Coming soon · Book Profile
Human + Machine
In the age of AI, the greatest business value comes not from machines replacing humans but from humans and machines collaborating in a 'missing middle' to reimagine work and processes.
A profile of this book is on the way.
What it’s about
Human + Machine reframes the dominant 'robots-will-take-our-jobs' narrative by showing, through research across 1,500+ companies and rich real-world examples, that the third wave of business transformation is about adaptive, collaborative processes in which people and AI amplify each other's strengths. Drawing on Accenture's research and hands-on experience, Daugherty and Wilson reveal the 'missing middle'—new categories of work where humans train, explain, and sustain AI while machines amplify, interact with, and embody new human capabilities. The book supplies a practical roadmap—the MELDS framework (Mindset, Experimentation, Leadership, Data, Skills) plus eight new 'fusion skills'—that leaders, managers, and workers can use to reimagine processes, avoid stalling out on mere automation, and capture step-change gains in performance while keeping people at the center and deploying AI responsibly.
The through-line
- Who it’s for
- A business leader, manager, or worker who wants to harness AI to drive growth, improve work, and secure their organization's and career's future in the age of AI.
- The problem
- AI is rapidly transforming business, but it is unclear how to apply it for breakthrough results rather than stalling automation. Leaders feel the topic of AI is intimidating, confusing, and threatening, fearing job displacement and being left behind by competitors.
- The plan
- Adopt the right mindset: reimagine processes around the missing middle rather than just automating them.
- Foster a culture of experimentation to test, learn, and scale AI in your processes.
- Exercise leadership for responsible AI—building trust and managing ethical, legal, and societal concerns.
- Build a dynamic data supply chain to fuel intelligent systems.
- Develop the eight fusion skills your people need to collaborate with AI.
- The payoff
- Step-change gains in performance that propel your organization to industry leadership. · Workers who are safer, more engaged, and freed to do more human, creative, judgment-based work. · New business models, products, and services made possible by human-machine collaboration.
See our guide
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- Analytics at Work: Smarter Decisions, Better Results →
- Antifragile (Incerto) →
- Ben Horowitz - The Hard Thing About Hard Things_ Building a Business When There Are No Easy Answers (2014, HarperBusiness) - libgen.li →
- Big Data: A Revolution That Will Transform How We Live, Work, and Think →
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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.