Coming soon · Book Profile
Marketing Artificial Intelligence: AI, Marketing, and the Future of Business
A practical, nontechnical guide showing marketers how to understand, pilot, and scale artificial intelligence to make their marketing smarter, more efficient, and more human.
A profile of this book is on the way.
What it’s about
Marketing Artificial Intelligence demystifies AI for the nontechnical marketer, defining it simply as 'the science of making marketing smart.' Drawing on years of research, dozens of interviews with AI executives and engineers, and the authors' experience building Marketing AI Institute, the book explains the core technologies (machine learning, deep learning, NLP/NLG, computer vision) through three approachable categories—language, vision, and prediction. It then walks readers through how to evaluate AI vendors using the Marketer-to-Machine Scale, how to identify and prioritize use cases via the 5Ps framework, and how to apply AI across ten marketing disciplines (advertising, analytics, PR, content, customer service, ecommerce, email, sales, SEO, and social). With real-world examples, vendor spotlights, and a clear-eyed look at bias, ethics, and responsible AI, the book argues that AI will not replace marketers but will augment them—freeing humans to focus on creativity, empathy, and strategy while machines handle data-driven, repetitive tasks. The result is a blueprint for becoming a 'next-gen marketer' who builds a sustained competitive advantage.
The through-line
- Who it’s for
- A nontechnical marketer (entry-level to CMO) who wants to stay relevant, build a competitive advantage, and advance their career in an industry being transformed by AI.
- The problem
- Marketers struggle to understand what AI is, how to assess AI vendors, and how to apply AI to reduce costs and accelerate revenue. They feel overwhelmed, uncertain, and fearful of being left behind or replaced as technology accelerates.
- The plan
- Learn what AI is via the language, vision, and prediction categories.
- Use the Marketer-to-Machine Scale to assess and vet AI vendors.
- Identify and prioritize use cases with the 5Ps framework and AI Score tool.
- Run quick-win pilot projects that are data-driven, repetitive, and predictive.
- Apply AI across your marketing discipline using proven use cases and vendors.
- The payoff
- Accelerated revenue growth and lower costs through intelligent automation. · Personalized consumer experiences delivered at scale. · More time freed for strategy, creativity, and empathy—the uniquely human work.
See our guide
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Additional reading
- Designing Machine Learning Systems (DMLS) · Chip Huyen
The author explicitly positions 'AI Engineering' as a companion book to DMLS. DMLS covers the principles of traditional ML engineering (feature engineering, model training, tabular data), which this book builds upon by focusing on the new paradigm of foundation models.
- Artificial Intelligence: A Modern Approach · Stuart Russell and Peter Norvig
The book references this classic AI textbook for its definition of an 'agent' as a perceiving and acting entity, providing the foundational academic context for the 'Agents' section in Chapter 6.
- OpenAI Prompt Engineering Guide · OpenAI
Official documentation from the model provider on how to craft effective prompts, recommended by the author.
- Anthropic's Prompt Engineering Guide · Anthropic
Official guide from another major LLM provider, offering best practices for prompt design.
- dair-ai/Prompt-Engineering-Guide · DAIR.AI Community
A comprehensive, community-maintained GitHub repository covering a wide range of prompt engineering techniques.
- GPT Researcher · assafelovic
An open-source project that inspired the book's research summarization engine, recommended for further exploration of building production-grade LLM applications.
- Building Effective AI Agents · Anthropic
An article recommended by the author for a deeper understanding of the distinction between agentic workflows and autonomous agents.
- Model Context Protocol (MCP) Official Website · Anthropic and Community
The primary source for the MCP specification, tutorials, and ecosystem resources, essential for building or consuming MCP servers.
- FastMCP 2 Documentation · jlowin and community
The official documentation for the recommended Python SDK for building MCP servers, used in the book's example.
- Knowledge Graph Enhanced RAG · Tomaž Bratanič and Oskar Hane
Recommended by the author for readers interested in diving deeper into combining knowledge graphs with RAG.