Human + Machine

How do you prepare for a future where artificial intelligence isn’t just automating tasks but reshaping the very meaning of work? In Human + Machine: Reimagining Work in the Age of AI, authors Paul Daugherty and H. James Wilson invite you to rethink what it means to collaborate with technology. They argue that the rise of AI marks not the end of human relevance, but the beginning of a profound partnership that redefines business, creativity, and purpose. Their central claim is simple but transformative: the future of work depends on humans and machines working together—each amplifying the other’s strengths rather than competing for dominance.

Rather than accepting the popular dystopian narrative of machines replacing people, Daugherty and Wilson introduce what they call the ‘missing middle’—a space where humans and intelligent systems collaborate to do things neither could achieve alone. This space is where the most groundbreaking innovations will arise, and where organizations can gain exponential performance gains. Through vivid examples—from BMW’s collaborative factory robots to AI-driven diagnostic systems at GE—the authors show that companies across industries are already thriving by embracing this new symbiosis.

From Automation to Adaptation

For decades, technology has focused on automating existing tasks: making them faster, more standardized, and more efficient. Daugherty and Wilson point out that this approach has reached its limits. The next wave—what they call the third wave of business transformation—is built on adaptive processes that evolve dynamically using real-time data and human input. Companies that treat AI merely as automation will plateau, while those that reimagine their processes as organic collaborations will leap ahead.

To illustrate, they compare business automation to GPS navigation: early systems simply digitized static maps, while today’s AI-powered tools like Waze use live data to optimize routes in real time. The lesson? AI doesn’t just make old processes digital—it makes them adaptive, self-improving, and human-aware.

The Missing Middle and Fusion Skills

At the heart of this transformation lies the missing middle. In this space, people train, explain, and sustain smart machines, while machines amplify, interact with, and embody human capabilities. The authors call these shared competencies fusion skills—new human abilities required to thrive in an AI-infused workplace. These skills include teaching algorithms empathy, learning to interrogate intelligent systems, and rehumanizing time by focusing on creativity and judgment rather than repetitive tasks.

These skills redefine careers: data scientists become ‘algorithm trainers,’ managers learn to balance automation with ethics, and factory workers partner with cobots that make production safer and more engaging. As Arianna Huffington observes in her endorsement of the book, this isn’t about machines replacing human potential—it’s about technology augmenting our humanity.

Why This Revolution Matters

The authors argue that ignoring this collaborative model will create a divide between winners and losers in the economy. The winners are not those who have implemented the most AI, but those who have reimagined their culture, leadership, and processes around human empowerment. Daugherty and Wilson warn that companies fixated on automation risk falling into the “digital Darwinism” trap—where technology evolves faster than their ability to adapt.

Their research across 1,500 organizations revealed that only about 9% have stepped fully into this adaptive future. These leading-edge firms follow five principles, summarized by the acronym MELDS: Mindset (rethinking human-machine collaboration), Experimentation (testing and learning continuously), Leadership (governing AI ethically), Data (building intelligent supply chains), and Skills (developing fusion capabilities). This framework, the authors contend, is the recipe for lasting innovation in the age of AI.

An Ethical, Human Future

The book’s ultimate goal is optimistic but pragmatic. It urges you—as a leader, employee, or policymaker—to stop thinking in terms of “humans versus machines” and start asking “how can humans and machines do better together?” This shift requires responsible AI systems that are explainable, fair, and transparent. It also requires leaders to place people at the center of transformation—ensuring training, equity, and accountability stay ahead of technology itself.

The Core Message

The AI revolution is not about replacing human work, but reimagining it. By embracing the missing middle, building fusion skills, and cultivating adaptive processes, you can turn AI from a threat into a partner for creativity and growth. As Daugherty and Wilson conclude, AI gives us “superhuman capabilities” so we can spend more time being human.

To truly grasp how AI is reshaping industries, Daugherty and Wilson describe three waves of business transformation. The first, pioneered by Henry Ford, standardized work through assembly lines. The second, driven by information technology, automated digital processes. The third—the age of AI—is defined by adaptive processes that combine machine learning with human insight to evolve in real time.

From Mechanistic to Organic Systems

In the first two waves, businesses optimized linear systems: step-by-step workflows designed for consistency and measurement. But now, AI makes those rigid systems obsolete. The authors show how organizations like BMW and Mercedes-Benz moved from fixed assembly lines to flexible human-machine teams that reconfigure themselves based on data. These “organic” systems adapt like living organisms, responding to market shifts, customer preferences, and operational conditions without requiring a massive overhaul.

This transition is similar to evolution in biology. Just as species survive by adapting to environmental change, companies survive by developing adaptive intelligence—using data feedback loops to learn and modify behavior over time.

Adaptive Process in Action

Look at the example of General Electric. By creating “digital twins” of its machines—virtual replicas that monitor performance and predict maintenance—GE reimagined how it delivers service and innovation. These twins allow engineers to run virtual experiments, foresee problems, and design improvements without halting production. Over time, this data-rich process boosts efficiency and human creativity, demonstrating adaptation at scale.

AI turns static processes like supply chains into dynamic ecosystems. A health-food company, for example, used neural networks to predict demand fluctuations, cutting forecast errors by 20%. Procter & Gamble’s billion-dollar savings goal rests on similar adaptive supply-chain logic. As Daugherty and Wilson emphasize, this new responsiveness is the hallmark of the third wave.

Implications for You and Your Organization

The shift to adaptive processes means embracing uncertainty and experimentation. Instead of aiming for efficiency alone, you need a system that learns from each interaction. Processes become less about control and more about collaboration. This requires a cultural mindset—one that values curiosity, continuous learning, and openness to untested ideas. (In The Second Machine Age, MIT’s Brynjolfsson and McAfee echo this sentiment, noting that innovation now stems from dynamic human-machine ecosystems.)

By committing to adaptive thinking, you position your organization to thrive in an era defined by flexibility and creativity—the essence of AI-powered transformation.

Imagine a team where machines handle data-heavy analysis while humans focus on judgment, empathy, and creativity. This is the missing middle—the space between pure automation and human-only work. Daugherty and Wilson call it “missing” because most businesses overlook it, yet it’s where the greatest potential for innovation lives.

Human Roles: Trainer, Explainer, Sustainer

On one side of the missing middle, humans give machines purpose and alignment. Trainers teach AI systems to interpret sarcasm, empathy, or local context—like Yahoo’s team that taught algorithms to recognize humor online. Explainers bridge the gap between AI and leadership, translating complex algorithmic logic into actionable and ethical business terms. And sustainers ensure systems remain safe and fair—actively monitoring biases, errors, and unintended consequences (similar to the role of data ethicists advocated by organizations like Google’s PAIR initiative).

Machine Roles: Amplify, Interact, Embody

On the other side, machines enhance human capability. They amplify decision-making through deep data analysis, interact seamlessly via natural language (as in AI assistants like Aida or Alexa), and embody human extension through robotics—such as cobots helping Mercedes and BMW workers lift and fit parts safely. Each collaboration shifts people from routine tasks toward higher-level problem-solving and creativity.

The Human Advantage

The missing middle challenges the “replacement myth.” It proves that as machines grow more capable, humans become more essential—not less. A Mercedes engineer explains that people and machines working together increase productivity by 85%. That’s not competition—it’s synergy. Similar effects occur in medicine, where AI tools help radiologists or doctors free up time for patient interaction. The paradox of AI, as Daugherty and Wilson note, is that more automation leads to more humanity—if managed thoughtfully.

For leaders, the question isn’t whether jobs will disappear, but which new ones will emerge. Roles like empathy trainers or algorithm forensics analysts didn’t exist before. As AI advances, the missing middle becomes the new frontier of human enterprise.

How can leaders navigate the rapid shift toward AI collaboration? Daugherty and Wilson provide an action-oriented guide called MELDS—five principles shaping organizations that succeed in the human + machine era. Mindset, Experimentation, Leadership, Data, and Skills form the foundation for transformation.

Mindset: Reimagine, Don’t Automate

Executives must stop equating AI with efficiency and start seeing it as creative reimagination. AI isn’t a tool for replacing tasks—it’s an opportunity to redesign processes like living systems. This means rethinking everything, from assembly lines to customer interactions. Instead of treating AI as a one-time implementation, you cultivate a mindset of adaptation and curiosity.

Experimentation: Build, Measure, Learn

AI thrives on iteration. Amazon illustrates this principle with its test of “Amazon Go,” where employees act as live beta users for cashierless retail. The company’s culture of experimentation allows failures without fear—Bezos even claims, “I’ve made billions of dollars of mistakes.” Like scientific inquiry, each failed hypothesis yields new data to refine the next round. The lesson for you is simple: encourage small experiments, gather evidence, and scale what works.

Leadership: Ethics, Agency, Trust

Trust is the cornerstone of the human-machine relationship. Leaders must confront algorithmic aversion—our tendency to trust people over computers—and install guardrails to ensure AI behaves responsibly. They also need to preserve human agency: giving employees some control over AI decisions fosters confidence rather than fear. As Toyota’s researchers remind us, humans forgive human mistakes more easily than machine errors; leadership must scale trust through visibility and accountability.

Data: Build a Supply Chain for Intelligence

The data principle calls for seamless integration of information across departments. AI depends on rich, diverse, and unbiased data pipelines. Leaders at Ducati and Facebook show how dynamic data supply chains can fuel intelligent systems while reducing waste. Managers should treat data like a living resource—clean it, share it, and monitor it ethically to avoid corrupted insights.

Skills: Cultivate Fusion Talent

Finally, organizations must invest in developing fusion skills—human abilities that complement machines. From reciprocity in training AI to judgment integration in high-risk decisions, these hybrid talents separate future-ready employees from obsolete roles. As Daugherty and Wilson warn, the biggest threat isn’t job loss—it’s skill stagnation. The MELDS framework reminds leaders to make learning a continuous process.

By applying MELDS, you can turn technological disruption into growth, purpose, and equity—anchoring AI transformation in humanity rather than machinery.

Fusion skills are the new vocabulary of success. They represent the blended capabilities that emerge when humans and machines work side by side. The authors outline eight of them—each transforming how you think, decide, and act in the modern workplace.

1. Rehumanizing Time

Instead of being slaves to machine schedules, you use AI to reclaim your time for meaningful tasks. At the University of Pittsburgh Medical Center, AI records patient dialogues so doctors can focus on empathy and decision-making. The result? Less burnout, more humanity.

2. Responsible Normalizing

This skill helps society accept AI safely. Audi, for instance, uses “piloted driving” campaigns to present autonomous cars as human collaborators, not replacements. CEOs must shape public narratives to reduce fear and increase understanding.

3. Judgment Integration

Even the smartest systems face ambiguity. Judgment integration is knowing when to step in. At Royal Dutch Shell, engineers oversee Sensabot robots remotely, applying human context when machines detect hazards. You learn to complement intelligence with judgment.

4. Intelligent Interrogation

Questioning AI becomes a new art. GE workers use digital twins to ask multi-level questions—technical, financial, and strategic. The better your questions, the better the insights. AI literacy becomes essential to leadership.

5. Bot-Based Empowerment

Individuals now wield fleets of digital assistants—from scheduling bots to career coaches like Wade & Wendy or Ella. These tools let you “punch above your weight,” automating drudgery so you can focus on creativity and strategy.

6. Holistic Melding

This is the mental and physical extension of human skills into machines, seen in AI-assisted surgery at Oxford or the intuitive coordination of BMW’s cobots. You don’t just use technology—you merge with it, developing intuitive flow.

7. Reciprocal Apprenticing

Humans train AI, and AI trains humans. IPsoft’s virtual assistant Amelia learns from human customer service reps while employees learn to manage and enhance the bot’s skills. Apprenticing builds confidence instead of fear.

8. Relentless Reimagining

This meta-skill is the lifelong discipline of redesigning work itself. Capital One’s innovation centers encourage employees to experiment and rebuild processes from scratch. Continuous reimagination keeps both people and organizations evolving.

Together, these eight skills redefine professional identity in the AI age. They transform fear into curiosity and automation into empowerment—the true hallmark of a human + machine mindset.

Ethics is not a side note—it’s the foundation of sustainable AI. Daugherty and Wilson insist that businesses must create systems that are explainable, fair, and accountable. This responsibility extends beyond compliance into culture.

Building Moral Frameworks

The authors highlight jobs like ethics compliance manager and AI safety engineer, whose roles mirror Isaac Asimov’s “Three Laws of Robotics”: prevent harm, obey human control, and protect existence responsibly. Quixote, a research prototype, learns ethics through stories—showing that machines can internalize cultural values.

Guardrails Against Bias

Biases in algorithms or data can have severe consequences—from unfair credit decisions to discriminatory job filters. Ethics managers and data hygienists cleanse data pipelines to ensure fairness. Daugherty and Wilson urge leaders to design AI with transparency and diversity in mind, following the EU’s General Data Protection Regulation (GDPR) and emerging IEEE standards.

Balancing Control and Accountability

A key insight: overtrusting AI can be as dangerous as undertrusting it. The authors describe “moral crumple zones,” where humans absorb blame for machine failures, as in ride-sharing mishaps. To avoid this, organizations must define clear lines of accountability between humans and algorithms.

Responsible AI isn’t just moral—it’s strategic. In a world of algorithmic decisions, companies that build trust and competence will win loyalty and innovation. Ethics, they remind us, is the new competitive advantage.

The concluding argument of Human + Machine centers on hope: the AI era can make work more human, not less. Daugherty and Wilson envision a world where technology amplifies creativity, compassion, and learning, provided that society invests in the right skills and policies.

The Skills Gap Challenge

Across major economies, millions of jobs remain unfilled—not because machines took them, but because people lack the training to work alongside them. Siemens plans thousands of new hires in robotics and software engineering, showing that AI creates jobs faster than humans can prepare for them. The authors argue that nations must invest heavily in upskilling and lifelong learning to bridge this gap.

Human-Centered Organizations

Successful firms like GE, Microsoft, and Amazon put people at the core of transformation. They cultivate continuous education, transparency, and purpose. Under the MELDS framework, human experience becomes the guiding metric—not just productivity or profit.

A Call to Action

The authors end with a moral and strategic imperative: reimagine your business and government systems now. Create policies around retraining, ethics, and inclusion. Discard “humans versus machines” narratives and adopt “humans and machines.” This mindset isn’t just futuristic—it’s vital for sustainable progress.

As Daugherty and Wilson conclude, “AI gives us superhuman capabilities so we can spend more time being human.” The task ahead is not automation—it’s imagination. Your future work begins with your willingness to collaborate and reimagine.