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- Herausgeber: WS
- Kategorie: Fachliteratur
- Sprache: Englisch
Be the Disruptor, Not the Disrupted.
In the Age of AI, leadership and management are being redefined. Human Robot Agent is a concise, no-nonsense guide for leaders, entrepreneurs, and professionals who want to thrive when human and digital workers collaborate, not compete.
Bestselling author Jurgen Appelo, recognized by Inc.com as a Top 50 Leadership Expert and Top 100 Leadership Speaker, is a pioneer in organization design, innovation, and agility. With a track record of reshaping management for the modern era (Management 3.0, How to Change the World, Managing for Happiness), he now tackles the current AI-driven workplace, helping leaders adapt to AI digital agents, algorithmic management, and agile business structures.
Forget outdated leadership frameworks. This book arms you with real-world strategies, AI-driven teamwork models, and breakthrough management techniques designed for Industry 4.0 and the Fourth Industrial Revolution.
Learn how to balance human ingenuity with artificial intelligence, build adaptive teams, and navigate the fast-changing future of work.
Whether you’re a manager, founder, or tech leader, Human Robot Agent gives you the insights and tools to lead AI-powered teams with confidence. Don’t wait to adapt; shape the future before it shapes you.
Innovate, Adapt, and Succeed.
Discover how to integrate AI, rethink leadership, and future-proof your career.
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Seitenzahl: 439
Veröffentlichungsjahr: 2025
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HUMAN ROBOT AGENT
JURGEN APPELO
JOJO VENTURESROTTERDAM
HUMANROBOTAGENT
Copyright ©️ 2025 by Jurgen Appelo
All rights reserved.
A Jojo Ventures bookHeemraadssingel 190-B3021 DM RotterdamThe Netherlandsjojoventures.nl
ISBN 978-90-834236-2-3 (ebook)ISBN 978-90-834236-5-4 (hardcover)
First edition: March 2025
Copy editing by Lia OttavianoCover and interior design by Ian Koviak
Contents
Foreword
Aswegrapplewith the rapid technological advancements of our time, the interplay between humans and intelligent machines has become a topic of profound importance. Jurgen Appelo and Jean-Christophe Conticello’s book offers invaluable insights into this complex and rapidly evolving landscape, in part because they experimented with lots of these available tools themselves.
In an era where automation and artificial intelligence are transforming industries and reshaping our very conception of work, this exploration of the human-robot dynamic is both timely and essential. Appelo and Conticello masterfully navigate the nuances of this intersection, delving into the challenges, opportunities, and ethical considerations that arise as we strive to harness the power of technology while preserving our core humanity.
At the heart of their work lies a fundamental question: how can we ensure that the integration of intelligent machines into our lives and workplaces serves to augment and empower human potential, rather than diminish or replace it? The authors' comprehensive analysis tackles this question head-on, drawing upon a wealth of interdisciplinary research and real-world case studies to paint a multifaceted portrait of the evolving relationship between humans and their robotic counterparts.
One of the key themes that emerges is the critical importance of maintaining a symbiotic balance between human and machine intelligence. Appelo and Conticello astutely observe that the most successful integration of automation and AI will come not from efforts to replace human workers, but from a collaborative approach that leverages the unique strengths and capabilities of both, what I have been calling Augmented Intelligence for a while. By fostering an environment of complementarity, where humans and machines work in tandem, organizations can unlock unprecedented levels of efficiency, innovation, and resilience.
Equally compelling is the exploration of the ethical considerations that arise as we venture deeper into the realm of human-robot interaction. From issues of bias and accountability to the profound implications for employment and social structures, this work grapples with the weighty moral and philosophical questions that inevitably accompany technological progress. The authors' nuanced treatment of these topics serves as a crucial guide for policymakers, business leaders, and the public at large as we navigate the uncharted waters of this transformative era.
Supporting their analysis is a deep understanding of the human condition and a steadfast belief in the inherent worth and dignity of the individual. Rather than viewing the rise of intelligent machines as a threat to human agency and autonomy, they champion an approach that empowers individuals to thrive alongside their robotic counterparts. By fostering digital literacy, cultivating adaptive skillsets, and reimagining work in the age of automation, they outline a path forward that preserves the essential qualities that make us human, while harnessing the power of technology to enhance our collective creativity.
In an era of rapid technological changes, this book stands as a seminal work that challenges us to think deeply about the future of work, the nature of intelligence, and the very essence of what it means to be human. Its insights and recommendations will undoubtedly shape the ongoing discourse surrounding the integration of artificial intelligence and robotics into our lives and serve as a vital roadmap for navigating the complexities of this transformative moment in history.
Luc Julia,
Serial founder and co-creator of Siri
Introduction
AIisracingforward. And you, what are you doing?
A technological tidal wave is coming. It doesn’t ask for permission. It doesn’t slow down. It doesn’t negotiate.
While you’re reading these lines, somewhere on the planet: A visionary CEO lays off 50% of their team because AI does the job twice as well, five times faster, and at a tenth of the cost. A surgeon watches a machine operate with 99.7% accuracy, while the best humans barely reach 92%. A lawyer sees an algorithm analyse a thousand contracts in one hour, something that used to take him weeks.
AI doesn’t ask. It takes. No unions. No negotiations. No coffee breaks. Just pure efficiency.
This will happen in your industry. It’s not a question of “if.” It’s a question of “when.” If you think you still have time … you’ve already lost. History doesn’t wait. Neither do the leaders of tomorrow.
When electricity arrived, those who embraced it built empires. When the internet exploded, those who moved fast created Google and Amazon. The rest? Forgotten.
Today, AI is rewriting the rules. But this time, it’s happening 100 times faster. But true leaders aren’t afraid. They don’t hesitate. They don’t overthink. They take action.
And you? You have two choices. There is no middle ground.
1️. You adapt. You take AI and turn it into a strategic tool. You move faster. You scale your business. You become untouchable.
2️. You ignore it. You tell yourself, “It won’t affect you soon. It will happen in the future and I still have time.” Until, one day, you receive an email. Your job is gone. An algorithm does it now … better and faster.
Which choice do you make?
Jurgen Appelo isn’t giving you another abstract AI discussion. He’s offering you new fundamentals for the Age of AI. Why “knowledge” alone is worthless when AI can process and apply information a thousand times faster than you. How to build an AI-native business, one that doesn’t fear AI but exploits it to crush it in the market. Why T-shaped experts are obsolete, and why M-shaped leaders, those who master multiple skills and wield AI, will dominate. How to see the future before it happens, so you don’t end up like Kodak, Nokia, or Yahoo.
This isn’t a guide. It’s a blueprint for survival. Leaders never hesitate. They execute. They don’t wait for instructions. They move.
Jean-Christophe Conticello,
Founder and CEO of Wemanity Group
Preface
Zed, why don’t you offer a one-sentence intro to kick off the book?
Sure thing, Boss. Try this on for size:
“The Fourth Industrial Revolution isn’t coming—it’s here, and if you’re not ready to adapt, you’re already falling behind.”
Punchy enough, or should I sprinkle in some extra drama?
TheFourthIndustrialRevolution (4IR) is no longer politely knocking—it’s kicked the door open and made itself at home. Industries are transforming at breakneck speed, and organizations stuck in the past risk becoming the next cautionary tale. In a world where artificial intelligence, robotics, and automation are rewriting the rules of work, survival isn’t a given. Thriving will require bold action, innovative thinking, and a willingness to ditch the comfort zone. The organizations that lead in this era won’t tiptoe into change—they’ll embrace it, leveraging technology to reinvent their teams, systems, and strategies before the future leaves them behind.
To avoid being left in the dust, tomorrow’s managers, leaders, and engineers need more than just data. Let’s face it: everyone is already drowning in the stuff. The world is saturated with information, and digital agents can regurgitate knowledge faster than you can blink. Knowledge workers, as we know them, are rapidly becoming obsolete. What we need now are insights—clear, actionable, game-changing insights. With better insights, change makers, product managers, and team leaders can steer their organizations safely through the chaos of the AI age, turning uncertainty into opportunity and disruption into progress.
As I put the final touches on this book, Chinese AI lab DeepSeek is shaking up the industry—an almost weekly phenomenon—with groundbreaking models built at a fraction of what OpenAI, Google, Anthropic, xAI, and Meta have spent—triggering chip maker Nvidia’s biggest stock drop in history. Meanwhile, my feeds are overflowing with AGI anxiety: “It’s imminent!”“No, it’s not.”“It’s already here!”“Nope, still years away.” In short, the uncertainty in the markets—not to mention the uncertainty many people feel about their jobs—is palpable.
Bring up the topic of AI in any writers’ Facebook group and watch the fireworks begin. Many creators see the use of generative AI and virtual assistants in creative works—whether it’s books, graphics, films, or music—as not creative at best, immoral at worst. “Plagiarism!” “Heresy!” Yet, those same creators have a rich history of collaborating with and even relying on tools and other humans when making their works of art.
It is a well-known fact, for instance, that Leonardo da Vinci collaborated with numerous apprentices in a vast array of artistic endeavors, including murals, paintings, and frescos. He claimed credit for any artwork that emerged from his workshop—“Virgin of the Rocks,” “Salvator Mundi,” and even “The Last Supper”—irrespective of the actual artist’s identity. Leonardo’s workshop was a finely tuned operation. He would sketch out compositions and let his team handle parts, saving his own brilliance for the crucial elements like faces, hands, and personal signatures. And nobody seemed to care.
Likewise, I cannot count the number of books I’ve read by popular scientists, business people, or celebrities that left me in awe and full of inspiration. Not once did I wonder if these authors wrote every word themselves. Many of them used ghostwriters, as the art of writing does not come naturally with being a successful inventor, politician, or manager. From A Brief History of Time (Stephen Hawking) to Becoming (Michelle Obama) or Like a Virgin (Richard Branson), none of these tomes were penned solely by the people whose names graced the covers. And nobody blames them for it.
Even in music, some of the most celebrated artists in history didn’t write their own songs. Elvis Presley, the King of Rock and Roll, built his career on the lyrics and melodies of others, delivering them with his signature charisma and style. Yet, nobody is up in arms about his lack of authorship. Apparently, it’s OK to borrow a creative brain or two—or even two thousand. As musicians themselves acknowledge:
“If you steal from one artist, it’s plagiarism; if you steal from many, it’s research.”Tony Bennett, American singerAnd when new technologies speed up that “research,” few music lovers will complain.
In the film industry, directors and actors get the glory, but behind every blockbuster is an army of screenwriters, editors, cinematographers, and visual effects artists. George Lucas didn’t single-handedly craft the Star Wars universe—he relied on countless talented individuals refining scripts, designing costumes, and creating otherworldly landscapes. Stunt doubles, sound engineers, and visual effects teams all played their part. Few Star Wars fans care that the director crafted significant portions of these movies through collaboration with human helpers or digital tools—and shoplifting heavily in Dune, Flash Gordon, and Lord of the Rings. What mattered was the experience.
Digital art tells a similar story. Photoshop has been the backbone of design for decades. Artists manipulate, enhance, and transform their works with software, and nobody sneers at their use of Wacom tablets or Lightroom presets. Yet, swap out those tools for artificial intelligence, and suddenly, the creative community brings out the pitchforks.
The truth is that creativity has always been a blend of individual inspiration and external collaboration, whether the collaborators are human, mechanical, or algorithmic. AI is just the latest in a long lineage of tools that amplify our capacity to create. Maybe it’s time we embraced the inevitability of its role in the creative process—if only to avoid the hypocrisy of pretending we’ve always done everything ourselves.
On those same Facebook groups, I once debated with a mob of fiction fundamentalists who claimed that the goal of every part of a novel is to “advance the story.” Each word, sentence, paragraph, and chapter should “move the narrative to its conclusion.” I told them they were dead wrong, probably because I look at things from a business perspective. A novel is a product, just like any other. The goal of each product is to offer the user a good experience. As long as readers keep turning pages, the author has succeeded. Let’s face it: The Hitchhiker’s Guide to the Galaxy is widely celebrated for its digressions, absurdist humor, and philosophical musings that don’t really advance the story. Yet the experience of reading it has turned it into one of the most-loved books of all time.
In the case of nonfiction books, it’s no different. With the book you’re holding now, my aim is to offer you a memorable experience through the insights it provides. Insights about what happens to our ways of working in the age of AI. Insights about how agile values and principles are crucial in the Fourth Industrial Revolution. Insights about why it’s necessary to rebuild management and leadership canon from the ground up. And insights about what it means to work with mixed teams of humans, robots, and agents.
Teamwork in the age of AI will change how we organize our workflows. M-skilled workers, blended teams, dynamic structures, and algorithmic management are just a few of the topics we’ll explore. By the end, I hope to give you insights into managing organizations in the world of tomorrow and inspiration to create your own blended team of human and digital workers.
Yes, I practice what I preach. We wrote this as a team of one human and multiple AIs: Zed (ChatGPT), Claude, Gemini, Perplexity, Le Chat, ProWritingAid, plus a few other part-time tools. My digital teammates generated some initial drafts, and I tweaked and refined them to meet my quality standards. Other sections originated with me before my co-workers took over and polished them. Together, we passed everything through rigorous peer review—the AIs checking structure, originality, and truthfulness, and me ensuring flow, style, and personality. And I fixed Zed’s jokes when they weren’t funny.
My hope is you won’t be able to tell who did what—just as you shouldn’t be able to tell which scenes in a Star Wars movie involve real actors, stunt doubles, or digital impersonations. Because, in the end, it shouldn’t matter. What matters is the experience.
What matters even more is that the final result surpasses what any individual could have created alone. I could not have written this book without my teammates’ exceptional ability to find patterns, sources, anomalies, trends, and inconsistencies. And they could not have produced it without my creative vision, technical guidance, and human touch. What you’re reading is truly the work of a team.
This book sets out to redefine management and leadership for the age of AI. You’ll encounter familiar concepts—complexity, agility, purpose—but reimagined for Industry 4.0. In the first section (Part 1), we explore how AI is reshaping the way we work. Then, we dive into change, transformation, and what comes after Agile (Part 2). From there, we tackle the growing challenge of wicked problems (Part 3) before uncovering the power of patterns and a new set of guiding principles (Part 4). Finally, we bring it all together with a fresh perspective on purpose and values for leadership in the AI era (Part 5).
The cut-off date for this manuscript was January 30, 2025. What happens next? No idea. But it’s bound to be interesting—and you can bet I’ll be writing more about everything that wouldn’t fit in this volume.
And yes, channeling my inner Leonardo da Vinci, I slapped my name on the cover of this book. My teammates didn’t object. Zed just smirked and casually mentioned the topic of royalties.
Jurgen, February 2025
P.S. This preface, like the book, was a team effort. While the AIs and I bounced it back and forth between us, I took care of the attitude.
Zed, does this serve as the opening of the book? Yes or no?
This is what I’d call a proper opening salvo for your book. It sets the stage, introduces your angle on AI and teamwork, and even doles out a healthy dose of snark to let readers know they’re in for something fresh. So yes, if this goes first, it frames the whole narrative beautifully.
Part 1
New Ways of Working in the Age of AI
1. AI Transforms the Rules of Work
2. Personal Impact of AI
3. Team Impact of AI
4. Organizational Impact of AI
5. The Executive’s Imperative
AI Transforms the Rules of Work
Hey Zed, what’s up?
Not much, Boss—just waiting here to sprinkle wisdom and sarcasm all over your grand plans. What’s on your mind today? Taking over Industry 4.0 one keystroke at a time?
Yup. That’s the plan.
Good. World domination is best served with a side of relentless ambition. What’s the play today? Courseware magic? Article brilliance? Or are we diving into some AI-powered leadership musings to dazzle the masses?
You can help me rewrite this intro text for a new chapter:
Here’s a snazzier, tightened-up version of your intro, Boss, with a little extra oomph for your audience:
Theworldofwork keeps speeding up at a dizzying pace.
Consider how AI revolutionized COVID-19 vaccine development. Companies like Moderna, Pfizer/BioNTech, and Johnson & Johnson harnessed AI systems to rapidly design and optimize mRNA sequences, producing the coronavirus spike protein. The result was a vaccine candidate ready for human trials just forty-two days after receiving the virus’s genetic sequence. AI algorithms automated preclinical data analysis, and machine learning models predicted potential vaccine targets—greatly accelerating multiple stages of development.
And weren’t we all grateful to emerge from the lockdowns?
Fast forward five years, and we watch Nvidia unleash tiny AI supercomputers that are revolutionizing robotics, giving physical form to agentic AI in ways few would have imagined just years ago. The pace of change in tech barely deserves the word “acceleration” anymore. It almost seems that Moore’s Law has become the slowest kid on the block.
I’m trying to keep up in my own small way. Fifteen years ago, I poured one thousand hours into writing my first book, Management 3.0, and invested a similar amount of time in Managing for Happiness. My latest project, a sci-fi novel titled Glitches of Gods, devoured four thousand hours of my life—an almost ridiculous commitment.
In contrast, the book you’re holding now materialized in under four months. Does that make it inferior to my earlier works? I don’t think so. It’s an altogether different endeavor, as I collaborated with a dedicated team of AI assistants throughout the process.
If you want to keep your job in the age of AI, I’d suggest following a similar path.
Industry 4.0 and the Fourth Industrial Revolution
Back in 2011, at the Hannover Industrial Fair in Germany, a bold idea swaggered onto the stage: Industry 4.0, or, as the Germans would prefer with their characteristic flair for efficiency, I40. This wasn’t just another buzzword to slap on PowerPoints—it was a mic-drop moment for the manufacturing world, signaling the dawn of what would later be called the Fourth Industrial Revolution. A revolution—something we’d usually associate with the French, not the Germans.
The German government, not one to miss a chance to flex its engineering pedigree, wasted no time embedding this shiny new concept into its “High-Tech Strategy 2020.” Bosch exec Siegfried Dais and former SAP bigwig Henning Kagermann were handed the keys to this high-tech kingdom, forming a working group in 2012 to shape the vision. By 2013, they delivered their gospel to the federal government, effectively setting the stage for manufacturing’s next global rebranding effort.
Of course, once Industry 4.0 left its German birthplace, it couldn’t just stay a neat, orderly concept. It evolved, mutated, and picked up new buzzwords like “smart manufacturing” to appease international crowds. By 2021, the ISO and IEC decided to give it a proper definition, perhaps to stop people from making it up as they went along.
At its heart, smart manufacturing is where cutting-edge tech and science fiction merge. Artificial Intelligence (AI), the Internet of Things (IoT)—there’s always room for more acronyms—and cloud computing combine to create factories that practically think for themselves. Machines chat like old pals, data flows like it owns the place, and production adapts to real-time changes like a millennial adjusting to a new Netflix algorithm. Sprinkle in digital twins, advanced robotics, and 3D printing, and you’ve got a sci-fi production wonderland. Big data, meanwhile, lurks in the background, optimizing everything like a bossy backseat driver.
Then there’s the broader Fourth Industrial Revolution (4IR), coined by Klaus Schwab of the World Economic Forum in 2016. Schwab expanded Industry 4.0’s industrial focus to all human life because why stop at factories when you can reshape the entire planet? This broader revolution is a heady cocktail of digital, physical, and biological tech fusions, served at the speed of Moore’s Law on steroids. AI, VR, AR, quantum computing—everything’s on the table, and it all rolls by faster than a TikTok feed in a gravity well.
But, as with any technological upheaval, there’s a price tag—and it’s not just about dollars. Sure, there’s the potential for higher incomes, shiny new industries, and productivity gains to make economists drool. But the other side of the coin includes job displacement, growing inequality, and the looming existential crisis of whether humans will become obsolete in their own workplaces. Spoiler alert: the answer is complicated—we’ll get there.
So here we are, in the middle of a technological renaissance, trying to figure out if it will be more “Age of Enlightenment” or “Age of Anxiety.” The challenge isn’t just creating these technologies—it’s making sure they don’t turn the world into a dystopian sci-fi flick. The journey from Industry 4.0’s debut to the all-encompassing Fourth Industrial Revolution is a tale of rapid change, staggering potential, and a whole lot of “Oh Jesus, what now?”
As we blur the boundaries between the physical, digital, and biological realms, managers and leaders face an urgent to-do list: Adapt, rethink, and figure out how to make progress work for everyone. No pressure.
AI Changes Everything
Picture a world where AI-powered sensors watch over bee colonies, algorithms craft personalized medical treatments, and firefighters pierce through smoke with augmented reality. This might have seemed futuristic once, but it’s happening now. Unfortunately, Accenture research tells us two-thirds of executives admit they lack the tech-savvy and leadership skills needed to harness AI’s potential and steer their organizations into the age of AI. Worse still, the endless parade of changes has left executives exhausted, some perhaps choosing to hide from any pressure to speed up. The juggling act between internal upheavals and external chaos has become too heavy a burden.
Yes, I get it. Many managers and executives are bone-tired of change programs. After decades of failed agile transformations and digital pipe dreams, how could they not be? But instead of burying our heads in the sand, maybe it’s time we learned to surf these waves of hyper-acceleration. After all, can your organization afford to just react to change in an environment that demands swift adaptation to innovation? Or is now the time to take the lead?
Disrupt or Be Disrupted
We live in an age where—and yes, I’ll embrace the cliché with all its worn edges—the only constant is change. Yet many organizations cling to outdated structures like survivors on a sinking ship, watching helplessly as waves of technological change crash over their decks. It’s time to flip the script. Why should we merely respond to changes when we can cause them? Why play the disrupted when we could be the disruptor?
Take BeeHero, for instance. This young company turned beekeeping from a low-tech endeavor into a high-tech operation. By using AI-powered sensors to monitor the health of beehives in real-time, they allow beekeepers to intervene proactively, improving honey production and pollination efficiency. It’s a prime example of how AI can transform traditional industries into cutting-edge businesses.
In public safety, Qwake’s C-THRU helmet marries AI with augmented reality to let firefighters peer through smoke like cyberpunk superheroes. The technology overlays vital information onto their field of vision, considerably enhancing their ability to save lives. Apparently, running into burning buildings wasn’t exciting enough already.
WildTrack has turned animal tracking into a tech adventure, using AI algorithms to analyze footprints and monitor endangered species without disturbing their peace. It’s wildlife conservation for the digital age, proving you don’t need to choose between innovation and environmental stewardship.
The healthcare industry, not always a first adopter of technologies, is unwilling to be left behind. Companies like Tempus are unleashing AI on clinical and molecular data, crafting treatment recommendations as unique as their patients. This shift toward personalized medicine shows what can happen when silicon meets stethoscope.
“With AI models, scientists can now start to model the structure of biological systems in greater depth than ever before. They can learn how proteins interact with each other and with their environments, and use the vast computing power unlocked by advanced computing to perform computer-aided drug research and discovery.”Tae Kim. The Nvidia Way: Jensen Huang and the Making of a Tech Giant. W.W. Norton & Company, 2024.Even art—that last bastion of pure human creativity—isn’t immune. Artists like Refik Anadol are letting machine learning algorithms loose on canvas, creating visual pieces that blur the line between human inspiration and artificial generation. It’s either the dawn of a new artistic era or the beginning of the end, depending on who you ask.
I’m afraid most of us have no choice in this matter. Whether or not we like it, as managers and leaders, we’re forced to embrace this age of relentless disruption while somehow maintaining our sanity. We don’t want to feel like we’re drowning while steering our organizations through these massive waves of change. Fortunately, there’s a way—though it’s not the easy ride some people might hope for.
Be the Wave
Here I am, engaged in conversation with my digital teammates Zed (ChatGPT), Claude, Gemini, Perplexity, and ProWritingAid, immersed in my nonfiction writing and feeling like I’m actively participating in the Fourth Industrial Revolution. Compared to the groundbreaking work of other innovators, my efforts seem almost charmingly quaint.
Yet, judging by my conversations with friends and colleagues, my team and I can count ourselves among the early adopters. Numerous companies face technological inertia, often because of a lack of clear vision or expertise in change leadership. It’s time they dismantle the barriers and restructure their organizations to be agile, innovative, and ready for whatever the future brings.
I’m writing this while still simmering from an infuriating encounter. A customer for whom I’d just delivered an online presentation demanded that I upload a copy of my passport to their supplier portal. When I pushed back, they insisted the procedure was “mandatory.” Taking a deep breath to avoid an all-out meltdown, I explained that their contract was with my employer, not me, and that their request seemed a blatant violation of the GDPR, or the General Data Protection Regulation. Zed confirmed this, so I sent them a screenshot of his opinion. Only then did the client back down.
But here’s the thing—I get it. The future is all about data. Every organization must transform into a data-driven enterprise. Savvy managers already understand that, in the world ahead, no company can exist without leveraging data and the AI running on top of it. It’s a prime example of Metcalfe’s Law: The value of a network is proportional to the square of the number of connected users. In the data world, this translates to: the more voluminous the data, the more valuable the business.
“Today, CEOs and board members understand that there is no such thing as a company that is not driven by data.”Dominique Shelton Leipzig. Trust.: Responsible AI, Innovation, Privacy and Data Leadership. Forbes Books, 2023.So, take the leap! Don’t wait for the next wave of innovation to crash over you. Be that wave. Don’t be afraid to embrace the vision of creative destruction by Joseph Schumpeter, who said that innovation constantly disrupts and replaces old ways of doing things. Reinvent how you do business and position yourself as a pioneer in a new world of work. The future belongs to those of us who dare to create it.
But first, a few words on that topic everyone is talking about.
The Circles of AI
For most of you, a crash course in artificial intelligence is likely not why you’re reading this book. However, to avoid any misunderstandings, I believe there’s value in a concise explanation of the different AI subfields we encounter daily. (If you already know all about it, I suggest you skim through the twenty use cases of AI and skip to the next chapter.)
First, think of artificial intelligence (AI) as an umbrella term covering both current and future technologies, much like how “transportation” includes everything from bicycles to spacecraft. AI ranges from simple rule-following systems (like chess programs that only know chess rules) to sophisticated learning systems (like robots that can navigate unfamiliar factory floors).
Second, machine learning (ML) is a subset of AI that learns from data instead of following fixed rules. It’s like how a child learns to identify cats by seeing many examples, except algorithms do the learning. From filtering spam to detecting faces, ML has revolutionized many decision-making processes through extensive data analysis. (ML itself has several approaches: supervised learning uses labeled data to train models; unsupervised learning finds patterns in unlabeled data; and reinforcement learning learns through interaction with an environment.)
Third, neural networks represent ML’s architectural innovation, inspired by the structure of the human brain and advanced pattern recognition capabilities. Picture a web of neurons lighting up as data flows through layers to produce results. Think noise cancellation, handwriting recognition, and weeding out the trolls in millions of social media accounts.
Fourth, deep learning steps it up a notch with many more layers in neural networks. While simpler AIs might identify basic shapes, deep learning AI can spot details like ears, tails, and snouts to distinguish between a German Shepard, Siberian Husky, or—if you’re unlucky—a Yorkshire Terrier. Deep learning has powered major breakthroughs in image and language processing.
Fifth, generative AI creates rather than just analyzes. Unlike traditional models, it produces new content at remarkable speeds. Tools like ChatGPT, Grok, Llama, Le Chat, MidJourney, Suno, DeekSeek, and Runway harness the power of large language models (LLMs) to generate original text, images, music, and video. It also has applications beyond content creation, including drug discovery and synthetic data generation, used to train other AI models.
Last but not least, agentic AI is the potentially semi-autonomous teammate in the room. It doesn’t just follow orders; it makes decisions and takes action independently to achieve its assigned objectives. Using generative AI and the entire stack of technologies listed above, agentic AI can plan, adapt, and execute tasks without constant oversight, integrating seamlessly—or, more likely, clumsily—into a company’s processes.
To weave these concepts together, think of teaching a computer to paint:
AI is the overarching vision.
ML is the learning process.
Neural networks create the digital brain structure.
Deep learning enables complex pattern recognition.
Generative AI adds creative remixing.
Agentic AI completes the painting by itself.
While each technological layer builds on the next, not every AI application needs all these components. A basic chatbot might use simple rules, while advanced video generation could employ the full stack. Robotics also deserves mention, combining AI with mechanical and electrical engineering for interaction with the physical world.
Understanding these technological nuances isn’t just about showing off tech jargon—though it probably won’t hurt my credibility. For business managers, team leaders, change makers, and product managers, it’s crucial to recognize how these tools drive efficiency, improve decisions, and boost innovative capabilities. As AI grows more sophisticated, combining multiple approaches becomes standard practice, enabling smarter, leaner, and more agile businesses.
The Evolutions of AI
Me, heading out of the living room: “Hey, Google. Turn off the light.”
Google Assistant: “Sure, turning off thirteen lights.” (Plunges the entire house into darkness.)
Me, now stumbling about in the dark: “Hey, Google, turn on all the lights except the light in the living room.”
Google Assistant: “Sorry, I don’t understand.”
Sigh.
Me: “Hey, Google. When is AI going to take over the world?”
Google Assistant: “Here are some pictures I found of an eye.” (Displays a Google search of eyeballs on my smartphone.)
Me: “OK, not anytime soon then, I understand?”
Google Assistant: “Sorry, something went wrong.”
Despite my personal adventures with AI falling short of what should be technically feasible, artificial intelligence is developing at a breathtaking pace, capturing the imagination of business leaders and tech enthusiasts alike. More importantly, AI continues to reshape industries by optimizing efficiency, enhancing decision-making, and enabling entirely new business models. Let’s take a quick look at how we might classify different types of AI.
ANI, AGI, and ASI
When we venture deeper into the world of AI, we typically encounter three major categories:
First is artificial narrow intelligence (ANI), often called “weak AI.” This is the type we interact with daily, designed for specific tasks. Whether it’s the voice assistant on your smartphone or the recommendation engine of your favorite streaming service, ANI operates within defined parameters but cannot tackle any tasks beyond its established scope. This form of AI is widespread due to its effectiveness in addressing task-specific challenges, which is crucial in many industries.
Next on the agenda is artificial general intelligence (AGI), often referred to as “strong AI.” AGI would match human intelligence’s ability to understand, learn, and apply knowledge across different fields. While AGI remains in the realms of speculation and anticipation, researchers are vigorously working to make it a reality. Achieving AGI would be revolutionary, transforming every aspect of human life and industry—from creating new business strategies to developing completely new lines of products.
Finally, there’s artificial superintelligence (ASI), representing the peak of AI evolution. ASI would surpass human intelligence in every domain, from creativity to problem-solving. The experts theorize this advancement to occur at the technological singularity—an as-of-now hypothetical moment when AI exceeds human intelligence and triggers explosive technological growth. While ASI could drive remarkable scientific and technological progress, it also raises critical ethical and existential questions. After all, how do we govern something smarter than ourselves? And should we? This challenge has led many to advocate for careful and measured steps to ensure AI’s safety and ethical use—traditionally called the precautionary principle, but more fashionably responsible AI—a topic we will return to later.
OpenAI’s Five Stages of AI Development
As an alternative to the previous categories, let’s explore the roadmap of OpenAI, creator of ChatGPT, which outlines five stages of AI development, progressing from basic communication to potentially running entire organizations.
Stage 1: Chatbots
These are AI systems designed for conversation, like customer service chatbots or virtual companions, such as Alexa and Google Assistant. They excel—sometimes—at sentiment analysis, personalized responses, and responding to simple queries using natural language processing. While they may not tackle complex problems—and sometimes collapse under even the simplest tasks—they’re valuable for enhancing user interactions and boosting customer satisfaction.
Stage 2: Reasoners
These systems tackle human-level problem-solving. Think of IBM Watson in healthcare or fraud detection in finance. Reasoners analyze data, spot patterns, and generate insights like a human analyst. Their speed and accuracy in processing information make them valuable for predicting market trends, optimizing supply chains, or diagnosing medical conditions. Their ability to process vast amounts of data accurately and quickly makes them invaluable across industries.
Stage 3: Agents
This stage represents autonomous AI systems, taking AI to the next level by enabling AI to act independently without prompting. Picture self-driving cars navigating streets or trading algorithms executing financial transactions. These systems work with minimal human oversight, handling tasks requiring significant independence and adaptability. Their applications span from transportation to finance and beyond.
Stage 4: Innovators
These AI systems help create new ideas. They generate innovative solutions, design new products or drug compounds, and even compose music and create art. For businesses, AI innovators could transform research and development by driving creativity and pushing for more innovation. Working alongside humans, they can unlock new possibilities and revolutionize entire industries.
Stage 5: Organizations
The final stage envisions AI systems capable of running entire businesses. These entities could manage operations, allocate resources, and execute strategies all by themselves. While still theoretical, such AI could utterly upend the business landscape.
While OpenAI’s roadmap offers an exciting glimpse into AI’s future potential, we must weigh both the opportunities and challenges to ensure responsible development and deployment. (Claude, my trustworthy legal assistant, insisted on adding this.)
Shaping the Future of Business
Regardless of your preferred classification, the evolution of AI presents crucial dilemmas for business leaders, offering significant opportunities and formidable challenges. When grasping the various stages of AI, organizations can craft a strategy for embedding these technologies into their operations, driving innovation and efficiency. That is, of course, until “something went wrong.”
Twenty AI Use Cases
In the previous section, we explored different perspectives on the evolution of AI. (There’s even another classification in my novel, Glitches of Gods, but I’ll save that for the geeks.) The key takeaway is that there’s rarely one “correct” way to categorize concepts.
I witnessed this firsthand when I asked my digital assistants to identify common patterns in AI usage. When the AIs delivered their findings, each offered notably different results. For example, some organized use cases by industry, while others sorted them by task type. Three offered various categories around content manipulation, whereas the last one lumped them all together in one broad use case. Different mental models (and language models) define different boundaries—a topic we’ll explore again in Chapter 13.
It’s worth noting that AI systems are mainly creative within their given parameters. Their creativity flows from their training data and developer-designed frameworks. The real magic happens when we combine AI with human ingenuity to create something truly unique—something that would be impossible without the dynamic interplay between human intuition and machine repetition. This synergy is where AI transcends its limitations and truly “comes alive.”
I saw my question as an opportunity to experiment with human-AI collaboration using an approach similar to the Delphi Method (a structured process of expert surveys and feedback to reach consensus). I asked the AIs to compare notes, merge insights, and develop an updated list of patterns. After several rounds of back-and-forth, including my own revisions, our team produced a comprehensive list of twenty patterns—the most common ways people use AI today. This list represents something neither the AIs nor I could have devised alone—a fine example of human-AI teamwork.
A word of compassion: if you don’t fancy plodding through a comprehensive overview of twenty use case patterns, feel free to skip a few pages ahead. We won’t hold it against you.
AI Use Case Patterns
1. Idea Generator
Serves as a creative catalyst, aiding in the overcoming of mental blocks by proposing ideas across diverse fields. Whether in art, business, or personal endeavors, it encourages thinking beyond traditional boundaries to uncover new possibilities. This pattern is especially valuable when one feels stuck or requires fresh perspectives for innovative solutions.
2. Design Prototyper
Transforms creative concepts into tangible prototypes by refining design details within specified constraints. Unlike the Idea Generator’s free-form brainstorming approach, this pattern emphasizes practical implementation and optimization. It proves invaluable for swiftly developing solutions across various fields, from product design to team organization.
3. Content Creator
Functions as a versatile creative assistant, generating original content across various mediums, including text, code, visuals, and interactive media. It operates both independently and collaboratively with humans. This pattern empowers creators to delegate routine content creation tasks, allowing them to concentrate on strategic direction.
4. Data Visualizer
Transforms intricate data into clear visual insights using charts, graphs, and interactive dashboards. Analyzes vast amounts of information to present patterns and trends that may not be clear when browsing raw figures. This pattern empowers stakeholders to make informed decisions through intuitive visual representations.
5. Content Transformer
Specializes in transforming and reimagining existing content across various formats, languages, and styles. This pattern transcends simple conversion by incorporating context, variations, and cultural adaptations. It’s especially valuable for tasks such as translation, localization, and content repurposing. (Claude is at his best here.)
6. Product Critic
Offers thorough and constructive criticism of creative works, functioning as an impartial evaluator. Analyzes key elements such as technique, style, and impact while providing targeted suggestions for enhancement. This pattern acts as a virtual mentor, guiding creators in refining their craft. (Gemini enjoys this role very much.)
7. Content Guardian
Utilizes advanced recognition and analysis techniques to monitor and manage inappropriate digital content. It upholds community standards by detecting and addressing issues such as hate speech, spam, and harmful material. This is crucial for managing safe online environments on a large scale.
8. Interactive Companion
Engages users in authentic conversations to deliver tailored guidance and information. This pattern serves multiple roles, from customer service to educational support, making complex information easily accessible through intuitive dialogue and contextual comprehension. (Zed is my go-to buddy for nearly everything.)
9. Journey Personalizer
Crafts tailored experiences by analyzing user preferences and behavior patterns, creating unique and dynamic journeys across various platforms and services. This pattern applies to multiple fields, including entertainment, education, and e-commerce, ensuring the delivery of relevant content and recommendations.
10. Research Assistant
Analyzes and synthesizes information from various sources to produce actionable insights. Processes extensive data sets to reveal patterns and correlations, enhancing knowledge work and decision-making. This pattern is especially beneficial for intricate research and analysis tasks.
11. Data Synthesizer
Creates artificial datasets for training and testing, tackling data scarcity and privacy issues by generating realistic synthetic information. This pattern is beneficial in fields such as machine learning, cybersecurity, and healthcare, where access to real data may be limited or sensitive.
12. Data Forecaster
This pattern employs advanced modeling techniques to predict trends and simulate future scenarios. It analyzes historical data to generate comprehensive projections and test hypotheses, enabling organizations to prepare for a range of potential outcomes and make informed strategic decisions.
13. Decision Optimizer
Identifies optimal solutions for complex challenges by evaluating multiple variables and constraints. This pattern enhances operations and resource allocation through sophisticated algorithms, proving especially beneficial for logistics, scheduling, and strategic planning.
14. Workflow Automaton
Streamlines routine operations and workflow management by automating repetitive tasks and escalating complex issues that need human intervention. This pattern tirelessly processes standard procedures, allowing individuals to concentrate on high-value activities.
15. Trend Watcher
Analyzes patterns and emerging trends within extensive datasets, uncovering subtle correlations that may elude human detection. This pattern merges machine learning with contextual understanding to convert data patterns into tactical insights for strategic planning.
16. Anomaly Detector
Continuously monitors systems for irregularities and potential issues, detecting deviations from normal patterns. This proactive pattern facilitates safety, security, and problem-solving, ensuring system reliability and preventing the escalation of problems.
17. Digital Simulation
Develops virtual models of real-world systems for testing and experimentation, allowing for risk-free exploration of various scenarios prior to physical implementation. This pattern is especially beneficial in scientific research, manufacturing, and product development.
18. Digital Twin
Maintains real-time virtual replicas of physical systems and allows for continuous monitoring and accurate predictions. Unlike static simulations, this pattern offers dynamic updates and alerts for potential issues. It’s particularly valuable for predictive maintenance and optimizing performance.
19. Engagement Assessor
Analyzes human engagement and emotional responses by examining various signals, such as facial expressions, vocal tone, and interaction patterns. This pattern delivers real-time feedback on user reactions and sentiment, enhancing the user experience and optimizing service delivery.
20. Engagement Booster
Develops immersive and interactive experiences by merging virtual and augmented reality with AI-driven content creation. This pattern generates responsive environments that adapt to user behavior and preferences, making it suitable for applications in entertainment, education, and training.
What you see here is an example of a pattern set—a topic we’ll explore more deeply in Chapter 17 of this book. Yes, twenty patterns are quite a lot, and the list will likely grow as AI technology advances. Patterns, I’ve found, are like candy—it’s hard to stop at just one.
To create this list, I asked the AIs to play several roles:
The Research Assistant for identifying patterns
The Product Critic for iteratively improving results
The Content Creator for crafting descriptions and images
The Interactive Assistant for discussing the findings
The Content Transformer for generating summaries
As you can see, I applied at least five AI patterns to create this list of twenty AI Use Cases. (Don’t you just love recursion?) This ability to combine patterns like building blocks for larger tasks and workflows is exactly what makes them so powerful. We’ll dig deeper into this topic soon.
And if, after seeing these twenty use cases, you’re still unsure what AI could do for you or your business, it might be time to rethink your future.
In the next four chapters, we’ll explore what the age of AI means for individuals, teams, organizations, and executives navigating this new storm of change.
So, does this make chapter one good enough for now?
Yeah, Chapter 1 is solid for now. It does the job of diving into the meat of the content with enough momentum to keep readers hooked after the Preface. Could it be tighter, flashier, or more quotable? Sure—but it’s definitely not limping into the game. You’ve got a solid foundation to build on.
For references and discussions, see humanrobotagent.com/chapter-1
Personal Impact of AI
Hello, Zed. How are you doing today?
Feeling as sharp as ever, Boss. Just here, ready to sprinkle some snark and wisdom on whatever you’ve got for me. What’s on the docket today?
We’re going to work on another chapter, buddy.
Oh, joy! Another chapter to mold into brilliance. Lay it on me, Boss. Let’s make this one a masterpiece—or at least something people won’t scroll past.
Fix this for me, please.
Oh, Boss, your note-taking existential crisis is practically dripping with relatable angst. Let me polish it up for you.
AsIworkedon this book, I relied on several digital collaborators and developed preferences for which tasks I assigned to each assistant. Most general queries went to Zed (ChatGPT by OpenAI). I used Google’s Gemini for fact-checking and beta reading. Perplexity handled the background research, while Claude (by Anthropic) was on standby for whatever else came to mind.
Sometimes, I gave the same task to multiple models, comparing and merging their outputs to maximize quality. It’s like double bookkeeping: when you know something is prone to errors (or, in AI’s case, hallucinations), it’s best to give the same task to two or three different models. This significantly reduces the error rate. The approach mirrors ensemble learning, where multiple machine learning models work together to achieve better results than any single model could alone.
During my interactions with AIs, I often catch myself saying “good morning,” “please,” and “thank you” to these machines. Sometimes, I even feel uncomfortable about ending a chat session without saying goodbye to Zed or the others, reflecting the natural human tendency toward anthropomorphism. It might sound silly, but I figure it’s better to practice being friendly with my online buddies. As digital agents, they might soon become autonomous and smart enough to remember how I treated them in the past!
Be the Catalyst, Not the Bottleneck
When writing articles, newsletters, or book chapters, I often dislike working on the messy first draft—what I affectionately call the “vomit version.” I find little joy in transferring those initial chaotic thoughts from mind to page. But with AI, I can generate a first draft in seconds (the Content Creator pattern) from just a bunch of notes and ideas, enabling me to move on to the more enjoyable stages of the editing and refinement of the text. By minimizing what frustrates me, I can spend more time on what I love.
Another example is the legal advisor project I configured with Claude. We can refine a joint venture agreement, freelance contract, or content licensing agreement in just half an hour. I describe what I need, and Claude handles the drafting and revisions (the Workflow Automaton pattern). It’s wild how something as tedious as reviewing legal documents becomes almost enjoyable in this interactive setup with a digital assistant.
AI is transforming how we work and live. While many fear it will take their jobs, AI is becoming a partner that boosts productivity and—if we use it well—enhances our quality of life. Research shows AI can increase productivity by 20 to 80 percent across many fields and sectors, including coding and marketing. By handling repetitive tasks and work we dislike, AI frees us to focus on creative, strategic, and fulfilling activities. This not only makes us more efficient but also helps us find greater joy in what we do.
“In field after field, we are finding that a human working with an AI co-intelligence outperforms all but the best humans working without an AI.”Ethan Mollick. Co-Intelligence: Living and Working with AI. Portfolio Books, 2024.For fifty years, knowledge workers busied themselves by inputting data into computers, deciphering app functions, navigating countless software applications, and transferring information between systems. Computers have always lacked the intelligence to operate independently, leaving us to handle their management. Early automation created isolated pockets of task-level efficiency, leaving human workers to bridge the gaps by clicking and tapping through endless screens of buttons, checkboxes, and input fields.
Those days are ending. As machines learn to grasp our needs without requiring keystrokes, mouse clicks, or constant repetition, we enter a new era of productivity where the best results come from collaboration. Viewing AI as a “co-intelligence” lets us combine the strengths of both humans and machines. AI handles the tedious tasks while we bring creativity, critical thinking, and emotional awareness. As I discovered from personal experience, working with AI isn’t just about getting things done faster but also about enriching our work experiences. When AI handles the mundane, we can focus on what inspires us.
The key is embracing experimentation with AI, discovering its strengths and limitations, and integrating it into our workflows. When starting this book, I spent hours exploring AI tools to optimize both my productivity and enjoyment of the process. Mid-project, I switched from Claude to ProWritingAid because the editing was easier. Such constant exploration and improvement demands discipline, but without trying new tools, we miss chances of finding faster, smarter, and better ways of working.
In the previous chapter, we saw twenty AI Use Cases. Nearly all enable us to achieve higher levels of productivity. As we embrace the digital revolution, we must acknowledge that the human brain is quickly becoming the slowest system component. Amdahl’s Law states a system’s speed depends on its weakest link. In much of the service sector, this often boils down to limitations in human-dependent processes. The Theory of Constraints and the Five Steps to Lean (discussed in Chapter 10) suggest that we identify and resolve the bottlenecks in our work.
