Advertisement
AI in Business Analytics: Revolutionizing Decision-Making
Author: Dr. Evelyn Reed, PhD in Data Science and Analytics, with 15 years of experience in applying AI techniques to business problems, currently Chief Data Scientist at InnovateAnalytics Inc.
Publisher: Data Insights Publishing, a leading publisher specializing in data science, business intelligence, and artificial intelligence applications.
Editor: Mr. Robert Jones, MBA, with 10 years of experience in editing and publishing technical articles in the field of data analytics and AI.
Keywords: AI in business analytics, artificial intelligence, business analytics, machine learning, deep learning, predictive analytics, prescriptive analytics, data mining, data visualization, business intelligence, AI-driven insights, data-driven decision making.
Abstract: This article explores the transformative impact of AI in business analytics. We delve into various methodologies and approaches, including machine learning, deep learning, and natural language processing, showcasing how these techniques are revolutionizing decision-making across numerous industries. The article emphasizes the practical applications of AI in business analytics, highlighting its capabilities in predictive modeling, anomaly detection, and automated insights generation.
1. Introduction: The Rise of AI in Business Analytics
The convergence of big data and advanced algorithms has ushered in a new era of business analytics. AI in business analytics is no longer a futuristic concept; it's a rapidly evolving reality transforming how organizations collect, analyze, and interpret data. The sheer volume, velocity, and variety of data generated today necessitate intelligent systems capable of extracting actionable insights efficiently and effectively. AI offers precisely that capability, automating complex analytical tasks and revealing hidden patterns that traditional methods often miss. This article provides a comprehensive overview of the methodologies and applications of AI in business analytics, illustrating its potential to drive strategic decision-making and competitive advantage.
2. Core Methodologies in AI-Driven Business Analytics
Several core AI methodologies underpin the application of AI in business analytics. These include:
Machine Learning (ML): ML algorithms learn from data without explicit programming, identifying patterns and making predictions. In business analytics, ML is used for tasks such as customer segmentation, fraud detection, and predictive maintenance. Supervised learning (e.g., regression, classification), unsupervised learning (e.g., clustering, dimensionality reduction), and reinforcement learning are all valuable tools within the AI in business analytics arsenal.
Deep Learning (DL): A subset of ML, DL employs artificial neural networks with multiple layers to analyze complex data structures. DL excels in tasks requiring high-dimensional data processing, such as image recognition, natural language processing, and time series forecasting. This makes it particularly valuable for AI in business analytics when dealing with unstructured data like customer reviews or social media sentiment.
Natural Language Processing (NLP): NLP techniques enable computers to understand, interpret, and generate human language. In business analytics, NLP is used for sentiment analysis of customer feedback, chatbots for customer service, and automated report generation from unstructured text data. This significantly enhances the ability of AI in business analytics to leverage the insights hidden within textual data.
Computer Vision: This field allows computers to “see” and interpret images and videos. In business analytics, computer vision is used for tasks like quality control in manufacturing, analyzing customer behavior in retail stores using CCTV footage, and optimizing supply chain logistics via drone imagery. The integration of computer vision significantly expands the scope of AI in business analytics beyond traditional structured data.
3. Applications of AI in Business Analytics Across Industries
The applications of AI in business analytics are widespread and impactful across various industries:
Finance: Fraud detection, risk assessment, algorithmic trading, customer churn prediction.
Retail: Customer segmentation, personalized recommendations, inventory optimization, supply chain management.
Healthcare: Disease prediction, personalized medicine, drug discovery, patient risk stratification.
Manufacturing: Predictive maintenance, quality control, supply chain optimization, production planning.
Marketing: Customer segmentation, targeted advertising, campaign optimization, sentiment analysis.
4. Predictive and Prescriptive Analytics Powered by AI
AI significantly enhances both predictive and prescriptive analytics.
Predictive Analytics: AI algorithms analyze historical data to predict future outcomes. This enables proactive decision-making, such as anticipating customer churn or predicting equipment failures. The accuracy and speed of these predictions are significantly improved with AI in business analytics.
Prescriptive Analytics: Building upon predictive analytics, prescriptive analytics suggests optimal actions to achieve desired outcomes. For instance, an AI system might recommend specific marketing campaigns to maximize customer acquisition or suggest optimal inventory levels to minimize costs. This capability is a key differentiator of AI in business analytics.
5. Challenges and Considerations in Implementing AI in Business Analytics
Despite its transformative potential, implementing AI in business analytics presents several challenges:
Data Quality: AI algorithms rely on high-quality data. Inaccurate or incomplete data can lead to flawed insights and poor decision-making.
Data Security and Privacy: Protecting sensitive data is paramount. Robust security measures are crucial when implementing AI in business analytics.
Explainability and Interpretability: Understanding how AI models arrive at their conclusions is essential for building trust and ensuring responsible AI deployment.
Integration with Existing Systems: Integrating AI solutions with existing business systems can be complex and require significant effort.
Talent Acquisition and Skill Development: A skilled workforce is needed to develop, implement, and manage AI solutions.
6. The Future of AI in Business Analytics
The future of AI in business analytics is bright. We can expect further advancements in:
Explainable AI (XAI): Developing more transparent and interpretable AI models.
Automated Machine Learning (AutoML): Automating the process of building and deploying ML models.
Edge AI: Deploying AI models on edge devices for real-time analysis and decision-making.
AI-driven data visualization: Creating more intuitive and insightful data visualizations.
7. Conclusion
AI in business analytics is no longer a niche technology; it's a fundamental driver of innovation and competitive advantage. By leveraging powerful methodologies like machine learning, deep learning, and natural language processing, organizations can extract valuable insights from their data, automate complex analytical tasks, and make data-driven decisions with unprecedented speed and accuracy. While challenges remain, the potential benefits of AI in business analytics are undeniable, promising a future where data-driven insights empower organizations to achieve their strategic objectives more effectively than ever before.
FAQs
1. What is the difference between AI and traditional business analytics? Traditional business analytics relies primarily on statistical methods and human interpretation. AI-powered business analytics leverages advanced algorithms to automate data analysis, identify complex patterns, and generate insights beyond human capabilities.
2. How can AI improve decision-making in my business? AI can provide more accurate predictions, identify hidden opportunities, automate routine tasks, and enable faster, more informed decision-making.
3. What are the ethical considerations of using AI in business analytics? Ethical considerations include data privacy, bias in algorithms, transparency, and accountability. It's crucial to develop and implement AI systems responsibly.
4. What are the key steps involved in implementing AI in business analytics? The steps include defining business objectives, data collection and preparation, model selection and training, deployment, monitoring, and continuous improvement.
5. How much does it cost to implement AI in business analytics? The cost varies depending on the complexity of the solution, the size of the data, and the required expertise.
6. What skills are needed to work with AI in business analytics? Skills include data science, machine learning, programming, data visualization, and business acumen.
7. What are some common pitfalls to avoid when implementing AI in business analytics? Pitfalls include neglecting data quality, choosing the wrong algorithms, ignoring ethical considerations, and lacking sufficient expertise.
8. How can I measure the success of AI in business analytics? Success can be measured by improvements in key performance indicators (KPIs), such as increased revenue, reduced costs, or improved customer satisfaction.
9. What are the future trends in AI in business analytics? Future trends include explainable AI, automated machine learning, and the integration of AI with other technologies like IoT and blockchain.
Related Articles:
1. "Machine Learning for Predictive Maintenance: A Case Study in Manufacturing": This article explores the application of machine learning in predicting equipment failures and optimizing maintenance schedules in the manufacturing industry.
2. "Deep Learning for Customer Segmentation: Unlocking Personalized Marketing Strategies": This article focuses on using deep learning techniques to segment customers based on various attributes and create more targeted marketing campaigns.
3. "Natural Language Processing in Customer Service: Building Intelligent Chatbots": This article discusses how NLP can be used to build chatbots that can effectively handle customer inquiries and improve customer service efficiency.
4. "AI-Driven Fraud Detection in the Financial Sector: A Comprehensive Overview": This article provides a detailed look at how AI is being used to detect and prevent fraudulent activities in the financial industry.
5. "The Role of AI in Supply Chain Optimization: Improving Efficiency and Reducing Costs": This article examines how AI can optimize various aspects of the supply chain, such as inventory management and logistics.
6. "AI and Data Visualization: Creating Powerful and Actionable Insights": This article explores how AI can be used to enhance data visualization techniques and create more intuitive and insightful dashboards.
7. "Ethical Considerations in AI-Driven Business Analytics: Building Responsible Systems": This article focuses on the ethical aspects of AI in business analytics, addressing issues such as bias, transparency, and data privacy.
8. "The Impact of AI on Business Decision-Making: A Strategic Perspective": This article explores the broader strategic implications of AI in business analytics and its impact on organizational decision-making processes.
9. "Overcoming Challenges in Implementing AI in Business Analytics: Best Practices and Solutions": This article addresses common challenges encountered when implementing AI in business analytics and provides practical solutions and best practices.
ai in business analytics: AI-Enabled Analytics for Business Lawrence S. Maisel, Robert J. Zwerling, Jesper H. Sorensen, 2022-01-19 We are entering the era of digital transformation where human and artificial intelligence (AI) work hand in hand to achieve data driven performance. Today, more than ever, businesses are expected to possess the talent, tools, processes, and capabilities to enable their organizations to implement and utilize continuous analysis of past business performance and events to gain forward-looking insight to drive business decisions and actions. AI-Enabled Analytics in Business is your Roadmap to meet this essential business capability. To ensure we can plan for the future vs react to the future when it arrives, we need to develop and deploy a toolbox of tools, techniques, and effective processes to reveal forward-looking unbiased insights that help us understand significant patterns, relationships, and trends. This book promotes clarity to enable you to make better decisions from insights about the future. Learn how advanced analytics ensures that your people have the right information at the right time to gain critical insights and performance opportunities Empower better, smarter decision making by implementing AI-enabled analytics decision support tools Uncover patterns and insights in data, and discover facts about your business that will unlock greater performance Gain inspiration from practical examples and use cases showing how to move your business toward AI-Enabled decision making AI-Enabled Analytics in Business is a must-have practical resource for directors, officers, and executives across various functional disciplines who seek increased business performance and valuation. |
ai in business analytics: Artificial Intelligence for Business Doug Rose, 2020-12-09 The Easy Introduction to Machine Learning (Ml) for Nontechnical People--In Business and Beyond Artificial Intelligence for Business is your plain-English guide to Artificial Intelligence (AI) and Machine Learning (ML): how they work, what they can and cannot do, and how to start profiting from them. Writing for nontechnical executives and professionals, Doug Rose demystifies AI/ML technology with intuitive analogies and explanations honed through years of teaching and consulting. Rose explains everything from early “expert systems” to advanced deep learning networks. First, Rose explains how AI and ML emerged, exploring pivotal early ideas that continue to influence the field. Next, he deepens your understanding of key ML concepts, showing how machines can create strategies and learn from mistakes. Then, Rose introduces current powerful neural networks: systems inspired by the structure and function of the human brain. He concludes by introducing leading AI applications, from automated customer interactions to event prediction. Throughout, Rose stays focused on business: applying these technologies to leverage new opportunities and solve real problems. Compare the ways a machine can learn, and explore current leading ML algorithms Start with the right problems, and avoid common AI/ML project mistakes Use neural networks to automate decision-making and identify unexpected patterns Help neural networks learn more quickly and effectively Harness AI chatbots, virtual assistants, virtual agents, and conversational AI applications |
ai in business analytics: Artificial Intelligence for Business Analytics Felix Weber, 2023 While methods of artificial intelligence (AI) were until a few years ago exclusively a topic of scientific discussions, today they are increasingly finding their way into products of everyday life. At the same time, the amount of data produced and available is growing due to increasing digitization, the integration of digital measurement and control systems, and automatic exchange between devices (Internet of Things). In the future, the use of business intelligence (BI) and a look into the past will no longer be sufficient for most companies. Instead, business analytics, i.e., predictive and predictive analyses and automated decisions, will be needed to stay competitive in the future. The use of growing amounts of data is a significant challenge and one of the most important areas of data analysis is represented by artificial intelligence methods. This book provides a concise introduction to the essential aspects of using artificial intelligence methods for business analytics, presents machine learning and the most important algorithms in a comprehensible form based on the business analytics technology framework, and shows application scenarios from various industries. In addition, it provides the Business Analytics Model for Artificial Intelligence, a reference procedure model for structuring BA and AI projects in the company. The Content Business Analytics Artificial Intelligence AI and BA platforms Technology framework and procedure model as reference Case studies on the use of AI-based business analytics The Author Felix Weber is a researcher at the University of Duisburg-Essen with a focus on digitalization, artificial intelligence, price, promotion, assortment management, and transformation management. At the Chair of Business Informatics and Integrated Information Systems, he founded the Retail Artificial Intelligence Lab (retAIL). At the same time, he also worked on various jobs as a consultant for SAP systems in retail, Head of Data Science and as Head of ERP. He thus combines current practice with scientific research in this subfield. This book is a translation of an original German edition. The translation was done with the help of artificial intelligence (machine translation by the service DeepL.com). A subsequent human revision was done primarily in terms of content, so that the book will read stylistically differently from a conventional translation. |
ai in business analytics: Leading with AI and Analytics: Build Your Data Science IQ to Drive Business Value Eric Anderson, Florian Zettelmeyer, 2020-11-23 Lead your organization to become evidence-driven Data. It’s the benchmark that informs corporate projections, decision-making, and analysis. But, why do many organizations that see themselves as data-driven fail to thrive? In Leading with AI and Analytics, two renowned experts from the Kellogg School of Management show business leaders how to transform their organization to become evidence-driven, which leads to real, measurable changes that can help propel their companies to the top of their industries. The availability of unprecedented technology-enabled tools has made AI (Artificial Intelligence) an essential component of business analytics. But what’s often lacking are the leadership skills to integrate these technologies to achieve maximum value. Here, the authors provide a comprehensive game plan for developing that all-important human factor to get at the heart of data science: the ability to apply analytical thinking to real-world problems. Each of these tools and techniques comes to powerful life through a wealth of powerful case studies and real-world success stories. Inside, you’ll find the essential tools to help you: Develop a strong data science intuition quotient Lead and scale AI and analytics throughout your organization Move from “best-guess” decision making to evidence-based decisions Craft strategies and tactics to create real impact Written for anyone in a leadership or management role—from C-level/unit team managers to rising talent—this powerful, hands-on guide meets today’s growing need for real-world tools to lead and succeed with data. |
ai in business analytics: AI Meets BI Lakshman Bulusu, Rosendo Abellera, 2020-11-03 With the emergence of Artificial Intelligence (AI) in the business world, a new era of Business Intelligence (BI) has been ushered in to create real-world business solutions using analytics. BI developers and practitioners now have tools and technologies to create systems and solutions to guide effective decision making. Decisions can be made on the basis of more reliable and accurate information and intelligence, which can lead to valuable, actionable insights for business. Previously, BI professionals were stymied by bad or incomplete data, poorly architected solutions, or even just outright incapable systems or resources. With the advent of AI, BI has new possibilities for effectiveness. This is a long-awaited phase for practitioners and developers and, moreover, for executives and leaders relying on knowledgeable and intelligent decision making for their organizations. Beginning with an outline of the traditional methods for implementing BI in the enterprise and how BI has evolved into using self-service analytics, data discovery, and most recently AI, AI Meets BI first lays out the three typical architectures of the first, second, and third generations of BI. It then takes an in-depth look at various types of analytics and highlights how each of these can be implemented using AI-enabled algorithms and deep learning models. The crux of the book is four industry use cases. They describe how an enterprise can access, assess, and perform analytics on data by way of discovering data, defining key metrics that enable the same, defining governance rules, and activating metadata for AI/ML recommendations. Explaining the implementation specifics of each of these four use cases by way of using various AI-enabled machine learning and deep learning algorithms, this book provides complete code for each of the implementations, along with the output of the code, supplemented by visuals that aid in BI-enabled decision making. Concluding with a brief discussion of the cognitive computing aspects of AI, the book looks at future trends, including augmented analytics, automated and autonomous BI, and security and governance of AI-powered BI. |
ai in business analytics: Advanced Analytics and AI Tony Boobier, 2018-04-03 Be prepared for the arrival of automated decision making Once thought of as science fiction, major corporations are already beginning to use cognitive systems to assist in providing wealth advice and also in medication treatment. The use of Cognitive Analytics/Artificial Intelligence (AI) Systems is set to accelerate, with the expectation that it’ll be considered ‘mainstream’ in the next 5 – 10 years. It’ll change the way we as individuals interact with data and systems—and the way we run our businesses. Cognitive Analysis and AI prepares business users for the era of cognitive analytics / artificial intelligence. Building on established texts and commentary, it specifically prepares you in terms of expectation, impact on personal roles, and responsibilities. It focuses on the specific impact on key industries (retail, financial services, utilities and media) and also on key professions (such as accounting, operational management, supply chain and risk management). Shows you how users interact with the system in natural language Explains how cognitive analysis/AI can source ‘big data’ Provides a roadmap for implementation Gets you up to speed now before you get left behind If you’re a decision maker or budget holder within the corporate context, this invaluable book helps you gain an advantage from the deployment of cognitive analytics tools. |
ai in business analytics: Artificial Intelligence Harvard Business Review, 2019 Companies that don't use AI to their advantage will soon be left behind. Artificial intelligence and machine learning will drive a massive reshaping of the economy and society. What should you and your company be doing right now to ensure that your business is poised for success? These articles by AI experts and consultants will help you understand today's essential thinking on what AI is capable of now, how to adopt it in your organization, and how the technology is likely to evolve in the near future. Artificial Intelligence: The Insights You Need from Harvard Business Review will help you spearhead important conversations, get going on the right AI initiatives for your company, and capitalize on the opportunity of the machine intelligence revolution. Catch up on current topics and deepen your understanding of them with the Insights You Need series from Harvard Business Review. Featuring some of HBR's best and most recent thinking, Insights You Need titles are both a primer on today's most pressing issues and an extension of the conversation, with interesting research, interviews, case studies, and practical ideas to help you explore how a particular issue will impact your company and what it will mean for you and your business. |
ai in business analytics: Artificial Intelligence for Business Rajendra Akerkar, 2018-08-11 This book offers a practical guide to artificial intelligence (AI) techniques that are used in business. The book does not focus on AI models and algorithms, but instead provides an overview of the most popular and frequently used models in business. This allows the book to easily explain AI paradigms and concepts for business students and executives. Artificial Intelligence for Business is divided into six chapters. Chapter 1 begins with a brief introduction to AI and describes its relationship with machine learning, data science and big data analytics. Chapter 2 presents core machine learning workflow and the most effective machine learning techniques. Chapter 3 deals with deep learning, a popular technique for developing AI applications. Chapter 4 introduces recommendation engines for business and covers how to use them to be more competitive. Chapter 5 features natural language processing (NLP) for sentiment analysis focused on emotions. With the help of sentiment analysis, businesses can understand their customers better to improve their experience, which will help the businesses change their market position. Chapter 6 states potential business prospects of AI and the benefits that companies can realize by implementing AI in their processes. |
ai in business analytics: Data Analytics and AI Jay Liebowitz, 2020-08-06 Analytics and artificial intelligence (AI), what are they good for? The bandwagon keeps answering, absolutely everything! Analytics and artificial intelligence have captured the attention of everyone from top executives to the person in the street. While these disciplines have a relatively long history, within the last ten or so years they have exploded into corporate business and public consciousness. Organizations have rushed to embrace data-driven decision making. Companies everywhere are turning out products boasting that artificial intelligence is included. We are indeed living in exciting times. The question we need to ask is, do we really know how to get business value from these exciting tools? Unfortunately, both the analytics and AI communities have not done a great job in collaborating and communicating with each other to build the necessary synergies. This book bridges the gap between these two critical fields. The book begins by explaining the commonalities and differences in the fields of data science, artificial intelligence, and autonomy by giving a historical perspective for each of these fields, followed by exploration of common technologies and current trends in each field. The book also readers introduces to applications of deep learning in industry with an overview of deep learning and its key architectures, as well as a survey and discussion of the main applications of deep learning. The book also presents case studies to illustrate applications of AI and analytics. These include a case study from the healthcare industry and an investigation of a digital transformation enabled by AI and analytics transforming a product-oriented company into one delivering solutions and services. The book concludes with a proposed AI-informed data analytics life cycle to be applied to unstructured data. |
ai in business analytics: Artificial Intelligence and Machine Learning in Business Management Sandeep Kumar Panda, Vaibhav Mishra, R. Balamurali, Ahmed A. Elngar, 2021-11-04 Artificial Intelligence and Machine Learning in Business Management The focus of this book is to introduce artificial intelligence (AI) and machine learning (ML) technologies into the context of business management. The book gives insights into the implementation and impact of AI and ML to business leaders, managers, technology developers, and implementers. With the maturing use of AI or ML in the field of business intelligence, this book examines several projects with innovative uses of AI beyond data organization and access. It follows the Predictive Modeling Toolkit for providing new insight on how to use improved AI tools in the field of business. It explores cultural heritage values and risk assessments for mitigation and conservation and discusses on-shore and off-shore technological capabilities with spatial tools for addressing marketing and retail strategies, and insurance and healthcare systems. Taking a multidisciplinary approach for using AI, this book provides a single comprehensive reference resource for undergraduate, graduate, business professionals, and related disciplines. |
ai in business analytics: E-Business Robert M.X. Wu, Marinela Mircea, 2021-05-19 This book provides the latest viewpoints of scientific research in the field of e-business. It is organized into three sections: “Higher Education and Digital Economy Development”, “Artificial Intelligence in E-Business”, and “Business Intelligence Applications”. Chapters focus on China’s higher education in e-commerce, digital economy development, natural language processing applications in business, Information Technology Governance, Risk and Compliance (IT GRC), business intelligence, and more. |
ai in business analytics: Artificial Intelligence in Practice Bernard Marr, 2019-04-15 Cyber-solutions to real-world business problems Artificial Intelligence in Practice is a fascinating look into how companies use AI and machine learning to solve problems. Presenting 50 case studies of actual situations, this book demonstrates practical applications to issues faced by businesses around the globe. The rapidly evolving field of artificial intelligence has expanded beyond research labs and computer science departments and made its way into the mainstream business environment. Artificial intelligence and machine learning are cited as the most important modern business trends to drive success. It is used in areas ranging from banking and finance to social media and marketing. This technology continues to provide innovative solutions to businesses of all sizes, sectors and industries. This engaging and topical book explores a wide range of cases illustrating how businesses use AI to boost performance, drive efficiency, analyse market preferences and many others. Best-selling author and renowned AI expert Bernard Marr reveals how machine learning technology is transforming the way companies conduct business. This detailed examination provides an overview of each company, describes the specific problem and explains how AI facilitates resolution. Each case study provides a comprehensive overview, including some technical details as well as key learning summaries: Understand how specific business problems are addressed by innovative machine learning methods Explore how current artificial intelligence applications improve performance and increase efficiency in various situations Expand your knowledge of recent AI advancements in technology Gain insight on the future of AI and its increasing role in business and industry Artificial Intelligence in Practice: How 50 Successful Companies Used Artificial Intelligence to Solve Problems is an insightful and informative exploration of the transformative power of technology in 21st century commerce. |
ai in business analytics: AI-Based Data Analytics Kiran Chaudhary, Mansaf Alam, 2023-12-29 Apply analytics to improve customer experience, AI applied to targeted and personalized marketing Debugging and simulation tools and techniques for massive data systems |
ai in business analytics: Competing in the Age of AI Marco Iansiti, Karim R. Lakhani, 2020-01-07 a provocative new book — The New York Times AI-centric organizations exhibit a new operating architecture, redefining how they create, capture, share, and deliver value. Now with a new preface that explores how the coronavirus crisis compelled organizations such as Massachusetts General Hospital, Verizon, and IKEA to transform themselves with remarkable speed, Marco Iansiti and Karim R. Lakhani show how reinventing the firm around data, analytics, and AI removes traditional constraints on scale, scope, and learning that have restricted business growth for hundreds of years. From Airbnb to Ant Financial, Microsoft to Amazon, research shows how AI-driven processes are vastly more scalable than traditional processes, allow massive scope increase, enabling companies to straddle industry boundaries, and create powerful opportunities for learning—to drive ever more accurate, complex, and sophisticated predictions. When traditional operating constraints are removed, strategy becomes a whole new game, one whose rules and likely outcomes this book will make clear. Iansiti and Lakhani: Present a framework for rethinking business and operating models Explain how collisions between AI-driven/digital and traditional/analog firms are reshaping competition, altering the structure of our economy, and forcing traditional companies to rearchitect their operating models Explain the opportunities and risks created by digital firms Describe the new challenges and responsibilities for the leaders of both digital and traditional firms Packed with examples—including many from the most powerful and innovative global, AI-driven competitors—and based on research in hundreds of firms across many sectors, this is your essential guide for rethinking how your firm competes and operates in the era of AI. |
ai in business analytics: AI-Driven Intelligent Models for Business Excellence Samala Nagaraj, Korupalli V. Rajesh Kumar, 2022 As digital technology is taking the world in a revolutionary way and business related aspects are getting smarter this book is a potential research source on the Artificial Intelligence-based Business Applications and Intelligence-- |
ai in business analytics: HBR's 10 Must Reads on AI, Analytics, and the New Machine Age (with bonus article "Why Every Company Needs an Augmented Reality Strategy" by Michael E. Porter and James E. Heppelmann) Harvard Business Review, Michael E. Porter, Thomas H. Davenport, Paul Daugherty, H. James Wilson, 2018-12-24 Intelligent machines are revolutionizing business. Machine learning and data analytics are powering a wave of groundbreaking technologies. Is your company ready? If you read nothing else on how intelligent machines are revolutionizing business, read these 10 articles. We've combed through hundreds of Harvard Business Review articles and selected the most important ones to help you understand how these technologies work together, how to adopt them, and why your strategy can't ignore them. In this book you'll learn how: Data science, driven by artificial intelligence and machine learning, is yielding unprecedented business insights Blockchain has the potential to restructure the economy Drones and driverless vehicles are becoming essential tools 3-D printing is making new business models possible Augmented reality is transforming retail and manufacturing Smart speakers are redefining the rules of marketing Humans and machines are working together to reach new levels of productivity This collection of articles includes Artificial Intelligence for the Real World, by Thomas H. Davenport and Rajeev Ronanki; Stitch Fix's CEO on Selling Personal Style to the Mass Market, by Katrina Lake; Algorithms Need Managers, Too, by Michael Luca, Jon Kleinberg, and Sendhil Mullainathan; Marketing in the Age of Alexa, by Niraj Dawar; Why Every Organization Needs an Augmented Reality Strategy, by Michael E. Porter and James E. Heppelmann; Drones Go to Work, by Chris Anderson; The Truth About Blockchain, by Marco Iansiti and Karim R. Lakhani; The 3-D Printing Playbook, by Richard A. D’Aveni; Collaborative Intelligence: Humans and AI Are Joining Forces, by H. James Wilson and Paul R. Daugherty; When Your Boss Wears Metal Pants, by Walter Frick; and Managing Our Hub Economy, by Marco Iansiti and Karim R. Lakhani. |
ai in business analytics: Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry Chkoniya, Valentina, 2021-06-25 The contemporary world lives on the data produced at an unprecedented speed through social networks and the internet of things (IoT). Data has been called the new global currency, and its rise is transforming entire industries, providing a wealth of opportunities. Applied data science research is necessary to derive useful information from big data for the effective and efficient utilization to solve real-world problems. A broad analytical set allied with strong business logic is fundamental in today’s corporations. Organizations work to obtain competitive advantage by analyzing the data produced within and outside their organizational limits to support their decision-making processes. This book aims to provide an overview of the concepts, tools, and techniques behind the fields of data science and artificial intelligence (AI) applied to business and industries. The Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry discusses all stages of data science to AI and their application to real problems across industries—from science and engineering to academia and commerce. This book brings together practice and science to build successful data solutions, showing how to uncover hidden patterns and leverage them to improve all aspects of business performance by making sense of data from both web and offline environments. Covering topics including applied AI, consumer behavior analytics, and machine learning, this text is essential for data scientists, IT specialists, managers, executives, software and computer engineers, researchers, practitioners, academicians, and students. |
ai in business analytics: Artificial Intelligence Business Przemek Chojecki, 2020-07-15 ** The concise guide to Artificial Intelligence for business people and commercially oriented data scientists ** We’re living through a revolution. Artificial Intelligence is changing how we operate in the world and how smooth certain processes are. Just think about going on holidays. Multiple services allow you to find the most convenient flights and best hotels, you get personalized suggestions on what you might want to see, you go to the airport via one of the ride-sharing apps. At each of these steps, some AI algorithms are at work for your convenience. With this book, you'll learn everything from what is Artificial Intelligence, to how AI influences our economy and society. We'll talk through trends in Machine Learning and commercial applications of Artificial Intelligence. Table of Contents: Introduction Why Artificial Intelligence Practical AI and how it is done Powering Enterprises with AI Boosting Startups with Artificial Intelligence One person enhanced with AI Trends in Artificial Intelligence AI in retail Manufacturing Logistics Robotics and Autonomous Vehicles Robotic Process Automation Image generation Text generation and Chatbots AI-powered education AI in Healthcare Cybersecurity powered by AI Climate Change Games and Reinforcement Learning Hardware and beyond Machine Learning Trends AI, Politics and Society Future of Artificial Intelligence |
ai in business analytics: Analytics, Data Science, and Artificial Intelligence Ramesh Sharda, Dursun Delen, Efraim Turban, 2020-03-06 For courses in decision support systems, computerized decision-making tools, and management support systems. Market-leading guide to modern analytics, for better business decisionsAnalytics, Data Science, & Artificial Intelligence: Systems for Decision Support is the most comprehensive introduction to technologies collectively called analytics (or business analytics) and the fundamental methods, techniques, and software used to design and develop these systems. Students gain inspiration from examples of organisations that have employed analytics to make decisions, while leveraging the resources of a companion website. With six new chapters, the 11th edition marks a major reorganisation reflecting a new focus -- analytics and its enabling technologies, including AI, machine-learning, robotics, chatbots, and IoT. |
ai in business analytics: Responsible AI and Analytics for an Ethical and Inclusive Digitized Society Denis Dennehy, Anastasia Griva, Nancy Pouloudi, Yogesh K. Dwivedi, Ilias Pappas, Matti Mäntymäki, 2021-08-25 This volume constitutes the proceedings of the 20th IFIP WG 6.11 Conference on e-Business, e-Services, and e-Society, I3E 2021, held in Galway, Ireland, in September 2021.* The total of 57 full and 8 short papers presented in these volumes were carefully reviewed and selected from 141 submissions. The papers are organized in the following topical sections: AI for Digital Transformation and Public Good; AI & Analytics Decision Making; AI Philosophy, Ethics & Governance; Privacy & Transparency in a Digitized Society; Digital Enabled Sustainable Organizations and Societies; Digital Technologies and Organizational Capabilities; Digitized Supply Chains; Customer Behavior and E-business; Blockchain; Information Systems Development; Social Media & Analytics; and Teaching & Learning. *The conference was held virtually due to the COVID-19 pandemic. |
ai in business analytics: The Organisation of Tomorrow Mark Van Rijmenam, 2019-07-19 The Organisation of Tomorrow presents a new model of doing business and explains how big data analytics, blockchain and artificial intelligence force us to rethink existing business models and develop organisations that will be ready for human-machine interactions. It also asks us to consider the impacts of these emerging information technologies on people and society. Big data analytics empowers consumers and employees. This can result in an open strategy and a better understanding of the changing environment. Blockchain enables peer-to-peer collaboration and trustless interactions governed by cryptography and smart contracts. Meanwhile, artificial intelligence allows for new and different levels of intensity and involvement among human and artificial actors. With that, new modes of organising are emerging: where technology facilitates collaboration between stakeholders; and where human-to-human interactions are increasingly replaced with human-to-machine and even machine-to-machine interactions. This book offers dozens of examples of industry leaders such as Walmart, Telstra, Alibaba, Microsoft and T-Mobile, before presenting the D2 + A2 model – a new model to help organisations datafy their business, distribute their data, analyse it for insights and automate processes and customer touchpoints to be ready for the data-driven and exponentially-changing society that is upon us This book offers governments, professional services, manufacturing, finance, retail and other industries a clear approach for how to develop products and services that are ready for the twenty-first century. It is a must-read for every organisation that wants to remain competitive in our fast-changing world. |
ai in business analytics: Business Analytical Capabilities and Artificial Intelligence-enabled Analytics: Applications and Challenges in the Digital Era, Volume 2 Abdalmuttaleb M. A. Musleh Al-Sartawi, |
ai in business analytics: Applied Artificial Intelligence: Where AI Can Be Used In Business Francesco Corea, 2018-03-09 This book deals with artificial intelligence (AI) and its several applications. It is not an organic text that should be read from the first page onwards, but rather a collection of articles that can be read at will (or at need). The idea of this work is indeed to provide some food for thoughts on how AI is impacting few verticals (insurance and financial services), affecting horizontal and technical applications (speech recognition and blockchain), and changing organizational structures (introducing new figures or dealing with ethical issues). The structure of the chapter is very similar, so I hope the reader won’t find difficulties in establishing comparisons or understanding the differences between specific problems AI is being used for. The first chapter of the book is indeed showing the potential and the achievements of new AI techniques in the speech recognition domain, touching upon the topics of bots and conversational interfaces. The second and thirds chapter tackle instead verticals that are historically data-intensive but not data-driven, i.e., the financial sector and the insurance one. The following part of the book is the more technical one (and probably the most innovative), because looks at AI and its intersection with another exponential technology, namely the blockchain. Finally, the last chapters are instead more operative, because they concern new figures to be hired regardless of the organization or the sector, and ethical and moral issues related to the creation and implementation of new type of algorithms. |
ai in business analytics: Building Analytics Teams John K. Thompson, Douglas B. Laney, 2020-06-30 Master the skills necessary to hire and manage a team of highly skilled individuals to design, build, and implement applications and systems based on advanced analytics and AI Key FeaturesLearn to create an operationally effective advanced analytics team in a corporate environmentSelect and undertake projects that have a high probability of success and deliver the improved top and bottom-line resultsUnderstand how to create relationships with executives, senior managers, peers, and subject matter experts that lead to team collaboration, increased funding, and long-term success for you and your teamBook Description In Building Analytics Teams, John K. Thompson, with his 30+ years of experience and expertise, illustrates the fundamental concepts of building and managing a high-performance analytics team, including what to do, who to hire, projects to undertake, and what to avoid in the journey of building an analytically sound team. The core processes in creating an effective analytics team and the importance of the business decision-making life cycle are explored to help achieve initial and sustainable success. The book demonstrates the various traits of a successful and high-performing analytics team and then delineates the path to achieve this with insights on the mindset, advanced analytics models, and predictions based on data analytics. It also emphasizes the significance of the macro and micro processes required to evolve in response to rapidly changing business needs. The book dives into the methods and practices of managing, developing, and leading an analytics team. Once you've brought the team up to speed, the book explains how to govern executive expectations and select winning projects. By the end of this book, you will have acquired the knowledge to create an effective business analytics team and develop a production environment that delivers ongoing operational improvements for your organization. What you will learnAvoid organizational and technological pitfalls of moving from a defined project to a production environmentEnable team members to focus on higher-value work and tasksBuild Advanced Analytics and Artificial Intelligence (AA&AI) functions in an organizationOutsource certain projects to competent and capable third partiesSupport the operational areas that intend to invest in business intelligence, descriptive statistics, and small-scale predictive analyticsAnalyze the operational area, the processes, the data, and the organizational resistanceWho this book is for This book is for senior executives, senior and junior managers, and those who are working as part of a team that is accountable for designing, building, delivering and ensuring business success through advanced analytics and artificial intelligence systems and applications. At least 5 to 10 years of experience in driving your organization to a higher level of efficiency will be helpful. |
ai in business analytics: AI for People and Business Alex Castrounis, 2019-07-05 If you’re an executive, manager, or anyone interested in leveraging AI within your organization, this is your guide. You’ll understand exactly what AI is, learn how to identify AI opportunities, and develop and execute a successful AI vision and strategy. Alex Castrounis, business consultant and former IndyCar engineer and race strategist, examines the value of AI and shows you how to develop an AI vision and strategy that benefits both people and business. AI is exciting, powerful, and game changing—but too many AI initiatives end in failure. With this book, you’ll explore the risks, considerations, trade-offs, and constraints for pursuing an AI initiative. You’ll learn how to create better human experiences and greater business success through winning AI solutions and human-centered products. Use the book’s AIPB Framework to conduct end-to-end, goal-driven innovation and value creation with AI Define a goal-aligned AI vision and strategy for stakeholders, including businesses, customers, and users Leverage AI successfully by focusing on concepts such as scientific innovation and AI readiness and maturity Understand the importance of executive leadership for pursuing AI initiatives A must read for business executives and managers interested in learning about AI and unlocking its benefits. Alex Castrounis has simplified complex topics so that anyone can begin to leverage AI within their organization. - Dan Park, GM & Director, Uber Alex Castrounis has been at the forefront of helping organizations understand the promise of AI and leverage its benefits, while avoiding the many pitfalls that can derail success. In this essential book, he shares his expertise with the rest of us. - Dean Wampler, Ph.D., VP, Fast Data Engineering at Lightbend |
ai in business analytics: Artificial Intelligence for Business Analytics Felix Weber, 2023-03-01 While methods of artificial intelligence (AI) were until a few years ago exclusively a topic of scientific discussions, today they are increasingly finding their way into products of everyday life. At the same time, the amount of data produced and available is growing due to increasing digitalization, the integration of digital measurement and control systems, and automatic exchange between devices (Internet of Things). In the future, the use of business intelligence (BI) and a look into the past will no longer be sufficient for most companies.Instead, business analytics, i.e., predictive and predictive analyses and automated decisions, will be needed to stay competitive in the future. The use of growing amounts of data is a significant challenge and one of the most important areas of data analysis is represented by artificial intelligence methods.This book provides a concise introduction to the essential aspects of using artificial intelligence methods for business analytics, presents machine learning and the most important algorithms in a comprehensible form using the business analytics technology framework, and shows application scenarios from various industries. In addition, it provides the Business Analytics Model for Artificial Intelligence, a reference procedure model for structuring BA and AI projects in the company. This book is a translation of the original German 1st edition Künstliche Intelligenz für Business Analytics by Felix Weber, published by Springer Fachmedien Wiesbaden GmbH, part of Springer Nature in 2020. The translation was done with the help of artificial intelligence (machine translation by the service DeepL.com). A subsequent human revision was done primarily in terms of content, so that the book will read stylistically differently from a conventional translation. Springer Nature works continuously to further the development of tools for the production of books and on the related technologies to support the authors. |
ai in business analytics: The AI Advantage Thomas H. Davenport, 2019-08-06 Cutting through the hype, a practical guide to using artificial intelligence for business benefits and competitive advantage. In The AI Advantage, Thomas Davenport offers a guide to using artificial intelligence in business. He describes what technologies are available and how companies can use them for business benefits and competitive advantage. He cuts through the hype of the AI craze—remember when it seemed plausible that IBM's Watson could cure cancer?—to explain how businesses can put artificial intelligence to work now, in the real world. His key recommendation: don't go for the “moonshot” (curing cancer, or synthesizing all investment knowledge); look for the “low-hanging fruit” to make your company more efficient. Davenport explains that the business value AI offers is solid rather than sexy or splashy. AI will improve products and processes and make decisions better informed—important but largely invisible tasks. AI technologies won't replace human workers but augment their capabilities, with smart machines to work alongside smart people. AI can automate structured and repetitive work; provide extensive analysis of data through machine learning (“analytics on steroids”), and engage with customers and employees via chatbots and intelligent agents. Companies should experiment with these technologies and develop their own expertise. Davenport describes the major AI technologies and explains how they are being used, reports on the AI work done by large commercial enterprises like Amazon and Google, and outlines strategies and steps to becoming a cognitive corporation. This book provides an invaluable guide to the real-world future of business AI. A book in the Management on the Cutting Edge series, published in cooperation with MIT Sloan Management Review. |
ai in business analytics: Artificial Intelligence and Legal Analytics Kevin D. Ashley, 2017-07-10 This book describes how text analytics and computational models of legal reasoning will improve legal IR and let computers help humans solve legal problems. |
ai in business analytics: Integration Challenges for Analytics, Business Intelligence, and Data Mining Azevedo, Ana, Santos, Manuel Filipe, 2020-12-11 As technology continues to advance, it is critical for businesses to implement systems that can support the transformation of data into information that is crucial for the success of the company. Without the integration of data (both structured and unstructured) mining in business intelligence systems, invaluable knowledge is lost. However, there are currently many different models and approaches that must be explored to determine the best method of integration. Integration Challenges for Analytics, Business Intelligence, and Data Mining is a relevant academic book that provides empirical research findings on increasing the understanding of using data mining in the context of business intelligence and analytics systems. Covering topics that include big data, artificial intelligence, and decision making, this book is an ideal reference source for professionals working in the areas of data mining, business intelligence, and analytics; data scientists; IT specialists; managers; researchers; academicians; practitioners; and graduate students. |
ai in business analytics: Artificial Intelligence in Finance Yves Hilpisch, 2020-10-14 The widespread adoption of AI and machine learning is revolutionizing many industries today. Once these technologies are combined with the programmatic availability of historical and real-time financial data, the financial industry will also change fundamentally. With this practical book, you'll learn how to use AI and machine learning to discover statistical inefficiencies in financial markets and exploit them through algorithmic trading. Author Yves Hilpisch shows practitioners, students, and academics in both finance and data science practical ways to apply machine learning and deep learning algorithms to finance. Thanks to lots of self-contained Python examples, you'll be able to replicate all results and figures presented in the book. In five parts, this guide helps you: Learn central notions and algorithms from AI, including recent breakthroughs on the way to artificial general intelligence (AGI) and superintelligence (SI) Understand why data-driven finance, AI, and machine learning will have a lasting impact on financial theory and practice Apply neural networks and reinforcement learning to discover statistical inefficiencies in financial markets Identify and exploit economic inefficiencies through backtesting and algorithmic trading--the automated execution of trading strategies Understand how AI will influence the competitive dynamics in the financial industry and what the potential emergence of a financial singularity might bring about |
ai in business analytics: Artificial Intelligence Design and Solution for Risk and Security Archie Addo, Srini Centhala, Muthu Shanmugam, 2020-03-13 Artificial Intelligence (AI) Design and Solutions for Risk and Security targets readers to understand, learn, define problems, and architect AI projects. Starting from current business architectures and business processes to futuristic architectures. Introduction to data analytics and life cycle includes data discovery, data preparation, data processing steps, model building, and operationalization are explained in detail. The authors examine the AI and ML algorithms in detail, which enables the readers to choose appropriate algorithms during designing solutions. Functional domains and industrial domains are also explained in detail. The takeaways are learning and applying designs and solutions to AI projects with risk and security implementation and knowledge about futuristic AI in five to ten years. |
ai in business analytics: The Economics of Artificial Intelligence Ajay Agrawal, Joshua Gans, Avi Goldfarb, Catherine Tucker, 2024-03-05 A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system. |
ai in business analytics: The AI Marketing Canvas Raj Venkatesan, Jim Lecinski, 2021-05-18 This book offers a direct, actionable plan CMOs can use to map out initiatives that are properly sequenced and designed for success—regardless of where their marketing organization is in the process. The authors pose the following critical questions to marketers: (1) How should modern marketers be thinking about artificial intelligence and machine learning? and (2) How should marketers be developing a strategy and plan to implement AI into their marketing toolkit? The opening chapters provide marketing leaders with an overview of what exactly AI is and how is it different than traditional computer science approaches. Venkatesan and Lecinski, then, propose a best-practice, five-stage framework for implementing what they term the AI Marketing Canvas. Their approach is based on research and interviews they conducted with leading marketers, and offers many tangible examples of what brands are doing at each stage of the AI Marketing Canvas. By way of guidance, Venkatesan and Lecinski provide examples of brands—including Google, Lyft, Ancestry.com, and Coca-Cola—that have successfully woven AI into their marketing strategies. The book concludes with a discussion of important implications for marketing leaders—for your team and culture. |
ai in business analytics: Enterprise Artificial Intelligence Transformation Rashed Haq, 2020-06-10 Enterprise Artificial Intelligence Transformation AI is everywhere. From doctor's offices to cars and even refrigerators, AI technology is quickly infiltrating our daily lives. AI has the ability to transform simple tasks into technological feats at a human level. This will change the world, plain and simple. That's why AI mastery is such a sought-after skill for tech professionals. Author Rashed Haq is a subject matter expert on AI, having developed AI and data science strategies, platforms, and applications for Publicis Sapient's clients for over 10 years. He shares that expertise in the new book, Enterprise Artificial Intelligence Transformation. The first of its kind, this book grants technology leaders the insight to create and scale their AI capabilities and bring their companies into the new generation of technology. As AI continues to grow into a necessary feature for many businesses, more and more leaders are interested in harnessing the technology within their own organizations. In this new book, leaders will learn to master AI fundamentals, grow their career opportunities, and gain confidence in machine learning. Enterprise Artificial Intelligence Transformation covers a wide range of topics, including: Real-world AI use cases and examples Machine learning, deep learning, and slimantic modeling Risk management of AI models AI strategies for development and expansion AI Center of Excellence creating and management If you're an industry, business, or technology professional that wants to attain the skills needed to grow your machine learning capabilities and effectively scale the work you're already doing, you'll find what you need in Enterprise Artificial Intelligence Transformation. |
ai in business analytics: Artificial Intelligence for Business Leaders Ajit Jha, 2020-07-23 ◆◆ Embrace artificial intelligence or be replaced by it. ◆◆ AI is a new electricity. Andrew Ng ✓Have you ever thought that if AI is the new electricity, why does it not quickly inspire Managers/Leaders/C-Suites? ✓If business leaders do not act, they must be prepared to lag behind competitors who adopt new technologies. ✓Managers/Leaders/C-Suites and others who are willing to feel the spark of AI, should learn and understand AI immediately to know what AI can do and what it cannot. ✓Did you know that AI is changing our world faster than we think? Artificial intelligence will affect all areas of life in ways we cannot even predict, whether we like it or not. According to research done by PricewaterhouseCoopers (PwC), by 2030, artificial intelligence can contribute up to US$15.7 trillion to the global economy, so the opportunities for implementing and learning AI are huge. ⚠ Companies that do not use AI will soon become obsolete. From making faster and better decisions to automating rote memorization to enabling robots to respond to emotions, artificial intelligence and machine learning have been reshaping business and society. ⚠ Not investing in the organizational and technical requirements of adopting AI may mean that they are far behind and unable to compete in the future. ✓ Business is changing. Will you adapt or fall behind? Accelerate and deepen your understanding of the themes that shape the company's future. ✓ This book is suitable for business executives, business managers, business leaders, senior managers, technical leaders, students, and many people who want to understand artificial intelligence. ✓ It will take you to learn the concepts of machine learning, artificial intelligence and deep learning, more and how to use them to influence your business. ✓✓ Even if you do not have technical knowledge, you will understand AI, ML and its implementation. ◆◆ Key features ◆◆ nbsp; ★ A must book for the business leader to understand AI and its application ★ Understand strategy behind AI implementation ★ Zero coding with simple explanation ★ A straightforward explanation for important algorithms like TensorFlow, NLP, K-Means, Support Vector Machine, Supervised Learning, Unsupervised Learning, Ensemble Techniques, Regression, Clustering, and many more ★★ Grab your copy of this book to build artificial intelligence for business and stand to the best of times! |
ai in business analytics: AI in Marketing, Sales and Service Peter Gentsch, 2018-10-22 AI and Algorithmics have already optimized and automated production and logistics processes. Now it is time to unleash AI on the administrative, planning and even creative procedures in marketing, sales and management. This book provides an easy-to-understand guide to assessing the value and potential of AI and Algorithmics. It systematically draws together the technologies and methods of AI with clear business scenarios on an entrepreneurial level. With interviews and case studies from those cutting edge businesses and executives who are already leading the way, this book shows you: how customer and market potential can be automatically identified and profiled; how media planning can be intelligently automated and optimized with AI and Big Data; how (chat)bots and digital assistants can make communication between companies and consumers more efficient and smarter; how you can optimize Customer Journeys based on Algorithmics and AI; and how to conduct market research in more efficient and smarter way. A decade from now, all businesses will be AI businesses – Gentsch shows you how to make sure yours makes that transition better than your competitors. |
ai in business analytics: Reimagining Businesses with AI Khaled Al Huraimel, Sudhi Sinha, 2020-09-22 Discover what AI can do for your business with this approachable and comprehensive resource Reimagining Businesses with AI acquaints readers with both the business challenges and opportunities presented by the rapid growth and progress of artificial intelligence. The accomplished authors and digital executives of the book provide you with a multi-industry approach to understanding the intersection of AI and business. The book walks you through the process of recognizing and capitalizing on AI’s potential for your own business. The authors describe: How to build a technological foundation that allows for the rapid implementation of artificial intelligence How to manage the disruptive nature of powerful technology while simultaneously harnessing its capabilities The ethical implications and security and privacy concerns raised by the spread of AI Perfect for business executives and managers who seek a jargon-free and approachable manual on how to implement artificial intelligence in everyday operations, Reimagining Businesses with AI also belongs on the bookshelves of anyone curious about the interaction between artificial intelligence and business. |
ai in business analytics: Artificial Intelligence for Business Jason L. Anderson, Jeffrey L. Coveyduc, 2020-04-09 Artificial Intelligence for Business: A Roadmap for Getting Started with AI will provide the reader with an easy to understand roadmap for how to take an organization through the adoption of AI technology. It will first help with the identification of which business problems and opportunities are right for AI and how to prioritize them to maximize the likelihood of success. Specific methodologies are introduced to help with finding critical training data within an organization and how to fill data gaps if they exist. With data in hand, a scoped prototype can be built to limit risk and provide tangible value to the organization as a whole to justify further investment. Finally, a production level AI system can be developed with best practices to ensure quality with not only the application code, but also the AI models. Finally, with this particular AI adoption journey at an end, the authors will show that there is additional value to be gained by iterating on this AI adoption lifecycle and improving other parts of the organization. |
ai in business analytics: Data Accelerator for AI and Analytics Simon Lorenz, Gero Schmidt, TJ Harris, Mike Knieriemen, Nils Haustein, Abhishek Dave, Venkateswara Puvvada, Christof Westhues, IBM Redbooks, 2021-01-20 This IBM® Redpaper publication focuses on data orchestration in enterprise data pipelines. It provides details about data orchestration and how to address typical challenges that customers face when dealing with large and ever-growing amounts of data for data analytics. While the amount of data increases steadily, artificial intelligence (AI) workloads must speed up to deliver insights and business value in a timely manner. This paper provides a solution that addresses these needs: Data Accelerator for AI and Analytics (DAAA). A proof of concept (PoC) is described in detail. This paper focuses on the functions that are provided by the Data Accelerator for AI and Analytics solution, which simplifies the daily work of data scientists and system administrators. This solution helps increase the efficiency of storage systems and data processing to obtain results faster while eliminating unnecessary data copies and associated data management. |
ai in business analytics: AI and Machine Learning for Coders Laurence Moroney, 2020-10-01 If you're looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney's extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build confidence while you learn key topics. You'll understand how to implement the most common scenarios in machine learning, such as computer vision, natural language processing (NLP), and sequence modeling for web, mobile, cloud, and embedded runtimes. Most books on machine learning begin with a daunting amount of advanced math. This guide is built on practical lessons that let you work directly with the code. You'll learn: How to build models with TensorFlow using skills that employers desire The basics of machine learning by working with code samples How to implement computer vision, including feature detection in images How to use NLP to tokenize and sequence words and sentences Methods for embedding models in Android and iOS How to serve models over the web and in the cloud with TensorFlow Serving |
OpenAI
May 21, 2025 · ChatGPT for business just got better—with connectors to internal tools, MCP support, record mode & SSO to Team, and flexible pricing for Enterprise. We believe our …
What is AI - DeepAI
What is AI, and how does it enable machines to perform tasks requiring human intelligence, like speech recognition and decision-making? AI learns and adapts through new data, integrating …
Artificial intelligence - Wikipedia
Artificial intelligence (AI) is the capability of computational systems to perform tasks typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, …
ISO - What is artificial intelligence (AI)?
AI spans a wide spectrum of capabilities, but essentially, it falls into two broad categories: weak AI and strong AI. Weak AI, often referred to as artificial narrow intelligence (ANI) or narrow AI, …
Artificial intelligence (AI) | Definition, Examples, Types ...
4 days ago · Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of …
Google AI - How we're making AI helpful for everyone
Discover how Google AI is committed to enriching knowledge, solving complex challenges and helping people grow by building useful AI tools and technologies.
What Is Artificial Intelligence? Definition, Uses, and Types
May 23, 2025 · Artificial intelligence (AI) is the theory and development of computer systems capable of performing tasks that historically required human intelligence, such as recognizing …
What is artificial intelligence (AI)? - IBM
Artificial intelligence (AI) is technology that enables computers and machines to simulate human learning, comprehension, problem solving, decision-making, creativity and autonomy.
What is Artificial Intelligence (AI)? - GeeksforGeeks
Apr 22, 2025 · Narrow AI (Weak AI): This type of AI is designed to perform a specific task or a narrow set of tasks, such as voice assistants or recommendation systems. It excels in one …
Machine learning and generative AI: What are they good for in ...
Jun 2, 2025 · What is generative AI? Generative AI is a newer type of machine learning that can create new content — including text, images, or videos — based on large datasets. Large …
Enhancing Business Intelligence: Harnessing Text Analytics, …
The integration of text analytics with Enterprise Resource Planning (ERP) systems heralds a new era of enhanced business intelligence capabilities.
Transforming Business Technology Landscape: Leveraging …
Transforming Business Technology Landscape SHOHONI MAHABUB 4033 Nanotechnology Perceptions 20 No.6 (2024) 4031-4042 The central theme of this research is exploring how AI …
UPDATED_Course Guide for Business Analytics Emphasis
Course Guide for Business Analytics Emphasis Business analytics and data are transforming modern firms. Every aspect of the firms is shifting toward ... The following courses provide …
Business Intelligence, Analytics, and Data Science
Chapter 3 Descriptive Analytics II: Business Intelligence and Data Warehousing 153 Chapter 4 Predictive Analytics I: Data Mining Process, Methods, and Algorithms 215 Chapter 5 Predictive …
Reviewing the role of AI and machine learning in supply …
This review underscores the transformative impact of AI and ML on supply chain analytics, emphasizing their potential to revolutionize traditional practices, enhance efficiency, and fortify …
Understanding AI business uses and strategies - KPMG
There’s a common misperception that generative AI is “AI 2.0” or that it has replaced more traditional AI models, but . they are really designed for two very different purposes. Put . …
ADVANCING DIGITAL AND ANALYTICS IN HEALTH CARE …
ANALYTICS IN HEALTH CARE Lessons for Payers, Providers & Services • Boosting Health Care Payer Performance with Advanced Analytics • Making Big Data Work: Health Care Payers and …
ACCELERATED DUAL-DEGREE Code Title Credits Core …
AI and Business Analytics program will be issued a Badge in Business Analytics using SAS. Students enter the program as first-year students and learn at an accelerated pace to earn a …
Current and Future Artificial Intelligence (AI) Curriculum in …
Using text mining analysis, we collected and analyzed AI courses from the top 46 business schools at both undergraduate and graduate levels, ranked by US News in 2020. The findings …
Analytics and AI: Accelerate your Data Driven Intelligence
business performance. Our Analytics and AI solutions are built with deep expertise and experience from across industry verticals –industrial, manufacturing, aerospace, medical, e …
Data Quality: Empowering Businesses with Analytics and AI: …
Dr. Southekal’s book Data Quality: Empowering Businesses with Analytics and AI is empowering business and data leaders and giving practical guidance on how to build good-quality data to …
The state of AI in 2022—and a half decade in review
This marks the fifth consecutive year we’ve conducted research globally on AI’s role in business, and we have seen shifts over this period. First, AI adoption has more than doubled.² In 2017, …
SAP Business Technology Platform
With SAP Business AI, we build a system of intelligence with three core principles: •Relevant The most relevant AI delivered in the context of your business processes •Reliable Uniquely …
Data AI: Business Analytics with Power BI
Data AI: Business Analytics with Power BI WorkshopPLUS It is a three day WorkshopPLUS course that provides attendees with deep knowledge of the latest Microsoft business analyst …
Connect data with business value in ways you never imagined.
Deloitte’s analytics, business, industry, and technology Value-added, leading Analytics + AI offerings,1 underpinned by our mature cloud infrastructure migration and transformation …
Business Tech-GB.3332: Introduction to AI and Its …
Tech-GB.3332: Introduction to AI and Its Applications in Business Professor Alex Tuzhilin Fall 2021 *** DRAFT SYLLABUS; SUBJECT TO CHANGE *** Course Description The field of …
Predictive analytics for market trends using AI: A study in …
a rapidly evolving business landscape. 3 AI Techniques in Predictive Analytics . Artificial Intelligence (AI) plays a pivotal role in predictive analytics, especially in understanding market …
The business value of AI How Microsoft is reinventing …
support infrastructure to support AI-powered processes. Even so, most transactions remained highly manual, with the HR team relying on a fragmented ecosystem of 108 ... HR Data …
AI: BUILT TO SCALE - Accenture
model linked to the company’s business objectives, supported by a larger, multi-dimensional team championed by the Chief AI, Data or Analytics Officer. However, the scaled AI is generally …
Data & Analytics Center of Excellence PLAYBOOK - U.S.
analytics techniques (e.g., data visualization, artificial intelligence (AI), operations research, etc.), and personnel capabilities Consider what types of roles would have access to which types of …
The power of an innovative data analytics platform
the potential of your business. By having a well-architected DAP, you can unlock real-time insights to make informed decisions and drive better results. To unleash your business’ potential with a …
ANALYTICS, DATA SCIENCE,
0 APPLICATION CASE 1.8 AI Increases Passengers’ Comfort and Security in Airports and Borders 90 The Three Flavors of AI Decisions 91 Autonomous AI 91 Societal Impacts 92 0 …
SAP Business AI
previous generation of AI and analytics Generative AI could add $2.6 to $4.4 trillion incremental value annually to the global Economy Widespread adoption, despite early days 33% …
Big Data, Analytics & Artificial Intelligence - GE Healthcare
Big Data, Analytics & Artificial Intelligence | 2 Table of Contents Preface 3 Introduction 4 Reimagining Medicine 5 Massive Amounts of Data Driving Digital Transformation 7 Veteran’s …
International Journal of Business Analytics Volume 11 • Issue …
International Journal of Business Analytics Volume 11 • Issue 1 4 lasted seven years, AI faced another setback in 1987 mainly due to the collapse of Lisp machines and the failure of the …
California Institute of Technology Machine Learning for …
Machine Learning for Advanced Analytics 2022–2023 Syllabus ===== Course Page: https://ctme.caltech.edu/ml-open Course Description Analytics is understanding the semantics …
Centre for Academic Courses
Centre for Academic Courses
An executive’s guide to AI - McKinsey & Company
AI is typically defined as the ability of a machine to perform cognitive functions we associate with human minds, such as perceiving, reasoning, learning, and problem solving. Examples of …
Applied Artificial Intelligence: A Handbook for Business …
popular AI applications for common business functions. Chapter 12 ... 16 describe how machine learning can dramatically improve business intelligence, analytics, and software development. …
BUSINESS INTELLIGENCE TRANSFORMATION …
The advent of AI and Data Analytics in Business Intelligence represents a paradigm shift in how businesses approach data analysis and decision-making. This integration has led to the .
Leading your organization to responsible AI - McKinsey
one AI capability in their business processes more than doubled, with nearly all companies using AI reporting achieving some level of value.¹ Not surprisingly, though, as AI supercharges …
Most of AI’s business uses will be in two areas - McKinsey
actual AI use cases across 19 industries and nine business functions, we’ve discovered an old adage proves most useful in answering the question of where to put AI to work: “Follow the …
Purdue_Applied Generative AI Specialization_Brochure
For admission to this Applied Generative AI Specialization program, candidates should: Our team of dedicated admissions counselors is prepared to address your questions or concerns about …
Advanced Analytics and AI Services
Jul 4, 2024 · analytics and AI service providers in 2024. Advanced BI and Reporting Modernization Services Data Modernization Services Data Science and AI Services Scope of …
AI-Powered Solutions for Enhancing National
1MBA in Business Analytics, Gannon University, Erie, PA, USA Email: Khalilor_rahman_88@yahoo.com 2 School of Business, International American University, Los …
Business Intelligence and SQL Analytics - Databricks
Data Science and AI / ML - Mosaic AI Business Intelligence and SQL Analytics 52 Data Management Collaboration Storage Data Engineering and Processing Automation and …
AI Adoption Study - PwC
and Analytics Survey (2022) 73% of data and analytics decision makers are building AI technologies and 74% seeing a positive impact from AI technologies in their organization. AI is …
BUSINESS ANALYTICS & INFORMATION MANAGEMENT
BUSINESS ANALYTICS & INFORMATION MANAGEMENT. PLACEMENT PROFILE. EMPLOYMENT AVERAGE SALARY TOP EMPLOYERS TOP POSITIONS. 100% …
The Role of AI and Business Analytics in Modern Business
3 Introduction Artificial intelligence (AI) and business analytics (BA) are rapidly changing how organizations make decisions, especially in competitive and complex environments (Ahmed et
The Impact of Artificial Intelligence on Business Operations
The text explores the role of AI in transforming decision-making processes, highlighting its versatility in optimizing operations across various industries. It covers topics such as …
MASTER OF BUSINESS ANALYTICS EMPLOYMENT …
The market for Master of Business Analytics graduates was strong in 2021, with 100% of the MBAn Class of 2021 seeking employment receiving offers ... C3.ai Citadel Clinc Comcast …
ONLINE MS IN BUSINESS ANALYTICS & APPLIED AI
Distinguish yourself with in-demand business analytics expertise—without pausing your career. As an Online MS in Business Analytics & Applied AI student at Simon Business School, you’ll …
McKinsey Analytics Global survey: The state of AI in 2020
embedded AI in at least one function or business unit. 2 The high-tech and telecom sectors include respondents who say they work in broadband communication, call centers, hardware, …
Modernizing analytics for the Generative AI era - DELOITTE
Upgrade your analytics stack to reduce technical debt and safeguard for the future Rapid developments in Generative AI (GenAI) have turbocharged the pace of change in data and …
AI: BUILT TO SCALE - Accenture
model linked to the company’s business objectives, supported by a larger, multi-dimensional team championed by the Chief AI, Data or Analytics Officer. However, the scaled AI is generally …
Deep Learning in Business Analytics: A Clash of Expectations
Business analytics constitutes a quite long chain of different analytics, which includes descriptive, predictive, and prescriptive analytics (Delen & Ram, 2018). ML operates mainly in the …
AI-Enhanced Data Analytics for Real-Time Business …
AI-enhanced data analytics, real-time business intelligence, machine learning algorithms, predictive analytics, anomaly detection, automated decision support, data integration, data
NOTES FROM THE AI FRONTIER INSIGHTS FROM …
the value of AI is not to be found in the models themselves, but in organizations’ abilities to harness them. Business leaders will need to prioritize and make careful choices about how, …
A Tutorial on Teaching Data Analytics with Generative AI
large language models (LLMs), business schools should transition from graphical-user-interface-based tools, such as Excel or SPSS, to text-based approaches, such as R or Python. 2.1.1. …
What AI can and can’t do (yet) for your business - McKinsey …
˜Adopting 1 or more AI technologies at scale or in business core; weighted by company size. ource: McKinsey Global Institute AI adoption and use survey; McKinsey Global Institute …