Advertisement
AI Digital Asset Management: Revolutionizing Content Organization and Workflow
Author: Dr. Anya Sharma, PhD in Computer Science & Engineering, specializing in AI and data management, with 10+ years of experience in enterprise software solutions.
Publisher: TechForward Insights – A leading technology research and analysis firm known for its unbiased and in-depth reports on emerging technologies. Their reputation is built on rigorous methodology and expert contributions.
Editor: David Chen, Senior Editor at TechForward Insights with 15 years of experience editing publications in the technology sector.
Keyword: AI Digital Asset Management
Summary: This analysis explores the burgeoning field of AI digital asset management (DAM), examining its impact on current trends in content organization, retrieval, and workflow optimization. We delve into the core functionalities of AI-powered DAM systems, analyze their benefits and limitations, and discuss the implications for various industries. The analysis concludes that AI DAM is transforming how businesses manage their digital assets, paving the way for greater efficiency, improved content discoverability, and enhanced creative workflows, although challenges related to data security and integration remain.
1. Introduction: The Rise of AI in Digital Asset Management
The explosion of digital content across various formats – images, videos, audio files, documents – has created a critical need for efficient and scalable digital asset management (DAM) systems. Traditional DAM solutions often struggle to keep pace with the sheer volume and variety of assets, leading to challenges in organization, search, and retrieval. This is where AI digital asset management steps in, offering intelligent solutions to overcome these limitations. AI-powered DAM systems leverage machine learning (ML) and artificial intelligence (AI) to automate many of the manual processes involved in managing digital assets, significantly improving efficiency and enabling better content utilization. The impact of AI digital asset management is already being felt across various industries, transforming how businesses handle their valuable digital resources.
2. Core Functionalities of AI Digital Asset Management Systems
AI digital asset management systems offer a range of advanced functionalities not found in traditional DAM solutions. Key features include:
Automated Metadata Tagging and Classification: AI algorithms can automatically analyze digital assets and assign relevant metadata tags, including keywords, descriptions, and categories. This eliminates the time-consuming manual tagging process, ensuring that assets are easily searchable and discoverable.
Intelligent Search and Retrieval: AI-powered search functionalities go beyond keyword matching, utilizing semantic understanding to return more relevant results. This allows users to find assets even if they don't know the exact keywords or filenames.
Automated Asset Organization: AI can automatically organize assets into folders and collections based on predefined rules or learned patterns. This reduces the manual effort required to maintain a structured asset library.
Content Recommendation and Insights: AI algorithms can analyze asset usage patterns and user behavior to provide recommendations for relevant assets and identify content gaps. This helps businesses optimize their content strategy and improve content discovery.
Duplicate Detection and Removal: AI can identify and remove duplicate assets, freeing up storage space and ensuring consistency across the asset library.
Automated Workflow and Approval Processes: AI can automate various steps in the asset workflow, such as approval processes, version control, and distribution.
3. Impact of AI Digital Asset Management on Current Trends
The adoption of AI digital asset management is driving several key trends:
Improved Content Discoverability: AI-powered search and metadata tagging significantly improve the ability to find and reuse assets, reducing time spent searching and increasing content efficiency.
Enhanced Collaboration: AI-powered DAM systems facilitate collaboration by providing a central repository for all digital assets and automating workflows.
Increased Efficiency and Productivity: Automation of manual tasks frees up time for creative professionals to focus on higher-value activities.
Data-Driven Content Strategy: AI provides insights into asset usage and performance, enabling data-driven decisions about content creation and distribution.
Scalability and Flexibility: AI DAM systems are designed to scale with the growing volume of digital assets, ensuring that businesses can manage their content effectively regardless of size.
Enhanced Brand Consistency: Automated metadata tagging and approval processes ensure consistency in brand messaging and visual identity across all channels.
4. Challenges and Limitations of AI Digital Asset Management
Despite its numerous benefits, AI digital asset management also faces some challenges:
Data Security and Privacy: Protecting sensitive data within the DAM system is paramount. Robust security measures are essential to prevent unauthorized access and data breaches.
Integration with Existing Systems: Integrating AI DAM with existing enterprise systems and workflows can be complex and require significant investment.
Cost of Implementation: AI DAM solutions can be expensive, particularly for smaller businesses.
Accuracy of AI Algorithms: The accuracy of AI algorithms depends on the quality of the data used to train them. Inaccurate or biased data can lead to inaccurate tagging and search results.
Lack of Skilled Personnel: Managing and maintaining AI DAM systems requires specialized skills and expertise, which can be difficult to find.
5. Future Trends in AI Digital Asset Management
The future of AI digital asset management is promising, with several exciting trends on the horizon:
Increased Use of Generative AI: Generative AI models can be used to create new digital assets, such as images and videos, based on user input or existing content.
Improved Personalization: AI can personalize the user experience within the DAM system, providing tailored recommendations and search results.
Enhanced Integration with Other Technologies: AI DAM systems will be increasingly integrated with other technologies, such as CRM and marketing automation platforms.
Greater Focus on Sustainability: AI can help optimize asset storage and usage, reducing the environmental impact of digital content.
6. Conclusion
AI digital asset management is transforming how businesses manage and utilize their digital assets. By automating tedious tasks, improving content discoverability, and providing valuable insights, AI DAM systems offer significant benefits for organizations of all sizes. While challenges remain, the ongoing advancements in AI and machine learning are poised to further enhance the capabilities of these systems, making them an increasingly essential tool for managing the ever-growing volume of digital content. The strategic adoption of AI digital asset management is no longer a luxury; it's becoming a necessity for businesses striving for efficiency, innovation, and competitive advantage.
FAQs:
1. What is the difference between traditional DAM and AI DAM? Traditional DAM systems rely on manual tagging and keyword-based search, while AI DAM utilizes machine learning to automate these processes and improve search accuracy.
2. How secure is AI Digital Asset Management? Security is paramount. Robust systems employ encryption, access controls, and regular security audits to protect sensitive data.
3. What are the costs associated with implementing AI DAM? Costs vary depending on the size and complexity of the system, as well as the vendor. Expect costs related to software licensing, implementation, training, and ongoing maintenance.
4. Can AI DAM handle all types of digital assets? Yes, modern AI DAM systems are designed to handle a wide range of asset types, including images, videos, audio files, documents, and 3D models.
5. How long does it take to implement AI DAM? Implementation time depends on factors like the size of the asset library, the complexity of integration with existing systems, and the level of customization required.
6. What are the key metrics for measuring the success of AI DAM implementation? Key metrics include improved asset discoverability, reduced time spent searching, increased content reuse, and enhanced collaboration.
7. What are the risks associated with AI DAM? Risks include data security breaches, inaccurate AI-generated metadata, and integration challenges with existing systems.
8. How can I choose the right AI DAM solution for my business? Consider factors like scalability, integration capabilities, security features, pricing, and vendor support.
9. What is the future of AI in Digital Asset Management? The future involves greater integration with other technologies, improved personalization, and the use of generative AI for creating new assets.
Related Articles:
1. "The Business Case for AI-Powered Digital Asset Management": This article explores the financial benefits of AI DAM, including ROI calculations and cost-saving strategies.
2. "AI and the Future of Creative Workflows: The Role of Digital Asset Management": This focuses on how AI DAM enhances creativity and collaboration in design and marketing teams.
3. "Top 10 AI-Powered DAM Features to Look for in 2024": A comparative analysis of leading AI DAM vendors and their key features.
4. "Best Practices for Implementing AI Digital Asset Management": A guide to successful implementation, including planning, integration, and training.
5. "Security Considerations for AI Digital Asset Management Systems": A detailed look at security protocols and best practices for safeguarding sensitive assets.
6. "The Impact of AI on Content Strategy and Digital Asset Management": This article examines how AI-driven insights can inform content strategy and improve content performance.
7. "AI-Powered Metadata Enrichment: Transforming Digital Asset Discoverability": A deep dive into the automation of metadata tagging and its benefits.
8. "Case Study: How [Company X] Used AI DAM to Improve Efficiency and Productivity": A real-world example showcasing the benefits of AI DAM implementation.
9. "Overcoming Challenges in AI Digital Asset Management Implementation": This article addresses common hurdles and provides solutions for successful adoption.
| ai digital asset management: Artificial Intelligence in Asset Management Söhnke M. Bartram, Jürgen Branke, Mehrshad Motahari, 2020-08-28 Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and return forecasts and more complex constraints. Trading algorithms use AI to devise novel trading signals and execute trades with lower transaction costs. AI also improves risk modeling and forecasting by generating insights from new data sources. Finally, robo-advisors owe a large part of their success to AI techniques. Yet the use of AI can also create new risks and challenges, such as those resulting from model opacity, complexity, and reliance on data integrity. |
| ai digital asset management: Metadata Matters John Horodyski, 2022-04-03 In what is certain to be a seminal work on metadata, John Horodyski masterfully affirms the value of metadata while providing practical examples of its role in our personal and professional lives. He does more than tell us that metadata matters—he vividly illustrates why it matters. —Patricia C. Franks, PhD, CA, CRM, IGP, CIGO, FAI, President, NAGARA, Professor Emerita, San José State University, USA If data is the language upon which our modern society will be built, then metadata will be its grammar, the construction of its meaning, the building for its content, and the ability to understand what data can be for us all. We are just starting to bring change into the management of the data that connects our experiences. Metadata Matters explains how metadata is the foundation of digital strategy. If digital assets are to be discovered, they want to be found. The path to good metadata design begins with the realization that digital assets need to be identified, organized, and made available for discovery. This book explains how metadata will help ensure that an organization is building the right system for the right users at the right time. Metadata matters and is the best chance for a return on investment on digital assets and is also a line of defense against lost opportunities. It matters to the digital experience of users. It helps organizations ensure that users can identify, discover, and experience their brands in the ways organizations intend. It is a necessary defense, which this book shows how to build. |
| ai digital asset management: Dam Survival Guide David Diamond, 2012-11-04 What Digital Asset Management Industry Pros say about DAM Survival Guide: If you are investing in DAM books to learn more about the subject, I can recommend this one. - Naresh Sarwan, Senior Editor, DigitalAssetManagementNews.org After you've read DAM Survival Guide, when you negotiate with a DAM vendor or try to evaluate the value of a system for your business, you won't have many blanks left for a vendor to fill in with marketing babble. It therefore is a book I warmly recommend. - Erik Vlietinck, Principle, IT Enquirer From newbies to experienced digital asset managers, DAM Survival Guide provides enough information that you can access what you need when you need it. - Marisa Peacock, Journalist, CMS Wire Digital Asset Management Vendors, Integrators, Analyst and Consultants be warned the DAM Survival Guide is packed full of insights, strategies and common sense guides for making DAM work for the end user. David Diamond, a seasoned DAM professional, shares his knowledge using wit, analogy, metaphor that cleaves the real meat on the bones of complexity that is Digital Asset Management. David nails it on every level: technology, human and insights. I would not hesitate in recommending DAM Survival Guide to anyone on or starting their DAM Journey. - Mark Davey, Founder, DAM Foundation _______________ ABOUT THE BOOK DAM Survival Guide is a digital asset management book that explains everything you need to know to design, plan, deploy, promote and maintain a successful DAM initiative at your organization. Written by a recognized DAM industry export in a friendly, easy-to-follow style, DAM Survival Guide is a must-have resource for those new to DAM, and it's great for those looking to increase their DAM knowledge too. DAM Survival Guide is everything you need to know about DAM in one book. Starting with an overview of what digital asset management is and isn't (including a section on why you might not need DAM at all), the book goes on to offer a detailed discussion of everything that's important for you to know before you get too far with your DAM planning: Learn the benefits of wrapping DAM into a corporate initiate you can better manage Know how to find and recruit others at your organization who can become great allies See how you can benefit from reliable professional help (cheap or even free!), so you can avoiding expensive time-wasters Fully understand the needs of your organization, so that you can exceed expectations Start thinking about DAM software at the right time, so you can avoid costly purchase mistakes Discover tricks to determine which DAM vendors are most favored by customers, most progressive, and most likely to stay in business Explore elements of human psychology that can help you overcome change-resistance and increase buy-in Including approximately 56,000 words, this book, first published in June, 2012, is packed with useful information the author, David Diamond, has acquired during his 12+ years as a professional in the Digital Asset Management industry. Note: The Digital Asset Management Survival Guide mentions no DAM software solutions or vendors by name. The book's contents are unbiased and applicable no matter which DAM solution you determine to be right for you. |
| ai digital asset management: Digital Asset Management Elizabeth Keathley, 2014-03-31 Digital Asset Management: Content Architectures, Project Management, and Creating Order out of Media Chaos is for those who are planning a digital asset management system or interested in becoming digital asset managers. This book explains both the purpose of digital asset management systems and why an organization might need one. The text then walks readers step-by-step through the concerns involved in selecting, staffing, and maintaining a DAM. This book is dedicated to providing you with a solid base in the common concerns, both legal and technical, in launching a complex DAM capable of providing visual search results and workflow options. Containing sample job models, case studies, return on investment models, and quotes from many top digital asset managers, this book provides a detailed resource for the vocabulary and procedures associated with digital asset management. It can even serve as a field guide for system and implementation requirements you may need to consider. This book is not dedicated to the purchase or launch of a DAM; instead it is filled with the information you need in order to examine digital asset management and the challenges presented by the management of visual assets, user rights, and branded materials. It will guide you through justifying the cost for deploying a DAM and how to plan for growth of the system in the future. This book provides the most useful information to those who find themselves in the bewildering position of formulating access control lists, auditing metadata, and consolidating information silos into a very new sort of workplace management tool – the DAM. The author, Elizabeth Ferguson Keathley, is a board member of the DAM Foundation and has chaired both the Human Resources and Education committees. Currently Elizabeth is working with the University of British Columbia and the DAM Foundation to establish the first official certificate program for Digital Asset Managers. She has written, taught, and been actively a part of conferences related to the arrangement, description, preservation and access of information for over ten years. Her ongoing exploration of digital asset management and its relationship to user needs can be followed at her homepage for Atlanta Metadata Authority : atlantametadata.com. |
| ai digital asset management: The AI Book Ivana Bartoletti, Anne Leslie, Shân M. Millie, 2020-06-29 Written by prominent thought leaders in the global fintech space, The AI Book aggregates diverse expertise into a single, informative volume and explains what artifical intelligence really means and how it can be used across financial services today. Key industry developments are explained in detail, and critical insights from cutting-edge practitioners offer first-hand information and lessons learned. Coverage includes: · Understanding the AI Portfolio: from machine learning to chatbots, to natural language processing (NLP); a deep dive into the Machine Intelligence Landscape; essentials on core technologies, rethinking enterprise, rethinking industries, rethinking humans; quantum computing and next-generation AI · AI experimentation and embedded usage, and the change in business model, value proposition, organisation, customer and co-worker experiences in today’s Financial Services Industry · The future state of financial services and capital markets – what’s next for the real-world implementation of AITech? · The innovating customer – users are not waiting for the financial services industry to work out how AI can re-shape their sector, profitability and competitiveness · Boardroom issues created and magnified by AI trends, including conduct, regulation & oversight in an algo-driven world, cybersecurity, diversity & inclusion, data privacy, the ‘unbundled corporation’ & the future of work, social responsibility, sustainability, and the new leadership imperatives · Ethical considerations of deploying Al solutions and why explainable Al is so important |
| ai digital asset management: The DAM Book Peter Krogh, 2009-04-27 One of the main concerns for digital photographers today is asset management: how to file, find, protect, and re-use their photos. The best solutions can be found in The DAM Book, our bestselling guide to managing digital images efficiently and effectively. Anyone who shoots, scans, or stores digital photographs is practicing digital asset management (DAM), but few people do it in a way that makes sense. In this second edition, photographer Peter Krogh -- the leading expert on DAM -- provides new tools and techniques to help professionals, amateurs, and students: Understand the image file lifecycle: from shooting to editing, output, and permanent storage Learn new ways to use metadata and key words to track photo files Create a digital archive and name files clearly Determine a strategy for backing up and validating image data Learn a catalog workflow strategy, using Adobe Bridge, Camera Raw, Adobe Lightroom, Microsoft Expression Media, and Photoshop CS4 together Migrate images from one file format to another, from one storage medium to another, and from film to digital Learn how to copyright images To identify and protect your images in the marketplace, having a solid asset management system is essential. The DAM Book offers the best approach. |
| ai digital asset management: Artificial Intelligence for Asset Management and Investment Al Naqvi, 2021-02-09 Make AI technology the backbone of your organization to compete in the Fintech era The rise of artificial intelligence is nothing short of a technological revolution. AI is poised to completely transform asset management and investment banking, yet its current application within the financial sector is limited and fragmented. Existing AI implementations tend to solve very narrow business issues, rather than serving as a powerful tech framework for next-generation finance. Artificial Intelligence for Asset Management and Investment provides a strategic viewpoint on how AI can be comprehensively integrated within investment finance, leading to evolved performance in compliance, management, customer service, and beyond. No other book on the market takes such a wide-ranging approach to using AI in asset management. With this guide, you’ll be able to build an asset management firm from the ground up—or revolutionize your existing firm—using artificial intelligence as the cornerstone and foundation. This is a must, because AI is quickly growing to be the single competitive factor for financial firms. With better AI comes better results. If you aren’t integrating AI in the strategic DNA of your firm, you’re at risk of being left behind. See how artificial intelligence can form the cornerstone of an integrated, strategic asset management framework Learn how to build AI into your organization to remain competitive in the world of Fintech Go beyond siloed AI implementations to reap even greater benefits Understand and overcome the governance and leadership challenges inherent in AI strategy Until now, it has been prohibitively difficult to map the high-tech world of AI onto complex and ever-changing financial markets. Artificial Intelligence for Asset Management and Investment makes this difficulty a thing of the past, providing you with a professional and accessible framework for setting up and running artificial intelligence in your financial operations. |
| ai digital asset management: Intelligent Data Driven Techniques for Security of Digital Assets Arun Kumar Rana, Sumit Kumar Rana, Ritu Dewan, Vishnu Sharma, 2025-03-06 The book covers the role of emerging technologies such as blockchain technology, machine learning, IoT, cryptography etc. in digital asset management. It further discusses the digital asset management applications in different domains like healthcare, travel industry, image processing and our daily life activities to maintain privacy and confidentiality. This book: Discusses techniques for securing and protecting digital assets in collaborative environments, where multiple organizations need access to the same resources. Explores how artificial intelligence can be used to automate the management of digital assets, and how it can be used to improve security and privacy. Explains the role of emerging technology like blockchain technology for transforming the conventional business models. Highlights the importance of machine learning technique in maintain the privacy and security of data. Covers the encryption and decryption techniques, their advantages and role in improving the privacy of data. The text is primarily written for senior undergraduate, graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, computer science and engineering, information technology, and business management. |
| ai digital asset management: Machine Learning for Asset Management Emmanuel Jurczenko, 2020-10-06 This new edited volume consists of a collection of original articles written by leading financial economists and industry experts in the area of machine learning for asset management. The chapters introduce the reader to some of the latest research developments in the area of equity, multi-asset and factor investing. Each chapter deals with new methods for return and risk forecasting, stock selection, portfolio construction, performance attribution and transaction costs modeling. This volume will be of great help to portfolio managers, asset owners and consultants, as well as academics and students who want to improve their knowledge of machine learning in asset management. |
| ai digital asset management: Herding Tigers Todd Henry, 2018-01-16 A practical handbook for every manager charged with leading teams to creative brilliance, from the author of The Accidental Creative and Die Empty. Doing the work and leading the work are very different things. When you make the transition from maker to manager, you give ownership of projects to your team even though you could do them yourself better and faster. You're juggling expectations from your manager, who wants consistent, predictable output from an inherently unpredictable creative process. And you're managing the pushback from your team of brilliant, headstrong, and possibly overqualified creatives. Leading talented, creative people requires a different skill set than the one many management books offer. As a consultant to creative companies, Todd Henry knows firsthand what prevents creative leaders from guiding their teams to success, and in Herding Tigers he provides a bold new blueprint to help you be the leader your team needs. Learn to lead by influence instead of control. Discover how to create a stable culture that empowers your team to take bold creative risks. And learn how to fight to protect the time, energy, and resources they need to do their best work. Full of stories and practical advice, Herding Tigers will give you the confidence and the skills to foster an environment where clients, management, and employees have a product they can be proud of and a process that works. |
| ai digital asset management: Digital Asset Management David Austerberry, 2012-10-12 Content and media asset management systems are core back office applications of the modern day broadcaster, yet there is little information available on the control and management of these systems and how content can be delivered over a variety of different channels: television, iTV, internet, webcasting, mobile phones and wireless PDAs. This book explains the potential for applying asset management systems to content creation models for distribution over a variety of outlets and the benefits gained from increased efficiency and lowering of costs. Taking an unbiased view and focusing on core principles rather than specific systems, David Austerberry presents the business case for digital asset management systems, demystifies some assumptions regarding the technology and provides a thorough introduction to the system components required, such as indexing, searching, middleware, database and rightsmanagement and web portals. |
| ai digital asset management: Digital Asset Management Unknown Author, 2012-10-12 Content and media asset management systems are core back office applications of the modern day broadcaster, yet there is little information available on the control and management of these systems and how content can be delivered over a variety of different channels: television, iTV, internet, webcasting, mobile phones and wireless PDAs. This book explains the potential for applying asset management systems to content creation models for distribution over a variety of outlets and the benefits gained from increased efficiency and lowering of costs. Taking an unbiased view and focusing on core principles rather than specific systems, David Austerberry presents the business case for digital asset management systems, demystifies some assumptions regarding the technology and provides a thorough introduction to the system components required, such as indexing, searching, middleware, database and rightsmanagement and web portals. |
| ai digital asset management: Digital Asset Management David Austerberry, 2012-07-26 The second edition focuses on the media and entertainment sector (M&E), with more information relevant to encompass broadcasters migration to file-based production. New technology and new products are also included and there is more detail on systems integration and product examples, plus extra case studies. New content includes: - Storage management where several products have been designed for the special needs of the media business. - XML and web services. - New case studies. |
| ai digital asset management: Digital and Marketing Asset Management Theresa Regli, 2016-08-02 The digital world is transitioning from text to media: photos, audio files, video clips, animations, games, and more. Enterprises of all kinds struggle with how to manage those media assets. Digital professionals who want to master the life cycles behind creating, storing, and reusing media need the inside scoop on how digital and media asset management technology really works. |
| ai digital asset management: Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance El Bachir Boukherouaa, Mr. Ghiath Shabsigh, Khaled AlAjmi, Jose Deodoro, Aquiles Farias, Ebru S Iskender, Mr. Alin T Mirestean, Rangachary Ravikumar, 2021-10-22 This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight. |
| ai digital asset management: AI, Data and Private Law Gary Chan Kok Yew, Man Yip, 2021-09-23 This book examines the interconnections between artificial intelligence, data governance and private law rules with a comparative focus on selected jurisdictions in the Asia-Pacific region. The chapters discuss the myriad challenges of translating and adapting theory, doctrines and concepts to practice in the Asia-Pacific region given their differing circumstances, challenges and national interests. The contributors are legal experts from the UK, Israel, Korea, and Singapore with extensive academic and practical experience. The essays in this collection cover a wide range of topics, including data protection and governance, data trusts, information fiduciaries, medical AI, the regulation of autonomous vehicles, the use of blockchain technology in land administration, the regulation of digital assets and contract formation issues arising from AI applications. The book will be of interest to members of the judiciary, policy makers and academics who specialise in AI, data governance and/or private law or who work at the intersection of these three areas, as well as legal technologists and practising lawyers in the Asia-Pacific, the UK and the US. |
| ai digital asset management: 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 digital asset management: IBM Maximo Asset Management. The Consultant's Guide: Second Edition Robert Zientara, 2021-05-09 This book was written by a Maximo consultant for Maximo functional consultants to help them lead implementation projects better and faster. This is already the second edition of this book, revised and extended. The book covers the topic of how to implement IBM Maximo Asset Management efficiently and bring value to customers. The book begins by describing how to prepare the project and run the workshops. There is an explanation of how to design the system and what deliverables will be. The following chapters focus on the project organization to make it productive. This part of the book can be helpful also for managers of Maximo implementation teams. The second part of the book describes Maximo applications, their interactions, and processes. You will also find here a lot of configuration examples and sample content of the project deliverables. See what my readers have to say… “…I must thank you for your contribution towards the industry and how much it can help young and upcoming business consultants like me in getting things right. Knowledge is invaluable. Thanks for your time in creating a medium to share it globally…” —Hashmeet “…The book has immensely helped me in planning the activities and deploying the project….” —Kushal “…Very well written for a consultant to understand how to approach projects. Utilize many of your talking points with my clients. Great work!...” —John |
| ai digital asset management: Trustworthy AI Beena Ammanath, 2022-03-15 An essential resource on artificial intelligence ethics for business leaders In Trustworthy AI, award-winning executive Beena Ammanath offers a practical approach for enterprise leaders to manage business risk in a world where AI is everywhere by understanding the qualities of trustworthy AI and the essential considerations for its ethical use within the organization and in the marketplace. The author draws from her extensive experience across different industries and sectors in data, analytics and AI, the latest research and case studies, and the pressing questions and concerns business leaders have about the ethics of AI. Filled with deep insights and actionable steps for enabling trust across the entire AI lifecycle, the book presents: In-depth investigations of the key characteristics of trustworthy AI, including transparency, fairness, reliability, privacy, safety, robustness, and more A close look at the potential pitfalls, challenges, and stakeholder concerns that impact trust in AI application Best practices, mechanisms, and governance considerations for embedding AI ethics in business processes and decision making Written to inform executives, managers, and other business leaders, Trustworthy AI breaks new ground as an essential resource for all organizations using AI. |
| ai digital asset management: Machine Learning for Asset Managers Marcos M. López de Prado, 2020-04-22 Successful investment strategies are specific implementations of general theories. An investment strategy that lacks a theoretical justification is likely to be false. Hence, an asset manager should concentrate her efforts on developing a theory rather than on backtesting potential trading rules. The purpose of this Element is to introduce machine learning (ML) tools that can help asset managers discover economic and financial theories. ML is not a black box, and it does not necessarily overfit. ML tools complement rather than replace the classical statistical methods. Some of ML's strengths include (1) a focus on out-of-sample predictability over variance adjudication; (2) the use of computational methods to avoid relying on (potentially unrealistic) assumptions; (3) the ability to learn complex specifications, including nonlinear, hierarchical, and noncontinuous interaction effects in a high-dimensional space; and (4) the ability to disentangle the variable search from the specification search, robust to multicollinearity and other substitution effects. |
| ai digital asset management: 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 digital asset management: Digital Built Asset Management Qiuchen Lu, Michael Pitt, 2024-10-03 This insightful book presents a comprehensive understanding of the new technologies impacting the digital era of built asset and facility management. Informative and accessible, it illustrates how the concepts, principles, strategies and applications of digital built asset management can be improved and implemented in real-life practice. |
| ai digital asset management: AI Factory Ramin Karim, Diego Galar, Uday Kumar, 2023-05-24 This book provides insights into how to approach and utilise data science tools, technologies, and methodologies related to artificial intelligence (AI) in industrial contexts. It explains the essence of distributed computing and AI technologies and their interconnections. It includes descriptions of various technology and methodology approaches and their purpose and benefits when developing AI solutions in industrial contexts. In addition, this book summarises experiences from AI technology deployment projects from several industrial sectors. Features: Presents a compendium of methodologies and technologies in industrial AI and digitalisation. Illustrates the sensor-to-actuation approach showing the complete cycle, which defines and differentiates AI and digitalisation. Covers a broad range of academic and industrial issues within the field of asset management. Discusses the impact of Industry 4.0 in other sectors. Includes a dedicated chapter on real-time case studies. This book is aimed at researchers and professionals in industrial and software engineering, network security, AI and machine learning (ML), engineering managers, operational and maintenance specialists, asset managers, and digital and AI manufacturing specialists. |
| ai digital asset management: Internet of Things and Big Data Analytics-Based Manufacturing Arun Kumar Rana, Sudeshna Chakraborty, Pallavi Goel, Sumit Kumar Rana, Ahmed A. Elngar, 2024-10-17 By enabling the conversion of traditional manufacturing systems into contemporary digitalized ones, Internet of Things (IoT) adoption in manufacturing creates huge economic prospects through reshaping industries. Modern businesses can more readily implement new data-driven strategies and deal with the pressure of international competition thanks to Industrial IoT. But as the use of IoT grows, the amount of created data rises, turning industrial data into Industrial Big Data. Internet of Things and Big Data Analytics-Based Manufacturing shows how Industrial Big Data can be produced as a result of IoT usage in manufacturing, considering sensing systems and mobile devices. Different IoT applications that have been developed are demonstrated and it is shown how genuine industrial data can be produced, leading to Industrial Big Data. This book is organized into four sections discussing IoT and technology, the future of Big Data, algorithms, and case studies demonstrating the use of IoT and Big Data in a variety of industries, including automation, industrial manufacturing, and healthcare. This reference title brings all related technologies into a single source so that researchers, undergraduate and postgraduate students, academicians, and those in the industry can easily understand the topic and further their knowledge. |
| ai digital asset management: Infonomics Douglas B. Laney, 2017-09-05 Many senior executives talk about information as one of their most important assets, but few behave as if it is. They report to the board on the health of their workforce, their financials, their customers, and their partnerships, but rarely the health of their information assets. Corporations typically exhibit greater discipline in tracking and accounting for their office furniture than their data. Infonomics is the theory, study, and discipline of asserting economic significance to information. It strives to apply both economic and asset management principles and practices to the valuation, handling, and deployment of information assets. This book specifically shows: CEOs and business leaders how to more fully wield information as a corporate asset CIOs how to improve the flow and accessibility of information CFOs how to help their organizations measure the actual and latent value in their information assets. More directly, this book is for the burgeoning force of chief data officers (CDOs) and other information and analytics leaders in their valiant struggle to help their organizations become more infosavvy. Author Douglas Laney has spent years researching and developing Infonomics and advising organizations on the infinite opportunities to monetize, manage, and measure information. This book delivers a set of new ideas, frameworks, evidence, and even approaches adapted from other disciplines on how to administer, wield, and understand the value of information. Infonomics can help organizations not only to better develop, sell, and market their offerings, but to transform their organizations altogether. Doug Laney masterfully weaves together a collection of great examples with a solid framework to guide readers on how to gain competitive advantage through what he labels the unruly asset – data. The framework is comprehensive, the advice practical and the success stories global and across industries and applications. Liz Rowe, Chief Data Officer, State of New Jersey A must read for anybody who wants to survive in a data centric world. Shaun Adams, Head of Data Science, Betterbathrooms.com Phenomenal! An absolute must read for data practitioners, business leaders and technology strategists. Doug's lucid style has a set a new standard in providing intelligible material in the field of information economics. His passion and knowledge on the subject exudes thru his literature and inspires individuals like me. Ruchi Rajasekhar, Principal Data Architect, MISO Energy I highly recommend Infonomics to all aspiring analytics leaders. Doug Laney’s work gives readers a deeper understanding of how and why information should be monetized and managed as an enterprise asset. Laney’s assertion that accounting should recognize information as a capital asset is quite convincing and one I agree with. Infonomics enjoyably echoes that sentiment! Matt Green, independent business analytics consultant, Atlanta area If you care about the digital economy, and you should, read this book. Tanya Shuckhart, Analyst Relations Lead, IRI Worldwide |
| ai digital asset management: Content Strategy for the Web Kristina Halvorson, Melissa Rach, 2012-02-28 FROM CONSTANT CRISIS TO SUSTAINABLE SUCCESS BETTER CONTENT MEANS BETTER BUSINESS. Your content is a mess: the website redesigns didn’t help, and the new CMS just made things worse. Or, maybe your content is full of potential: you know new revenue and cost-savings opportunities exist, but you’re not sure where to start. How can you realize the value of content while planning for its long-term success? For organizations all over the world, Content Strategy for the Web is the go-to content strategy handbook. Read it to: Understand content strategy and its business value Discover the processes and people behind a successful content strategy Make smarter, achievable decisions about what content to create and how Find out how to build a business case for content strategy With all-new chapters, updated material, case studies, and more, the second edition of Content Strategy for the Web is an essential guide for anyone who works with content. |
| ai digital asset management: Tika in Action Jukka L. Zitting, Chris Mattmann, 2011-11-30 Summary Tika in Action is a hands-on guide to content mining with Apache Tika. The book's many examples and case studies offer real-world experience from domains ranging from search engines to digital asset management and scientific data processing. About the Technology Tika is an Apache toolkit that has built into it everything you and your app need to know about file formats. Using Tika, your applications can discover and extract content from digital documents in almost any format, including exotic ones. About this Book Tika in Action is the ultimate guide to content mining using Apache Tika. You'll learn how to pull usable information from otherwise inaccessible sources, including internet media and file archives. This example-rich book teaches you to build and extend applications based on real-world experience with search engines, digital asset management, and scientific data processing. In addition to architectural overviews, you'll find detailed chapters on features like metadata extraction, automatic language detection, and custom parser development. This book is written for developers who are new to both Scala and Lift and covers just enough Scala to get you started. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. What's Inside Crack MS Word, PDF, HTML, and ZIP Integrate with search engines, CMS, and other data sources Learn through experimentation Many examples This book requires no previous knowledge of Tika or text mining techniques. It assumes a working knowledge of Java. ========================================== Table of Contents PART 1 GETTING STARTED The case for the digital Babel fish Getting started with Tika The information landscape PART 2 TIKA IN DETAIL Document type detection Content extraction Understanding metadata Language detection What's in a file? PART 3 INTEGRATION AND ADVANCED USE The big picture Tika and the Lucene search stack Extending Tika PART 4 CASE STUDIES Powering NASA science data systems Content management with Apache Jackrabbit Curating cancer research data with Tika The classic search engine example |
| ai digital asset management: Disrupting Finance Theo Lynn, John G. Mooney, Pierangelo Rosati, Mark Cummins, 2018-12-06 This open access Pivot demonstrates how a variety of technologies act as innovation catalysts within the banking and financial services sector. Traditional banks and financial services are under increasing competition from global IT companies such as Google, Apple, Amazon and PayPal whilst facing pressure from investors to reduce costs, increase agility and improve customer retention. Technologies such as blockchain, cloud computing, mobile technologies, big data analytics and social media therefore have perhaps more potential in this industry and area of business than any other. This book defines a fintech ecosystem for the 21st century, providing a state-of-the art review of current literature, suggesting avenues for new research and offering perspectives from business, technology and industry. |
| ai digital asset management: Assetization Kean Birch, Fabian Muniesa, 2020-07-14 How the asset—anything that can be controlled, traded, and capitalized as a revenue stream—has become the primary basis of technoscientific capitalism. In this book, scholars from a range of disciplines argue that the asset—meaning anything that can be controlled, traded, and capitalized as a revenue stream—has become the primary basis of technoscientific capitalism. An asset can be an object or an experience, a sum of money or a life form, a patent or a bodily function. A process of assetization prevails, imposing investment and return as the key rationale, and overtaking commodification and its speculative logic. Although assets can be bought and sold, the point is to get a durable economic rent from them rather than make a killing on the market. Assetization examines how assets are constructed and how a variety of things can be turned into assets, analyzing the interests, activities, skills, organizations, and relations entangled in this process. The contributors consider the assetization of knowledge, including patents, personal data, and biomedical innovation; of infrastructure, including railways and energy; of nature, including mineral deposits, agricultural seeds, and “natural capital”; and of publics, including such public goods as higher education and “monetizable social ills.” Taken together, the chapters show the usefulness of assetization as an analytical tool and as an element in the critique of capitalism. Contributors Thomas Beauvisage, Kean Birch, Veit Braun, Natalia Buier, Béatrice Cointe, Paul Robert Gilbert, Hyo Yoon Kang, Les Levidow, Kevin Mellet, Sveta Milyaeva, Fabian Muniesa, Alain Nadaï, Daniel Neyland, Victor Roy, James W. Williams |
| ai digital asset management: Advances in Asset Management and Condition Monitoring Andrew Ball, Len Gelman, B. K. N. Rao, 2020-08-27 This book gathers select contributions from the 32nd International Congress and Exhibition on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2019), held at the University of Huddersfield, UK in September 2019, and jointly organized by the University of Huddersfield and COMADEM International. The aim of the Congress was to promote awareness of the rapidly emerging interdisciplinary areas of condition monitoring and diagnostic engineering management. The contents discuss the latest tools and techniques in the multidisciplinary field of performance monitoring, root cause failure modes analysis, failure diagnosis, prognosis, and proactive management of industrial systems. There is a special focus on digitally enabled asset management and covers several topics such as condition monitoring, maintenance, structural health monitoring, non-destructive testing and other allied areas. Bringing together expert contributions from academia and industry, this book will be a valuable resource for those interested in latest condition monitoring and asset management techniques. |
| ai digital asset management: Artificial Intelligence for Asset Management and Investment Al Naqvi, 2021-01-13 Make AI technology the backbone of your organization to compete in the Fintech era The rise of artificial intelligence is nothing short of a technological revolution. AI is poised to completely transform asset management and investment banking, yet its current application within the financial sector is limited and fragmented. Existing AI implementations tend to solve very narrow business issues, rather than serving as a powerful tech framework for next-generation finance. Artificial Intelligence for Asset Management and Investment provides a strategic viewpoint on how AI can be comprehensively integrated within investment finance, leading to evolved performance in compliance, management, customer service, and beyond. No other book on the market takes such a wide-ranging approach to using AI in asset management. With this guide, you’ll be able to build an asset management firm from the ground up—or revolutionize your existing firm—using artificial intelligence as the cornerstone and foundation. This is a must, because AI is quickly growing to be the single competitive factor for financial firms. With better AI comes better results. If you aren’t integrating AI in the strategic DNA of your firm, you’re at risk of being left behind. See how artificial intelligence can form the cornerstone of an integrated, strategic asset management framework Learn how to build AI into your organization to remain competitive in the world of Fintech Go beyond siloed AI implementations to reap even greater benefits Understand and overcome the governance and leadership challenges inherent in AI strategy Until now, it has been prohibitively difficult to map the high-tech world of AI onto complex and ever-changing financial markets. Artificial Intelligence for Asset Management and Investment makes this difficulty a thing of the past, providing you with a professional and accessible framework for setting up and running artificial intelligence in your financial operations. |
| ai digital asset management: Applied Artificial Intelligence Mariya Yao, Adelyn Zhou, Marlene Jia, 2018-04-30 This bestselling book gives business leaders and executives a foundational education on how to leverage artificial intelligence and machine learning solutions to deliver ROI for your business. |
| ai digital asset management: Digital Asset Valuation and Cyber Risk Measurement Keyun Ruan, 2019-05-29 Digital Asset Valuation and Cyber Risk Measurement: Principles of Cybernomics is a book about the future of risk and the future of value. It examines the indispensable role of economic modeling in the future of digitization, thus providing industry professionals with the tools they need to optimize the management of financial risks associated with this megatrend. The book addresses three problem areas: the valuation of digital assets, measurement of risk exposures of digital valuables, and economic modeling for the management of such risks. Employing a pair of novel cyber risk measurement units, bitmort and hekla, the book covers areas of value, risk, control, and return, each of which are viewed from the perspective of entity (e.g., individual, organization, business), portfolio (e.g., industry sector, nation-state), and global ramifications. Establishing adequate, holistic, and statistically robust data points on the entity, portfolio, and global levels for the development of a cybernomics databank is essential for the resilience of our shared digital future. This book also argues existing economic value theories no longer apply to the digital era due to the unique characteristics of digital assets. It introduces six laws of digital theory of value, with the aim to adapt economic value theories to the digital and machine era. |
| ai digital asset management: Engineering Applications of Artificial Intelligence Aziza Chakir, |
| ai digital asset management: 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 digital asset management: No-Code Artificial Intelligence Ambuj Agrawal, 2023-03-07 A practical guide that will help you build AI and ML solutions faster with fewer efforts and no programming knowledge KEY FEATURES ● Start your journey to become an AI expert today. ● Learn how to build AI solutions to solve complex problems in your organization. ● Get familiar with different No-code AI tools and platforms. DESCRIPTION “No-Code Artificial Intelligence” is a book that enables you to develop AI applications without any programming knowledge. Authored by the founder of AICromo (https://aicromo.com/), this book takes you through an array of examples that shows how to build AI solutions using No-code AI tools. The book starts by sharing insights on the evolution of No-code AI and the different types of No-code AI tools and platforms available in the market. The book then helps you start building applications of Machine Learning in Finance, Healthcare, Sales, and Cybersecurity. It will also teach you to create AI applications to perform sales forecasting, find fraudulent claims, and detect diseases in plants. Furthermore, the book will show how to build Machine Learning models for a variety of use cases in image recognition, video object recognition, and data prediction. After reading this book, you will be able to build AI applications with ease. WHAT YOU WILL LEARN ● Use different No-code AI tools such as AWS Sagemaker, DataRobot, and Google AutoML. ● Learn how to create a Machine Learning model to predict housing prices. ● Build Natural Language Processing (NLP) models for Healthcare information Identification. ● Learn how to build an AI model to create targeted customer offerings. ● Use traditional ways to perform AI implementation using programming languages and AI libraries. WHO THIS BOOK IS FOR This book is for anyone who wants to build an AI app without writing any code. It is also helpful for current and aspiring AI and Machine Learning professionals who are looking to build automated, intelligent, and smart AI-based solutions. TABLE OF CONTENTS 1. What is AI? 2. Getting Started with No-Code AI 3. Building AI Model to Predict Housing Prices 4. Classifying Different Images 5. Building AI Model to Perform Sales Forecasting 6. Building AI Model to Find Fraudulent Claims 7. Building AI Model to Detect Diseases in Plants 8. Building AI Model to Create Targeted Customer Offerings 9. Building AI Model for Healthcare Information Identification 10. Building AI Model for Video Action Recognition 11. Building AI Applications with Coded AI |
| ai digital asset management: Artificial Intelligence and Blockchain in Digital Forensics P. Karthikeyan, Hari Mohan Pande, Velliangiri Sarveshwaran, 2023-02-06 Digital forensics is the science of detecting evidence from digital media like a computer, smartphone, server, or network. It provides the forensic team with the most beneficial methods to solve confused digital-related cases. AI and blockchain can be applied to solve online predatory chat cases and photo forensics cases, provide network service evidence, custody of digital files in forensic medicine, and identify roots of data scavenging. The increased use of PCs and extensive use of internet access, have meant easy availability of hacking tools. Over the past two decades, improvements in the information technology landscape have made the collection, preservation, and analysis of digital evidence extremely important. The traditional tools for solving cybercrimes and preparing court cases are making investigations difficult. We can use AI and blockchain design frameworks to make the digital forensic process efficient and straightforward. AI features help determine the contents of a picture, detect spam email messages and recognize swatches of hard drives that could contain suspicious files. Blockchain-based lawful evidence management schemes can supervise the entire evidence flow of all of the court data. This book provides a wide-ranging overview of how AI and blockchain can be used to solve problems in digital forensics using advanced tools and applications available on the market. |
| ai digital asset management: The Fourth Industrial Revolution Klaus Schwab, 2017-01-03 World-renowned economist Klaus Schwab, Founder and Executive Chairman of the World Economic Forum, explains that we have an opportunity to shape the fourth industrial revolution, which will fundamentally alter how we live and work. Schwab argues that this revolution is different in scale, scope and complexity from any that have come before. Characterized by a range of new technologies that are fusing the physical, digital and biological worlds, the developments are affecting all disciplines, economies, industries and governments, and even challenging ideas about what it means to be human. Artificial intelligence is already all around us, from supercomputers, drones and virtual assistants to 3D printing, DNA sequencing, smart thermostats, wearable sensors and microchips smaller than a grain of sand. But this is just the beginning: nanomaterials 200 times stronger than steel and a million times thinner than a strand of hair and the first transplant of a 3D printed liver are already in development. Imagine “smart factories” in which global systems of manufacturing are coordinated virtually, or implantable mobile phones made of biosynthetic materials. The fourth industrial revolution, says Schwab, is more significant, and its ramifications more profound, than in any prior period of human history. He outlines the key technologies driving this revolution and discusses the major impacts expected on government, business, civil society and individuals. Schwab also offers bold ideas on how to harness these changes and shape a better future—one in which technology empowers people rather than replaces them; progress serves society rather than disrupts it; and in which innovators respect moral and ethical boundaries rather than cross them. We all have the opportunity to contribute to developing new frameworks that advance progress. |
| ai digital asset management: Leading Digital George Westerman, Didier Bonnet, Andrew McAfee, 2014-09-23 Become a Digital Master—No Matter What Business You’re In If you think the phrase “going digital” is only relevant for industries like tech, media, and entertainment—think again. In fact, mobile, analytics, social media, sensors, and cloud computing have already fundamentally changed the entire business landscape as we know it—including your industry. The problem is that most accounts of digital in business focus on Silicon Valley stars and tech start-ups. But what about the other 90-plus percent of the economy? In Leading Digital, authors George Westerman, Didier Bonnet, and Andrew McAfee highlight how large companies in traditional industries—from finance to manufacturing to pharmaceuticals—are using digital to gain strategic advantage. They illuminate the principles and practices that lead to successful digital transformation. Based on a study of more than four hundred global firms, including Asian Paints, Burberry, Caesars Entertainment, Codelco, Lloyds Banking Group, Nike, and Pernod Ricard, the book shows what it takes to become a Digital Master. It explains successful transformation in a clear, two-part framework: where to invest in digital capabilities, and how to lead the transformation. Within these parts, you’ll learn: • How to engage better with your customers • How to digitally enhance operations • How to create a digital vision • How to govern your digital activities The book also includes an extensive step-by-step transformation playbook for leaders to follow. Leading Digital is the must-have guide to help your organization survive and thrive in the new, digitally powered, global economy. |
| ai digital asset management: The Impact of AI Innovation on Financial Sectors in the Era of Industry 5.0 Irfan, Mohammad, Elmogy, Mohammed, Shabri Abd. Majid, M., El-Sappagh, Shaker, 2023-09-05 In the dynamic and ever-changing financial landscape, the seamless integration of artificial intelligence (AI) and machine learning (ML) has presented unprecedented challenges for the banking and finance industry. As we embrace the era of Industry 5.0, financial institutions find themselves confronted with intricate decisions pertaining to investments, macroeconomic analysis, and credit evaluation, necessitating innovative technologies to navigate this complexity. Additionally, the mounting volume of financial transactions calls for efficient data processing and analysis. Considering these pressing concerns, scholars, academicians, and industry practitioners are eagerly seeking comprehensive insights into the transformative potential of AI and ML, specifically in bolstering resilience, fostering sustainable development, and adopting human-centric approaches within the financial sector. Offering a compelling solution to these critical challenges, The Impact of AI Innovation on Financial Sectors in the Era of Industry 5.0, edited by esteemed scholars Mohammad Irfan, Mohammed Elmogy, M. Shabri Abd. Majid, and Shaker El-Sappagh, embark on an in-depth exploration of the multifaceted functions and applications of AI and ML algorithms in the realm of finance. With a keen focus on Industry 5.0 principles such as resilience, human centricity, and sustainable development, this comprehensive compendium presents a collection of groundbreaking research papers that unveil the remarkable potential of AI/ML technologies in revolutionizing the financial services industry. By catering to a diverse audience comprising researchers, academicians, industrialists, investors, and regulatory bodies, this book actively invites contributions from industry practitioners and scholars, facilitating ongoing discussions on the efficacy of ML algorithms in efficiently processing vast financial data. As the financial landscape charts an ambitious course into Industry 5.0, the book emerges as an indispensable resource, empowering the industry with transformative advancements that will indelibly shape the future of finance. |
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 …
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 …
