Enchanted Objects

What happens to your job, your company, and your future when machines can do almost everything you do—only faster, cheaper, and better? In What to Do When Machines Do Everything, Malcolm Frank, Paul Roehrig, and Ben Pring argue that artificial intelligence is not a threat to humanity—it’s an invitation to reinvent how we live and work. This book is less about robots taking over the world and more about how people can shape the new machine age into an era of prosperity and creativity. The authors make a bold claim: if you learn how to harness the power of intelligent machines, you can thrive; if you ignore them, you risk being left behind.

The authors structure their argument across what they call the AHEAD model, a five-part strategy for surviving and leading in the age of AI: Automate, Halo, Enhance, Abundance, and Discovery. These are not abstract ideas—they’re practical business plays that describe how companies across industries can reimagine processes, create new products, and find new sources of growth. Each of these plays captures a different way to coexist with machines: to offload rote work, to instrument everything, to amplify human performance, to create new markets through lowered costs, and to innovate continually.

The Context: A New Industrial Revolution

The book opens by placing AI within a larger historical context. The authors draw on economist Carlota Perez's theory that every industrial revolution follows a predictable pattern: an initial burst of innovation, a stall, and then a boom. Just as the steam engine, assembly line, and electricity transformed the human economy, AI represents the next wave—a fourth industrial revolution. They call the current stage the “digital build-out,” when the technology moves from the labs of Silicon Valley into the everyday infrastructure of society.

For decades, they note, people have feared automation. The Luddites smashed looms; John Maynard Keynes predicted “technological unemployment”; and Oxford researchers asserted that nearly half of U.S. jobs could be automated. But history’s pattern shows that automation ultimately leads to abundance rather than scarcity. When machines take over repetitive work, they free humans for higher-value tasks. In the authors’ view, the coming decade will produce vast new industries and career paths—those who can adapt will prosper.

Systems of Intelligence: The New Machine

At the heart of this revolution is what the authors call the “system of intelligence.” This system combines powerful software (algorithms that learn), hardware (connected devices and cloud computing), and data (the new fuel of the economy). These systems learn from experience—Google’s search algorithms, Uber’s ride-matching platform, Netflix’s recommendation engine—and get better the more data they consume. They’re becoming the invisible infrastructure behind most modern services. The authors even call these systems the new steam engines of our time.

The takeaway is clear: your company’s success no longer depends on scale or cheap labor; it depends on how effectively you build and apply systems of intelligence. If you can automate work, extract insights from data, and deploy intelligent systems, you’re competing at the “Google price” and the “Amazon speed.”

The Leader’s Dilemma and Opportunity

For business leaders, the authors see both promise and peril. They warn that “digital denial” is one of the biggest risks—believing your industry or company is immune to disruption. They note the fall of companies like Blockbuster, Borders, and Kodak, all of which clung to industrial-era models while digital competitors played a new game. In contrast, they highlight organizations such as GE, Nike, and McGraw-Hill Education that are embracing hybrid models—part physical, part digital—to create smarter machines, personalized experiences, and new business lines.

Importantly, the book rejects the binary choice between humans or machines. Instead, it calls for a partnership between the two: machines do the heavy lifting, humans provide creativity, empathy, and judgment. The future, the authors argue, belongs to the “enhanced worker”—someone who uses digital tools as intellectual exoskeletons to get smarter, faster, and more effective. Using real-world stories—from Betterment’s AI-driven financial advisory platform to Narayana Health’s near-free heart surgeries—they show how digital abundance can make products cheap, accessible, and high-quality at the same time.

Why This Matters to You

Whether you lead a company, a team, or simply your own career trajectory, this book argues that AI is too big to ignore and too powerful to fear. You can either shape the new machine or be shaped by it. The authors frame the next twenty years as a mirror of past industrial eras: first automation, then personalization, then abundance. They call this the greatest management opportunity of a generation—a chance to rethink everything from pricing to customer experiences to innovation itself.

Ultimately, What to Do When Machines Do Everything is a field guide to adaptation. It teaches you to see intelligent machines not as replacements, but as collaborators—digital colleagues that amplify your humanity. If you can learn to Automate, build Halos of data, Enhance human capability, create Abundance, and Discover new futures, you won’t just survive the machine age—you’ll lead it.

Frank, Roehrig, and Pring remind you that automation—though often viewed as a threat—has always been the engine of progress. From factory robots to software bots, automation replaces repetitive tasks and triggers the cycle of creative destruction described by economist Joseph Schumpeter. Every revolution begins with fear and ends with productivity.

Understanding the Automation Imperative

The authors emphasize that automation is not optional. Companies that don't adopt automated processes will incur what they call a “laggard penalty” and lose competitiveness, just as manual fabric makers lost to power looms. They show how automation moves from “back-office” functions to “front-office” experiences—machines now write news stories, adjudicate insurance claims, and even analyze X-rays. The Associated Press, for instance, publishes tens of thousands of AI-written sports stories each year through Narrative Science software.

Pick the Right Targets

The guidance is practical: find processes that are repetitive, rule-based, and high-volume. In health insurance, TriZetto uses software bots to process claims with nearly 90% fewer manual touches. In banking, robotic process automation from companies like Blue Prism handles compliance and risk alerts. These examples embody what the authors call the 25%-25% rule: aim for a 25% cost reduction and a 25% productivity gain to ensure automation is worthwhile.

Overcoming Organizational Resistance

Still, the biggest enemy of automation is human inertia. Middle managers—the “brass wall”—often block change to protect their jobs. Here, the authors borrow from military strategy, advising managers to start small, target weak points, and win quick victories to build momentum (similar to Stonewall Jackson’s battle philosophy). Automation should begin with a pilot process, then scale across departments.

Automation, they conclude, is the fuel for every other digital innovation. Once you automate the transactional core of your business, you free resources for creativity, personalization, and discovery—turning cost savings into the seed capital for tomorrow’s breakthroughs.

In the authors’ framework, data is the raw material of the digital age—more valuable than oil, because unlike oil, it’s infinite and grows more useful with every use. When you instrument people, products, and processes, you create what they call Code Halos, the digital twin of every “thing” that emits data. These halos are the foundation of personalization and intelligence.

Instrument Everything

The authors describe instrumentation as turning every noun—person, place, or thing—into a code generator. From GE’s sensor-packed locomotives to Nike’s connected sneakers, companies are discovering that “dumb” objects become powerful once they generate data. GE’s Tier 4 train, equipped with 200 sensors, can predict maintenance needs and optimize speed and fuel efficiency, turning industrial hardware into rolling data centers.

Knowing It All

Building Code Halos leads to what the authors call the “Know-It-All business.” With real-time data, you can make better decisions, anticipate customer needs, and remove guesswork. Discovery Health in South Africa illustrates this: facing universal pricing regulations, it reinvented health insurance by rewarding healthy behavior. Members who shared fitness data received discounts and rewards. The result: rising profits, lower medical costs, and healthier customers.

From Data to Trust

Of course, more data means more risk. The authors warn against the “dark side of the halo”—data breaches and privacy violations. They urge firms to treat transparency and control as design principles, allowing customers to delete data or see what’s known about them. Data, they argue, is the fuel of trust; those who manage it well will thrive, while those who treat it carelessly will lose credibility and customers.

Rather than fearing job loss, Frank, Roehrig, and Pring show how technology will enhance human ability—not replace it. They describe the enhanced worker as someone equipped with digital tools that act like an intellectual exoskeleton. Just as GPS systems revolutionized driving, AI assistants will transform work in teaching, medicine, law, and beyond.

Real Examples of Enhancement

In education, McGraw-Hill’s ALEKS platform uses adaptive AI to identify what each student knows and doesn’t know, helping teachers focus on mentoring instead of grading. CDO Stephen Laster explains that ALEKS “frees the teacher to think more deeply” and “teaches the teacher about the student.” In healthcare, Dartmouth-Hitchcock’s ImagineCare system monitors patients remotely, predicting heart failure before it happens. In both cases, humans aren’t sidelined—they’re empowered.

The Human Edge

The authors urge you to double down on being human. As machines take over routine work, empathy, communication, and creativity become more valuable. Frontline employees can finally shed the burden of rote tasks. The book spotlights Pret A Manger and Zappos, which automated transactions so staff could focus on authentic human service. Apple did the same by replacing checkout counters with roaming “Geniuses.”

Build Your White-Collar Exoskeleton

The idea of a “digital exoskeleton” borrows from physical robotics—like Panasonic’s lifting suits—but applies to mental work. Companies like Palantir give analysts data visualizations that amplify judgment, while IBM’s Avicenna partners with radiologists to increase diagnostic precision. The authors’ conclusion: in every field, machines will do the math, but humans will make meaning.

Perhaps the most inspiring chapter explores how machines democratize access. The authors argue that AI leads to abundance—markets where formerly expensive services become affordable and widespread. Like Henry Ford’s assembly line, digital systems can drop price points by 100x and open new markets.

The Economics of Abundance

Abundance follows the basic law of demand: as prices drop, demand rises. AI lowers costs not just in digital industries but even in highly physical ones. The story of Dr. Devi Shetty’s Narayana Health hospital in India proves it: by digitizing surgical and monitoring processes, they reduced the cost of heart surgery from $100,000 to $1,200 while maintaining world-class outcomes. Harvard Business Review called Shetty the “Henry Ford of heart surgery.”

Finding Your Abundance Opportunities

The authors suggest seven approaches to discovering abundance: obsess over start-ups, invite teams to “kill your company,” play the ‘Tomorrow It’s Free’ game, manage the innovator’s dilemma, make like a maker, think like a corner shop (personalization), and apply Digital Taylorism. Each encourages radical thinking—imagining what happens if your top product costs 90% less in five years. Amazon and Uber are already playing this game.

Digital Taylorism: Old Ideas Reborn

Echoing Frederick Taylor’s early 20th-century push for efficiency, the authors call for applying data analytics to knowledge work. Measuring and refining performance in real time can yield the same productivity leaps that manufacturing achieved a century ago. The takeaway: abundance requires rigor in process design, not chaos. Today’s sensors and algorithms are the stopwatches of the digital age.

Once automation, data, and abundance take hold, discovery becomes possible—the stage where innovation flourishes. The authors use the story of Edwin Budding’s lawn mower to show how small inventions can spawn entire industries. One humble tool created modern sports, just as AI will create unimaginable job categories and markets in the years ahead.

R&D Without AI Is No R&D

Traditional innovation, they argue, relied on intuition and brainstorming. Now, smart systems turn R&D into a data science. Netflix, for example, uses machine learning to identify viewer preferences globally—finding that 19-year-old men and 70-year-old women share surprising tastes. This “velocity and scale of AI” accelerates discovery tenfold over traditional research cycles.

Balancing Big Bets and Small Moves

Discovery requires both courage and portfolio thinking. Borrowing from venture capital, the authors urge leaders to “let hits pay for misses.” Most experiments will fail—but the few that succeed can redefine industries. Toyota’s simultaneous investment in driverless cars and assisted driving represents smart hedging. Leaders should back both short-term digital “kaizen” (continuous improvement) and long-term moonshots like blockchain or quantum computing.

Don’t Short Human Imagination

Above all, they insist on believing in curiosity—the defining trait of human intelligence. Machines may learn, but humans imagine. Discovery thrives when managers protect experimentation and remove bureaucratic clutter—what Marie Kondo did for closets, companies must do for legacy systems. Innovation, they argue, is less about brainstorming and more about cleaning house so new ideas can breathe.

The conclusion calls for pragmatism and courage. The authors contrast two camps—the utopians who expect miracles from AI, and the dystopians who fear societal collapse. They advocate a third position: pragmatic optimism. Machines will do more, yes, but humans will remain central if we learn to partner with code.

The Digital Build-Out

Between 2015 and 2040, the “digital build-out” will remake every industry: finance, health care, infrastructure, education, and government. Companies that align the Three M’s—materials (data), machines (intelligence systems), and models (new ways to monetize)—will dominate. Leaders must move AHEAD now: Automate, Halo, Enhance, Abundance, Discovery.

Human Courage and Innovation

The authors close with stories of organizations and thinkers betting boldly on the future: General Motors launching its Maven car-sharing service, Sony composing music with AI, and scientists teleporting quantum particles. These are “messages from the future,” glimpses of how fast things are changing. The moral is simple—don’t short human imagination; don’t wait for perfect clarity; don’t debate endlessly. Act.

For the authors, competing on code is humanity’s next stage of progress. By merging curiosity with computation, we can create not just smarter machines, but a smarter world. The only question left is personal: in an era when machines do everything, what will you choose to do?