, let’s first see what a law firm is and how it functions. A law firm is an association or a team of lawyers working together as a business entity and providing legal services to the public. The lawyers of the firm share their clients and profits, depending on their schedule, expertise, and seniority. Law firms follow a hierarchy where the main partners or shareholders sit on the top tier. The senior associates follow next, with the junior lawyers working under senior lawyers and learning from them. A law firm can be organized in several ways, based on where the practice is located. They can follow any of the below arrangements- Law firms also hire paralegals from time to time if required for the case. A firm can have both individuals and corporations as its clients. The primary step is to understand the client’s case, explain legal rights, responsibilities, and advise them on the best way forward. The firm can accept civil and/ or criminal cases, cases that deal with financial transactions, business complications, or any other areas of law. What are the Application Areas of Law? Law is classified into several finer categories, each dealing with a specific aspect of society/ life. A lawyer usually chooses an area of expertise and specialization, which helps them take up cases specific to the category and help their clients. Animal Rights Law An animal rights lawyer can save and protect the lives of those who cannot speak for themselves. Fighting against animal cruelty, getting better facilities for animal care shelters, and exposing animal abusers are some responsibilities of an animal rights lawyer. The lawyer takes up cases to protect domestic and wild animals. Blockchain Law This is a new area of expertise, thanks to the increasing popularity of Bitcoin and several other altcoins and digital currency. Though there aren’t many lawyers who specialize in this area, the field is set to grow and create many opportunities for upcoming lawyers looking for a new and profitable area of law. Civil Rights Law Civil rights are basic human rights every human being in this world is entitled to. Being a civil rights lawyer is a crucial responsibility. Even though many civil rights lawyers work for nonprofit organizations, they also take up other cases of interest, especially when human rights are being violated. Complex Litigation Law Complex litigation cases are difficult to handle and can go on and on for years. Civil and corporate cases with their stakes high and involving some noted individuals or entities are dealt with by complex litigation lawyers. It’s a serious area of law that demands everything from the lawyer. Corporate Law Corporate law deals with the everyday practices of a business and its other complex affairs. Corporate law enters the scene at the very beginning, right where the business is initially being set up. Compliance, contracts, policies, rules, and regulations, etc., come under this area of law. Corporate lawyers also take care of business liabilities and bankruptcy. Criminal Law A criminal lawyer can choose to be a defense lawyer or a prosecutor, or sometimes both. The role and priorities of the lawyer change based on whether they are defending the client or prosecuting an accused. However, the primary role of a criminal lawyer is to protect the basic rights of the client. Criminal lawyers deal with clients from all sections of society. Environmental Law Lawyers who practice environmental law will see more demand as the fight against climate change gets serious. Setting up new regulations, amending existing laws, etc., help protect the environment. The lawyer plays a vital role in promoting these laws and helping individuals/ businesses understand the environmental laws and how these can impact their business. Family Law Family law deals with family-related aspects, be it good or bad. From divorce to inheritance disputes to adoption and child care, it encompasses an array of elements. A lawyer who practices family law has to deal with the emotional aspects of the case as well as the financial and social factors. Healthcare Law The healthcare law deals with the healthcare sector. It includes- The healthcare lawyer works with hospitals, medical centers, doctors, and insurance providers to advise them about their legal obligations and rights. Immigration Law Immigration laws protect immigrants and refugees from being subject to abuse/ neglect/ racism in the country they are taking shelter in. Immigration lawyers are currently in demand as (illegal) immigration, deportation, etc., are the most discussed topics. An immigration lawyer can help families get the justice they deserve in another country. Intellectual Property Law Intellectual property theft is becoming quite common in recent times, and the demand for IP lawyers has gone up. Intellectual property law protects ideas, concepts, theories, formulations, equations, designs, etc., from being duplicated and stolen by others. Copyrights, trademarks, patents, and other such applications are filed to protect intellectual property. Labor Law Labor law deals with the rights of a laborer, worker, or employee of an enterprise. Lawyers who specialize in labor law are almost always in demand because of the varied nature of atrocities faced by employees. From hazardous working environments to sexual harassment and racism (among other things), the lawyer works with clients from the entry-level to top positions in an organization. Sports and Entertainment Law This area of law includes IP (intellectual property) laws, royalty disputes, ownership rights, contacts between different parties involved in the project, etc. The ‘entertainment’ part covers television and

9 Uses of Machine Learning in Business Communications

Artificial Intelligence (AI) and Machine Learning (ML) are becoming an integral part of our lives, at work or home. Enterprises use AI and ML to streamline the business processes and help employees become more productive. AI and ML are used by social media sites, search engines, and OTT platforms to assist users in finding what they want. At home, we use AL-based voice assistants like Alexa, Siri, and Google Home Assistant for several purposes. As days pass, we see ML being extensively adopted by businesses. North America grabs the first place for ML adoption, with 80% of the companies using artificial intelligence and machine learning in some way or another. According to GlobeNewswire, the global market value of machine learning is said to touch $117 billion by 2027 at a CAGR of 39%. So, what do companies use machine learning for? Why are AI and ML so crucial in today’s scenario that it has become necessary to invest in machine learning to stay competitive? Let’s find the answer to this question and more. Importance of Machine Learning in Business Machine learning is a subset of artificial intelligence. It analyses data sets to trace patterns and identify trends that are otherwise difficult to find. It allows a business to automate data analytics and save resources. Understand Customers Businesses have become customer-centric, thanks to the increasing competition. Understanding what the customer wants is essential if you wish to retain them. Remember that your competitors are putting in more effort to attract your customers to their business. Machine learning will analyze customer data to help you understand their preferences, likes, and dislikes. Automate Business Processes How is machine learning used in business? When a task can be completed by a machine in less time, with more efficiency, why dump it on employees? Let machine learning automate recurring tasks so that your employees will have more time to focus on the core projects. Personalization of Ads Customers like variety. They also like to be provided with several options of products/ services they want. How do you attract a customer to your business? Personalized ads are the result of machine learning. You can reach out to a user who wants a product/ service you provide by analyzing their search history and purchase preferences. Increase Business Security Cybersecurity has been a cause of concern for every business. A startup or a multinational company, no one is safe from hackers. But AI and ML-based antivirus software can safeguard your business and prevent cyberattacks by providing multilayer security. Of course, it is expected that hackers will also use the same technology to get in. But machine learning can help identify the weak spots in advance and strengthen the overall security system. Human Resource Management Can machine learning enhance human learning in business working environments? Absolutely! Machine learning is used for human resource management in several ways. From identifying the talent gap to screening applicants and assessing the value of an employee to providing customized training options, ML can help employees become better at work. Every employee’s career growth can be mapped in parallel to the company’s growth. Manufacturing and Logistics AI and ML are being used to streamline inventory and shorten the delivery time. Machine learning uses the existing data to provide valuable insights, whether predictive maintenance or alternative routes to reach the destination earlier. This helps you in making better decisions. What is Artificial Intelligence in Business Communication? What role does AI play in business communication? Artificial intelligence combines machine learning, deep learning, natural language processing, and more such technologies that effectively understand, analyze, and process data to provide meaningful insights. Artificial intelligence has been used to facilitate better communication in recent years. Here’s how AI is used in communication: Chatbots for Customer Service Chatbots have changed the way businesses and customers interact with each other. There is no need for customers to wait long hours, hoping the representative will respond soon. These chatbots are found not only on business websites but also on other communication channels. You can develop a chatbot for Facebook Messenger to respond to followers on that platform. Using chatbots also reduced the cost of expenditure incurred by the customer service department. Smart Campaigns Artificial intelligence solutions can create smart campaigns for marketing and promoting the brand among the target audiences. Customers are segmented and categorized based on their online data. This allows you to create hyper-targeted ads for each customer group and every customer. It can increase the chances of converting a prospective user into a successful lead and customer. Filtering Emails Do we need to tell you about the nuisance of spam emails? They keep coming, don’t they? Unless you have AI-based filters will effectively keep phishing emails out and prevent your employees from falling prey to cyberattacks. Though Gmail is effective, using your own ML-based filters and spamware will better protect against phishing attacks. Sending Automated Smart Replies Natural Language Processing helps understand the text and the intent behind the words. Instead of asking employees to send replies to every mail, this can be automated using AI and ML. The automated smart replies use the appropriate wording to frame the response for every email. Self-Servicing for Employees Similar to how chatbots communicate with customers, the same helpdesk system can be set up for internal customers, aka employees. Machine learning in business applications can help employees answer their queries by contacting the chatbot instead of a human agent. This saves time for both sets of employees. How to Apply Machine Learning to Business Problems? Machine learning can solve several business problems if you know how to use it. Of course, adopting AI and ML has its own set of challenges to deal with. That’s why most enterprises rely on offshore machine learning consulting companies to help with the adoption process. Prediction vs. Decision You need to determine if you want to use machine learning for prediction or decision-making. Using ML for the wrong purpose will result in more loss than profits. Data Processing and Analytics While data is available in abundance, not

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,

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

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

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