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Human + Machine

In a sentence

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.

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 story it tells the reader

The reader 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.

External problem

AI is rapidly transforming business, but it is unclear how to apply it for breakthrough results rather than stalling automation.

Internal problem

Leaders feel the topic of AI is intimidating, confusing, and threatening, fearing job displacement and being left behind by competitors.

Philosophical problem

It is wrong to view humans and machines as adversaries; the man-versus-machine mindset is shortsighted and squanders the greatest source of value.

The plan

  1. Adopt the right mindset: reimagine processes around the missing middle rather than just automating them.
  2. Foster a culture of experimentation to test, learn, and scale AI in your processes.
  3. Exercise leadership for responsible AI—building trust and managing ethical, legal, and societal concerns.
  4. Build a dynamic data supply chain to fuel intelligent systems.
  5. Develop the eight fusion skills your people need to collaborate with AI.

Success

  • 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.
  • Responsible, trusted AI deployments aligned with human values.

At stake

  • Modest productivity gains that eventually stall out as competitors surge ahead.
  • Unfilled jobs and a skills gap as workers are unprepared for evolving roles.
  • Eroded trust, biased systems, and brand or public-relations crises from irresponsible AI.
  • Being relegated to the losers in the next decade's divide between AI winners and losers.

Model of the world · 11 constructs · 14 relations

A causal model in which organizational design levers (MELDS practices) and human-machine collaboration roles in the 'missing middle' drive psychological and behavioral states (trust, augmented capability, employee engagement) that produce business outcomes such as reimagined processes and step-change performance.

Design levers

  • Responsible AI Leadership
  • Fusion Skills
  • Reimagining Mindset
  • Experimentation Culture
  • Data Supply Chain

Intermediate states & behaviors

  • Process Reimagination
  • Missing-Middle Collaboration Roles
  • Augmented Human Capability
  • Human Trust in AI
  • Employee Engagement and Satisfaction

Outcomes

  • Step-Change Business Performance
Consolidated shape of the book’s model — full constructs and relationships below.

Reimagining Mindsetdesign lever

A leadership orientation that rejects mere automation in favor of completely rethinking business processes and the nature of work around human-machine collaboration in the missing middle.

Experimentation Culturedesign lever

An organizational condition that actively tests AI in processes, embraces trial-and-error and failure, and uses build-measure-learn cycles to discover and scale reimagined processes.

Responsible AI Leadershipdesign lever

Executive commitment to deploying AI ethically and lawfully, managing trust, bias, explainability, accountability, guardrails, and societal consequences of process change.

Data Supply Chaindesign lever

A dynamic, enterprise-wide, real-time flow of rich and varied data for capturing, cleaning, integrating, curating, and storing information that fuels intelligent systems.

Fusion Skillsdesign lever

The eight human capabilities (rehumanizing time, responsible normalizing, judgment integration, intelligent interrogation, bot-based empowerment, holistic melding, reciprocal apprenticing, relentless reimagining) that combine human and machine talents within a process.

Missing-Middle Collaboration Rolesbehavioral pattern

The deployment of the six human-machine roles in which humans train, explain, and sustain AI and machines amplify, interact with, and embody human capabilities in reimagined processes.

Human Trust in AIpsychological state

The degree to which employees and customers trust and accept AI systems, shaped by transparency, explainability, agency, guardrails, and avoidance of moral crumple zones.

Augmented Human Capabilitypsychological state

The enhanced, often superhuman, performance employees achieve when AI amplifies their insight, interacts on their behalf, or physically extends their abilities.

Employee Engagement and Satisfactionpsychological state

The increased motivation, satisfaction, safety, and sense of doing more human, less robotic work that results from AI offloading tedious tasks and rehumanizing time.

Process Reimaginationbehavioral pattern

The transformation of business processes from linear, standardized, automated forms into fluid, adaptive, human-machine collaborative workflows.

Step-Change Business Performanceoutcome metric

Orders-of-magnitude improvements in productivity, growth, innovation, customer satisfaction, and cost that distinguish AI leaders from laggards.

How they connect

  • reimagining mindset → predicts process reimagination
  • experimentation culture → predicts process reimagination
  • responsible leadership → predicts human trust in ai
  • data supply chain → influences missing middle roles
  • missing middle roles → predicts augmented human capability
  • missing middle roles → mediates process reimagination
  • fusion skills → predicts augmented human capability
  • fusion skills → predicts process reimagination
  • human trust in ai → influences missing middle roles
  • augmented human capability → predicts employee engagement
  • augmented human capability → predicts business performance
  • process reimagination → predicts business performance
  • employee engagement → predicts business performance
  • responsible leadership → moderates business performance

Frameworks & instruments in this book

  • Use AI to augment, not just automate—reimagine the work itself.
  • Put people at the center of every AI initiative.
  • Let machines do what they do best (speed, scale, repetition, prediction) and humans do what they do best (creativity, judgment, empathy, improvisation).
  • Treat data as a dynamic, enterprise-wide supply chain that fuels intelligent systems.
  • Experiment continually; standard best-practice replication is over.
  • Deploy AI responsibly with explainability, accountability, fairness, and guardrails.

Several of these are operationalized as tools in the People Analytics Toolbox.

Topics

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