Ketan Sanjay Desale

This book is a practical guide to the integration of generative AI into human resources, covering use cases across recruitment, onboarding, training, and retention. It offers strategic guidance, ethical frameworks, and technical insights to help organisations deploy generative AI effectively while safeguarding fairness, privacy, and compliance. It covers AI-driven applications in talent acquisition, onboarding, employee engagement, career development, and decision-making, while addressing key concerns such as algorithmic bias, data privacy, and regulatory compliance. With practical case studies and deployment templates, the book provides a structured approach to evaluating and integrating generative AI tools within HR systems. From resume screening to personalised learning paths and predictive analytics, this book bridges the gap between AI technologies and HR operations to help organisations build agile, ethical, and employee-centric workplaces. • Examines generative AI’s applications across recruitment, onboarding, L&D, engagement, and retention • Provides strategic frameworks for integrating AI into HR ecosystems • Includes case studies and practical deployment examples from real organizations • Addresses key issues of AI bias, explainability, data privacy, and compliance • Offers guidance on selecting and evaluating HR-specific AI tools • Explains core AI concepts for both technical and non-technical HR professionals • Includes future-focused insights into AI-driven workforce planning and employee experience design This book is for HR professionals, business leaders, AI developers, and organizational strategists seeking to understand, implement, and manage generative AI solutions in the HR domain.

This book is a practical guide to the integration of generative AI into human resources, covering use cases across recruitment, onboarding, training, and retention. It offers strategic guidance, ethical frameworks, and technical insights to help organisations deploy generative AI effectively while safeguarding fairness, privacy, and compliance. It covers AI-driven applications in talent acquisition, onboarding, employee engagement, career development, and decision-making, while addressing key concerns such as algorithmic bias, data privacy, and regulatory compliance. With practical case studies and deployment templates, the book provides a structured approach to evaluating and integrating generative AI tools within HR systems. From resume screening to personalised learning paths and predictive analytics, this book bridges the gap between AI technologies and HR operations to help organisations build agile, ethical, and employee-centric workplaces. • Examines generative AI’s applications across recruitment, onboarding, L&D, engagement, and retention • Provides strategic frameworks for integrating AI into HR ecosystems • Includes case studies and practical deployment examples from real organizations • Addresses key issues of AI bias, explainability, data privacy, and compliance • Offers guidance on selecting and evaluating HR-specific AI tools • Explains core AI concepts for both technical and non-technical HR professionals • Includes future-focused insights into AI-driven workforce planning and employee experience design This book is for HR professionals, business leaders, AI developers, and organizational strategists seeking to understand, implement, and manage generative AI solutions in the HR domain.

This book explores the application of the complex relationship between concept drift and cutting-edge large language models to address the problems and opportunities in navigating changing data landscapes. It discusses the theoretical basis of concept drift and its consequences for large language models, particularly the transformative power of cutting-edge models such as GPT-3.5 and GPT-4. It offers real-world case studies to observe firsthand how concept drift influences the performance of language models in a variety of circumstances, delivering valuable lessons learnt and actionable takeaways. The book is designed for professionals, AI practitioners, and scholars, focused on natural language processing, machine learning, and artificial intelligence.Examines concept drift in AI, particularly its impact on large language modelsAnalyses how concept drift affects large language models and its theoretical and practical consequencesCovers detection methods and practical implementation challenges in language modelsShowcases examples of concept drift in GPT models and lessons learnt from their performanceIdentifies future research avenues and recommendations for practitioners tackling concept drift in large language models