AI has advanced at an extraordinary pace, yet our scientific understanding of cognition has not progressed at the same speed. Many books on cognitive computing concentrate on algorithms, architectures, ML, natural language processing, and the history of AI. These resources explain valuable technologies, but they leave a fundamental question unanswered: What makes a system cognitive?

I reposition cognitive computing within the broader scientific study of cognition itself. Rather than treating it as simply another branch of artificial intelligence, I introduce Neural Artificial Cognition(TM) (NAC), an integrative meta-framework for examining the processes that produce intelligent behavior across biological organisms, computational systems, enterprises, and collaborative human-AI ecosystems.

I begin with a vital distinction. Intelligence is recognized via observable performance: solving problems, detecting patterns, generating language, making recommendations, or adapting to change. Cognition concerns the processes that make those abilities possible. Perception, attention and task switching, memory, learning, knowledge representation, reasoning, prediction, decision-making, adaptation, communication, and metacognition form the cognitive architecture from which intelligent behavior emerges.

Instead of organizing the discussion around technologies that may soon be replaced, I adopt a cognition-centered structure. Each foundational capacity is examined via relatable human experiences, current scientific understanding, contemporary AI, organizational applications, and emerging human-machine collaboration. This approach helps you understand how biological and artificial systems differ while revealing the cognitive principles they may share.

Across 32 chapters, the discussion progresses via 3 connected stages. Part 1 establishes a new scientific foundation for cognitive computing and introduces NAC. Part 2 explains how cognition emerges via interacting capacities across biological, computational, enterprise, and collective systems. Part 3 looks ahead to human-AI cognitive collaboration, Cognitive Digital Twins, agentic AI, neuromorphic hardware, responsible cognition, and future cognitive enterprises including Artificial General Cognition.

Although grounded in scholarship, I wrote it in clear and relatable language. Real-life examples make complex ideas accessible without reducing their scientific significance. It's for researchers, AI practitioners, enterprise architects, engineers, educators, entrepreneurs, executives, philosophers, and curious readers seeking a deep understanding of cognition beyond technical implementation.