Creating Transparent AI From agriculture to transportation, entertainment to medicine, and banking to social media, artificial intelligence (AI) is changing how humans do practically everything. We experience AI in our daily lives through our fitness trackers, home digital assistant systems, and curated news services, to name a few examples. For talent development, this is no different. The fields of artificial intelligence and talent development have been on a collision course for decades, and their convergence has already occurred. It has just taken many in our profession some time to recognize this fact. On the horizon, AI-powered innovations are transforming the workplace and the role of the talent development professional, affecting recruiting to training to compensation. As such, there are actions TD professionals should take now to prepare ourselves and our organizations for the evolving AI revolution. In AI in Talent Development, Margie Meacham describes the benefits, uses, and risks of AI technology and offers practical tools to strengthen and enhance learning and performance programs. In layman’s terms, Meacham demonstrates how we can free time for ourselves by employing a useful robot “assistant,” create a chatbot for specific tasks (such as a new manager bot, a sales coach bot, or new employee onboarding bot), and build personalized coaching tools from AI-processed big data. She concludes each of the six chapters with helpful tips and includes a resource guide with planning tools, templates, and worksheets. Meacham dispels fear of AI’s black box—the term used to describe its unknowability and opacity—and points out ways AI can help us be better at creativity and critical thinking, what we humans do best.

Creating Transparent AI From agriculture to transportation, entertainment to medicine, and banking to social media, artificial intelligence (AI) is changing how humans do practically everything. We experience AI in our daily lives through our fitness trackers, home digital assistant systems, and curated news services, to name a few examples. For talent development, this is no different. The fields of artificial intelligence and talent development have been on a collision course for decades, and their convergence has already occurred. It has just taken many in our profession some time to recognize this fact. On the horizon, AI-powered innovations are transforming the workplace and the role of the talent development professional, affecting recruiting to training to compensation. As such, there are actions TD professionals should take now to prepare ourselves and our organizations for the evolving AI revolution. In AI in Talent Development, Margie Meacham describes the benefits, uses, and risks of AI technology and offers practical tools to strengthen and enhance learning and performance programs. In layman's terms, Meacham demonstrates how we can free time for ourselves by employing a useful robot “assistant,” create a chatbot for specific tasks (such as a new manager bot, a sales coach bot, or new employee onboarding bot), and build personalized coaching tools from AI-processed big data. She concludes each of the six chapters with helpful tips and includes a resource guide with planning tools, templates, and worksheets. Meacham dispels fear of AI's black box--the term used to describe its unknowability and opacity--and points out ways AI can help us be better at creativity and critical thinking, what we humans do best.

Artificial intelligence is changing the world of work. How can HR professionals understand the variety of opportunities AI has created for the HR function and how best to implement these in their organization? This book provides the answers. From using natural language processing to ensure job adverts are free from bias and gendered language to implementing chatbots to enhance the employee experience, artificial intelligence can add value throughout the work of HR professionals. Artificial Intelligence for HR demonstrates how to leverage this potential and use AI to improve efficiency and develop a talented and productive workforce. Outlining the current technology landscape as well as the latest AI developments, this book ensures that HR professionals fully understand what AI is and what it means for HR in practice. Alongside coverage of employee engagement and recruitment, this second edition features new material on applications of AI for virtual work, reskilling and data integrity. Packed with practical advice, research and new and updated case studies from global organizations including Uber, IBM and Unilever, the second edition of Artificial Intelligence for HR will equip HR professionals with the knowledge they need to improve people operational efficiencies, and allow AI solutions to become enhancements for driving business success.

With Artificial Intelligence (AI) creating huge opportunities for learning and employee development, how can learning professionals best implement the use of AI into their environment? Artificial Intelligence for Learning is the essential guide for learning professionals who want to understand how to use AI to improve all aspects of learning in organizations. This new edition debunks the myths and misconceptions around AI, discusses the learning theory behind generative AI and gives strategic and practical advice on how AI can be used. This book also includes specific guidance on how AI can provide learning support, chatbot functionality and content, as well as ideas on ethics and personalization. This book is necessary reading for all learning practitioners needing to understand AI and what it means in practice.

Harness the power of artificial intelligence in hiring The typical hiring process is fraught with complexity, inefficiency, and bias and often shuts out the most talented candidates. Decoding Talent: How AI and Big Data Can Solve Your Company’s People Puzzle makes the case for using complex advanced technologies to move past these problems toward effortless optimal candidate decisions. AI experts Eric Sydell, Mike Hudy, and Michael Ashley explain why the traditional resume-based process is out of date, why hiring is difficult, the cost of bad people decisions, how bias interferes in hiring practices, and how AI can address these problems. Decoding Talent reveals that using AI in hiring doesn’t require your human resource professionals to unlearn and relearn their craft; rather, machine learning can complement their skills by consolidating and analyzing data to recommend actions. Imagine a world in which you didn’t have to wonder: Who is the best candidate for the job? What is the return on investment of our hiring process? Is our hiring process fair and equitable? Is our human talent deployed optimally across our organization? What can human resources do to better drive business outcomes for our company? Is our candidate experience adding value to our brand? Incorporating scientifically based hiring can make this world a reality, benefiting both your company and the candidates for hire.

This book focuses on digitalised talent management—the use of information technologies in talent management. The book affords theoretically, methodologically and empirically informed insights that are especially salient given the need for executives and organisations to balance the role of humans and technology, while ensuring competitiveness in this interconnected and increasingly digital world. In doing so, the book will shape and contribute to academic and industry-based conversations about the role of technological innovations in enabling organizations to transition towards digital ways of organising talent, as well as the associated implications for the who, what, where, when, and why of talent management as stakeholders decide which aspects of talent management can be delegated to technology, and those that require human agency. This book adds value by assembling subject matter experts currently siloed within traditional research domains whilst also highlighting the complexity of managing talent. By synthesising content from world-leading academics who herald from various backgrounds, the book will instigate, shape and contribute to conversations about both the promises and perils of digitalised talent management and the extent to which judgments and decisions about an organisations most valuable asset—it’s talent—should be delegated to non-human agents. This book will be of interest to researchers, academics and students in the fields of talent management and organisational design, especially those interested in digital ways of working, managing and leading.

In the digital-driven economy era, an AI-oriented competency framework (AIoCF) is a collection to identify AI-oriented knowledge, attributes, efforts, skills, and experiences (AKASE) that directly and positively affect the success of employees and the organization. The application of skills-based competency analytics and AI-equipped systems is gradually becoming accepted by business and production organizations as an effective tool for automating several managerial activities consistently and efficiently in developing and moving the capacity of a company up to a world-class level. AI-Oriented Competency Framework for Talent Management in the Digital Economy: Models, Technologies, Applications, and Implementation discusses all the points of an AIoCF, which includes predictive analytics, advisory services, predictive maintenance, and automated processes, which help to make the operations of project management, personnel management, or administration more efficient, profitable, and safe. The book includes the functionality of emerging career pathways, hybrid learning models, and learning paths related to the learning and development of employees in the production or delivery fields. It also presents the relationship between skills taxonomy and competency framework with interactive methods using datasets, processing workflow diagrams, and architectural diagrams for easy understanding of the application of intelligent functions in role-based competency systems. By also covering upcoming areas of AI and data science in many government and private organizations, the book not only focuses on managing big data and cloud resources of the talent management system but also provides cybersecurity techniques to ensure that systems and employee competency data are secure. This book targets a mixed audience of students, engineers, scholars, researchers, academics, and professionals who are learning, researching, and working in the field of workforce training, human resources, talent management systems, requirement, headhunting, outsourcing, and manpower consultant services from different cultures and industries in the era of digital economy.