rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Agentic hyper-personalized dimensions
    sub title: six dimensions of business dark data
  name:
    name part: Vermeulen, Andreas François.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  type of resource: text
  origin info:
    place:
      place term: cau
    date issued: 2026
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (xliv, 206 pages) : illustrations
  abstract: Agentic Hyper-Personalized Dimensions explores how generative AI (Gen-AI), combined with agentic intelligence, can transform raw enterprise data into hyper-personalized, actionable insights. This book introduces a practical framework for building AI-powered agent swarms that operate across six critical dimensions of business dark data, empowering intelligent automation, decision augmentation, and strategic foresight at an unprecedented scale. In an age when data is underutilized, organizations are overwhelmed by complexity, latency, and fragmentation, traditional analytics pipelines fall short in handling dynamic environments and human-centric demands. This book addresses this critical gap by detailing how autonomous Gen-AI agents -- built on foundational disciplines of data engineering, data science, and machine learning -- can process, personalize, and operationalize hidden insights buried deep within enterprise systems. The six dimensions presented in the book form a cognitive and computational blueprint that guides the design, deployment, and evolution of agentic swarms. Each dimension focuses on a specific mode of intelligence, such as contextual reasoning, emotional alignment, or real-time adaptive sensing, and shows how AI agents can specialize within each domain to maximize business impact. Readers won't just learn theory, they'll explore field-tested methodologies to deploy Gen-AI-powered solutions at enterprise scale. From zoned data lakes and transformer models to swarm governance and consensus mechanisms, the book walks readers through the full life cycle of building intelligent systems that learn, evolve, and act with purpose. What makes this book essential, and unlike others in the market, is that the author is not writing from the sidelines. He is actively architecting and delivering these systems today, with firsthand insight into what works, what fails, and what the future holds. The methodology presented blends system design, human-aligned AI, indu…
  table of contents: Chapter 1: Six Thinking Dimensions -- Chapter 2: White Dimension - The Factual AI-powered Viewpoint -- Chapter 3: Red Dimension - Feelings and Instincts AI-powered Viewpoint -- Chapter 4: Black Dimension - Judgement and Evidence AI-powered Viewpoint -- Chapter 5: Yellow Dimension - Optimistic AI-powered Viewpoint -- Chapter 6: Green Dimension - Creativity AI-powered Viewpoint -- Chapter 7: Blue Dimension - Process Management AI-powered Viewpoint -- Chapter 8: Six Thinking Dimensions - Holistic AI-powered Viewpoint.
  note: Andreas François Vermeulen.
  note: Includes index.
  subject:
    topic: Machine learning
  subject:
    topic: Generative artificial intelligence
  subject:
    topic: Artificial intelligence
  subject:
    topic: Artificial intelligence
    topic: Data processing
  subject:
    topic: Apprentissage automatique
  subject:
    topic: Intelligence artificielle
  subject:
    topic: Intelligence artificielle
    topic: Informatique
  subject:
    topic: artificial intelligence
  classification: Q325.5 .V47 2026
  classification: 006.3/1
  related item:
    identifier: (OCoLC)1528538730
  identifier: 9798868818776 (electronic bk.)
  identifier: 9798868818769
  identifier: 8868818761
  identifier: 9788868818760
  identifier: 9798868818776 O'Reilly Media
  location:
    physical location: POD
  record info:
    record content source: GW5XE
    record creation date: 260112
    record change date: 20260412194815.7
    record identifier: in00005778105
    language of cataloging:
      language term: eng