We often find ourselves, in the tumultuous flow of technological history, at epochal turning points, moments when the veil of the future seems to lift for an instant, revealing landscapes of previously unimaginable possibilities. Today, we are witnesses and protagonists of one of these rare moments, a crossroads that will redefine the very fabric of our businesses and, I dare say, of our civilization. I speak of the advent of Generative Artificial Intelligence (GenAI), a phenomenon whose scope echoes, with an almost telluric force, the impact that the introduction of the Netscape browser had in the mid-nineties – a true “Netscape Moment” for the twenty-first century.

Do you remember the dawn of the World Wide Web? Before Netscape, the Internet was an archipelago of knowledge accessible to a few initiates, an esoteric language for academics and technicians. Then, like a modern Prometheus, Netscape brought the fire of global connectivity into everyone’s hands, democratizing access to information and unleashing a wave of innovation that has shaped the world as we know it. GenAI, with its ability to create original content, to converse with almost human naturalness, and to assist in complex tasks, is poised to bring about a similar, if not greater, revolution in the universe of artificial intelligence and beyond.

From Addition to Alchemical Integration: The Leap from +AI to AI+

For years, we have conceived of artificial intelligence as a “+AI,” an addition, however powerful, to our existing processes. An extra algorithm for data analysis, a chatbot for customer service, a recommendation system for e-commerce. Undoubtedly useful tools, but ones that often remained confined to specific silos, intelligent appendages to a corporate body that, in its essence, remained unchanged. It was like adding a more powerful engine to a carriage, without rethinking the very nature of the vehicle.

The era of GenAI imposes, indeed, invites us to a much deeper paradigm shift: the transition to “AI+”. It is no longer a matter of adding AI, but of fusing it with the very DNA of the organization, of rethinking strategies, business models, and workflows through the transformative lens of artificial intelligence. It is an alchemical process, where AI is not an added ingredient, but the catalyst that transforms the raw material of our operations into the pure gold of innovative value. This is not simply a technological upgrade; it is a cultural and strategic metamorphosis.

The book “AI Value Creators” by IBM offers us a precious compass to navigate this transition, starting with its reinterpretation of the “AI Ladder” – the scale for AI adoption – now recalibrated for the specificities and potential of GenAI. It is not just about collecting and organizing data, or building models, but about infusing intelligence into every aspect of the business, creating a virtuous cycle where AI learns, adapts, and generates value continuously and pervasively.

Mapping the Treasure: Budget and Vision in the AI+-Driven Enterprise

Embarking on this journey towards AI+ requires a clear vision and a prudent investment strategy. Before setting sail, like ancient navigators who gazed at the stars, we must ask ourselves: where do we want AI to lead us? And what resources are we willing to commit to reach that destination?

The text suggests two fundamental dimensions for this preliminary mapping. The first concerns financial intent: are we investing in AI to save money, optimizing processes and reducing inefficiencies, or to generate new money, creating innovative products, conquering new markets, or enhancing the customer experience? Both directions are valid, but clarity of intent is crucial. A company might, for example, use GenAI to automate the drafting of standard reports (saving), while simultaneously freeing up its talent to develop, with the help of the same GenAI, highly personalized and creative marketing campaigns (earning).

The second dimension invites us to categorize how AI will deliver value. Will it be through the intelligent automation of repetitive tasks, the extraction of deep insights from vast oceans of data, or the enabling of richer, more meaningful interactions with our stakeholders? Visualizing these value paths, perhaps through an “Acumen Curve” as proposed, helps us not to disperse energy and to concentrate efforts where the impact can be greatest.

And where to start? The advice, wise as an oracle, is not to wait for the perfect map. Begin with urgency, but with discernment. Identify those everyday problems, those operational frictions that, like pebbles in the gears, slow down your progress. Often, these are precisely the areas where AI, even with initially limited applications, can demonstrate its value most quickly, igniting enthusiasm and building the consensus needed for more ambitious undertakings.

The Art of Agile Metamorphosis: “Shift Left, Shift Right”

The integration of AI+ is not a project with a defined beginning and end, but a continuous process of adaptation and learning, a perpetual dance with innovation. The concept of “Shift Left, and Then, You Can Shift Right” beautifully captures this dynamic. “Shift Left” represents anticipation, the integration of quality and intelligence from the very earliest stages of any process or development. It is like a Renaissance artisan who does not merely decorate the finished work, but infuses beauty and functionality into every single component from its conception.

Once this proactive mindset is ingrained, one acquires the ability to “Shift Right” – that is, to respond with agility and speed to feedback, market changes, and new opportunities that emerge. AI becomes our co-pilot in this dynamic navigation, allowing us to correct course, experiment with new directions, and constantly evolve.

Every day, we walk alongside countless challenges and opportunities that can be addressed or seized more effectively with technology. Generative AI offers us a new lens to see them and a new toolkit to transform them. It is about cultivating a new perspective, a sensitivity to innovation that permeates every level of the organization.

Five Golden Rules to Master the Waves of GenAI:

To ride this majestic wave of GenAI, rather than be overwhelmed by it, the book offers us five precious pieces of advice, almost mantras for the modern innovator:

1. Act with Urgency: Time, in these phases of paradigmatic change, is an even more critical resource. Do not wait for perfection, but start experimenting, learning, and iterating.
2. Be an AI Value Creator, Not Just an Occasional User: Do not limit yourself to using tools, but actively seek to understand how AI can generate unique and distinctive value for your reality.
3. Bet on Community, Not a Single Model: The universe of AI models is rapidly expanding. Strength will lie in the ability to orchestrate and integrate diverse solutions, often open source or developed by the global community.
4. Run Everywhere, Efficiently: AI solutions must be scalable and implementable efficiently across the entire corporate infrastructure, from cloud to edge.
5. Responsibility Is the Key to Trust: In an era of intelligent machines, trust is the most precious currency. Operating ethically, transparently, and responsibly is the indispensable foundation for any lasting success with AI.

With these compasses, we can begin to focus on the “AI” component of this new equation, ready to explore the depths and wonders that await us. The journey has just begun, and it promises to be the greatest intellectual and entrepreneurial adventure of our time.

Becoming Architects of the Future: The Essence of the AI Value Creator

If the first act of our exploration led us to the threshold of a new era, comparable to a digital renaissance driven by GenAI, the second act invites us to question an even more crucial role: that of the AI Value Creator. It is not simply about using the tools that this revolution makes available to us, but about becoming the conscious architects of the future it can shape. It is an invitation to transcend mere passive enjoyment to embrace active creation, a path that transforms the observer into a protagonist, the consumer of AI into a true demiurge of innovation.

The book “AI Value Creators” guides us through this metamorphosis, tracing an evolutionary line that starts from a historical understanding of AI – a sort of technological “time travel” – to arrive at the definition of what it truly means to forge value in the era of advanced artificial intelligence.

On the Shoulders of Giants: A Look at the Past to Understand the Present of AI

Like a traveler who, to fully understand the surrounding landscape, must know the paths already trodden, so we, to grasp the scope of the current AI revolution, must look to its past. The “AI Time Lapse” section of the book offers us this perspective, a masterful fresco showing the evolution of artificial intelligence from an almost science-fiction concept to a driving force of the global economy. It is not an exercise in mere erudition, but a way to appreciate the dizzying acceleration we are witnessing and to recognize the seeds of the future in the achievements of the past.

In this retrospective journey, we encounter the “foundational models,” true pillars on which much of contemporary AI rests. Understanding, at least conceptually, how these models learn – distinguishing, for example, between supervised learning, similar to a student learning under the guidance of a master, and self-supervised learning, more akin to an explorer autonomously discovering the laws of the world – is fundamental. This understanding allows us not to be mere users of a “black box,” but to intuit the profound logics that animate these new forms of intelligence.

The Supreme Destination: Being Creators, Not Mere Consumers of AI

The beating heart of this chapter, and perhaps of the entire message of the book, lies in the crucial distinction between being a “consumer” of AI and aspiring to become an “AI value creator.” The first is a passive posture: one uses AI-based applications and services developed by others. It is like admiring a magnificent building. The second, however, is an active vocation: one designs, builds, and integrates AI to solve specific problems, to generate new opportunities, to shape novel solutions. It is like being the architect of that building.

The invitation is clear: the true destination, for businesses aspiring to thrive in the AI+ era, can only be value creation. This does not mean that every company must develop its own language models from scratch – a titanic undertaking, comparable to building a medieval cathedral. Rather, it means skillfully orchestrating existing technologies, customizing them, integrating them into one’s processes and products in ways that generate a unique and sustainable competitive advantage. It means having the vision to identify where AI can make a difference and the audacity to implement it.

How does one face this choice? Is one a “Value Creator” or a “Value User”? The answer, the text suggests, is not always binary, but the strategic direction must be unequivocal. It is somewhat like the difference between someone who reads a map and someone who draws it: both can reach a destination, but only the latter has the power to chart new routes.

Designing Tomorrow: AI and the Multiplicity of Possible Futures

Looking to the future, the AI landscape presents itself not as a monolith, but as a vibrant and diversified ecosystem, populated by a multiplicity of GenAI models. There will probably not be a single “one ring to rule them all,” to quote Tolkien. Rather, we will witness increasing specialization, with models optimized for specific tasks, collaborating and integrating into complex architectures.

The challenge, and the opportunity, for AI value creators will be to navigate this complexity, to demystify AI, and to apply it with pragmatism and vision. It is not about chasing the latest technological novelty for fashion’s sake, but about deeply understanding the needs of one’s business and customers, and selecting or building the AI solutions best suited to meet them. It is a continuous exercise of discernment, a sort of intelligent curation of the potential offered by technology.

The future of AI is a book still largely unwritten, and AI value creators will be its most prolific authors. It is a future that, while presenting unknowns and challenges – ethical, social, economic – also overflows with promise: the promise of solving problems intractable today, of unlocking new levels of creativity and productivity, of improving the quality of life in ways we can only begin to imagine.

Embracing this role of AI value creator ultimately means accepting the challenge of actively shaping this future. It is an invitation not to be passive spectators of change, but to become its enlightened protagonists, armed with vision, competence, and a profound sense of responsibility. Let us, therefore, enter this new chapter with the curiosity of the explorer and the ambition of the builder, ready to leave our mark on the age of artificial intelligence.

The Alchemical Equations of AI Persuasion: Transforming Resistance into Acceptance

Innovation, especially disruptive innovation like artificial intelligence, rarely sails in placid waters. It often encounters currents of skepticism, winds of resistance, and sometimes veritable storms of misunderstanding. To guide our organizations through this transformation, technological excellence alone is not enough; we need the art of persuasion, an almost alchemical ability to transmute doubt into curiosity, fear into enthusiasm, and inertia into convinced action. The third chapter of the journey we are undertaking with “AI Value Creators” offers us precisely this: not rigid mathematical formulas, but metaphorical “equations,” guiding principles to articulate the value of AI in a way that resonates deeply and drives adoption.

These “equations” are not extemporaneous inventions, but distillations of wisdom that draw on fundamental truths about the relationship between humans, technology, and progress. They recognize that, although technology evolves at astonishing speeds, certain mechanisms of the human psyche and organizational dynamics remain surprisingly constant.

The Eternal Dance Between Man and Machine: Immortal Principles in Technological Persuasion

Before delving into the specific “equations,” the text invites us to a preliminary reflection: some dynamics are timeless. The tension between the familiar and the unknown, between the comfort of the status quo and the promise (but also the uncertainty) of the new, has always accompanied every great technological leap. Think of the industrial revolution, the advent of electricity, or the spread of the Internet itself: every era has had its prophets and its detractors, its enthusiastic pioneers and its cautious conservatives.

Understanding this inherent tension is the first step towards effective persuasion. It is not about denying legitimate concerns or minimizing challenges, but about addressing them with intellectual honesty, offering a vision that is both ambitious and reassuring. It is like an experienced navigator who, despite knowing the dangers of the open sea, knows how to instill confidence in the crew thanks to his competence and the clarity of his route.

The book reminds us, with a touch of wit, that for these equations of persuasion, “no calculators are needed.” It is not about quantifying every variable with millimeter precision, but about grasping the qualitative essence of the forces at play. They are invitations to strategic reflection, rather than algorithms to be entered into a spreadsheet.

The Three Keystones of AI Persuasion:

1. Equation 1: How to Grow the Gross Domestic Product (of the Company) This first equation brings us back to an economic fundamental, translated, however, into the corporate microcosm. AI, to be persuasive, must demonstrate its ability to contribute to growth, prosperity, and the increase of the “added value” that the company generates. Whether it is increasing revenues, optimizing costs, improving productivity, or expanding market share, the link with economic performance must be explicit. It is not enough for AI to be “interesting”; it must ultimately be “profitable” or strategically crucial for future sustainability. It is the language that every board of directors understands, the metric that justifies investments and mobilizes resources. We could imagine it as AI’s ability to make new branches flourish on the company tree, each laden with juicy fruit.
2. Equation 2: What Makes AI a Success? The success of AI is not a monolith, but a mosaic composed of multiple tiles. This equation pushes us to identify and articulate the critical factors that determine the success of an AI initiative. Is it just about technology? Or do data quality, team skills, corporate culture, clarity of objectives, integration with existing processes, and the ability to manage change also come into play? The answer, of course, is that success is multifactorial. Effective persuasion must therefore touch upon these different dimensions, showing how all the necessary elements are intended to be orchestrated. It is like an orchestra conductor who focuses not only on the skill of individual musicians but on their ability to play in unison to create perfect harmony.
3. Equation 3: Finding Balance – Navigating the Paradox The adoption of AI is often a balancing act, a walk on a tightrope stretched between seemingly irreconcilable opposites. Think of the paradox between innovation and risk, between automation and human impact, between personalization and privacy, between speed of implementation and robustness of the solution. This third equation invites us to recognize these paradoxes and to demonstrate how we intend to navigate them wisely. It is not about choosing one extreme at the expense of the other, but about finding a higher synthesis, a point of dynamic equilibrium. It is the art of the tightrope walker who, with elegance and precision, maintains stability while moving towards the goal. Persuading, in this context, means demonstrating that one has understood the complexity and possesses the maturity to manage it.

The Final Admonition: AI as a Value Generator, Not a Simple Cost Center

Finally, the chapter leaves us with a crucial piece of advice, almost a categorical imperative for anyone wanting to promote AI within an organization: AI must be perceived and presented as a value generator, not as a mere cost center. This distinction is fundamental. If AI is seen only as an additional expense, a new item in the budget to be contained as much as possible, its adoption will always be an uphill battle, subject to cuts and resistance. If, on the contrary, it is understood as a strategic investment capable of unlocking new efficiencies, creating new revenue streams, improving the customer experience, and strengthening competitive advantage, then the doors will open more easily.

It is a matter of perspective, of narrative. We must be skilled in painting the picture of future benefits, in quantifying, where possible, the return on investment, and in showing how AI is not an end in itself, but a powerful means to achieve the company’s strategic objectives. It is like transforming, in the eyes of stakeholders, an apparently insignificant seed into the promise of a lush forest.

Concluding this reflection on the “equations of persuasion,” we realize that the art of communicating the value of AI is as important as the science that underpins it. It is a skill that requires empathy, strategic vision, and a deep understanding of both technology and human and organizational dynamics. Armed with these principles, we can face with greater confidence the task of guiding our businesses towards the bright horizon of AI+.

The Theatre of AI Innovation: From Horizontal Principles to Vertical Scenographies of Use Cases

After exploring the conceptual foundations and persuasion strategies for the adoption of artificial intelligence, our journey with “AI Value Creators” now leads us into the pulsating heart of practical application: the vast and varied theatre of use cases. It is here, on the stage of daily operations and sectoral challenges, that AI ceases to be an abstract promise and becomes a tangible force for transformation and value creation. This chapter is a kind of art gallery, where each exhibited work – each use case – illustrates a different facet of AI’s potential, inviting us to reflect on how these innovations can be orchestrated in our specific business realities.

Let us imagine this chapter as an exploration moving along two complementary lines: a horizontal one, embracing the transversal principles and applications of AI, and a vertical one, immersing itself in the specificities of individual industrial sectors. It is like observing a landscape first from a panoramic viewpoint that reveals its breadth and interconnections, and then descending into individual villages to discover their traditions and uniqueness.

The Ascending Curve of Value: Horizontal Principles for All Stages

The text introduces the illuminating concept of the “use case value creation curve,” an ideal path showing how AI’s impact can grow exponentially as one matures in its adoption and integration. But, even more interesting, is the assertion that “going horizontal gets you the most vertical.” What does this apparent contradiction, worthy of a Zen koan, mean?

It means that there are AI capabilities and applications – we could call them “horizontal” – that, despite being transversal to multiple functions and sectors, are fundamental to unlocking maximum value in vertical applications, those specific to a particular domain. It is like learning the fundamentals of music (theory, harmony, rhythm – the horizontal) to then be able to compose magnificent symphonies in any genre (the vertical).

Among these horizontal principles, the book highlights some crucial ones:

• Experimentation: AI innovation is not born from magic formulas, but from an iterative process of experimentation, learning, and adaptation. Creating safe spaces to try, fail fast, and learn from mistakes is fundamental. It is the approach of the alchemical laboratory, where every experiment, even an unsuccessful one, brings one closer to discovering the philosopher’s stone.
• Putting Your Data to Work: Data is the fuel of AI. The ability to collect, clean, manage, and interpret data effectively is a precondition for any successful AI initiative. It is not just about having large amounts of data (the Big Data “deluge”), but about transforming it into actionable knowledge, like a skilled sculptor who extracts a meaningful form from a raw block of marble.
• IT Automation: AI can revolutionize IT operations themselves, automating infrastructure management, performance monitoring, security, and much more. This not only frees up valuable resources but also creates a more agile and resilient environment for the development of further AI applications.
• Code—The Language of Computers: Although no-code and low-code platforms are democratizing access to AI, a deep understanding of code and its potential remains a strategic advantage. Code is the language with which we dialogue with machines, the tool with which we shape our intelligent intentions.
• Digital Labor and AI Assistants: AI is redefining the very concept of “work,” introducing increasingly sophisticated forms of human-machine collaboration. AI assistants, capable of understanding natural language and performing complex tasks, will become indispensable companions in many professions, enhancing human capabilities rather than merely replacing them.
• Intelligent Agents: A further step is represented by AI agents, systems capable of acting autonomously to achieve specific objectives, learning from experience and interacting with the environment. Think of agents optimizing supply chains in real-time or managing personalized investment portfolios.

The Business Lens: Horizontal Use Cases and the Power of Synthetic Data

Looking through the “business lens,” horizontal use cases manifest in applications that can bring benefits to almost all organizations, regardless of sector. A particularly powerful example discussed in the book is that of synthetic data. In an era where privacy is sacrosanct and access to high-quality real data can be limited, the ability to generate artificial, yet statistically representative, data opens incredible scenarios. AI models can be trained without compromising privacy, rare scenarios can be simulated, or new products can be tested in virtual environments, accelerating innovation and reducing risks. It is like having a flight simulator for business strategies, allowing experimentation without real-world consequences.

A Mosaic of Excellence: Vertical Use Cases for Every Industry

After exploring the horizon, the book guides us through a “parade of vertical use cases,” showing how AI is already leaving a profound mark on specific sectors. This is not an exhaustive list, but a stimulating taste of the infinite possibilities:

• Agriculture: From precision agriculture optimizing water and fertilizer use to crop monitoring via drones and satellites, AI promises more abundant and sustainable harvests.
• Accounting: Automation of transaction recording, predictive analytics for fraud detection, and AI-enhanced financial consulting are transforming the accounting profession.
• Education: Personalized learning platforms, virtual tutors, and tools for creating tailored educational content are just some of the applications that will revolutionize how we learn and teach.
• Healthcare: From early disease diagnosis and medical image analysis to new drug discovery and personalized therapy management, AI has the potential to drastically improve health and well-being.
• Insurance: More accurate risk assessment, policy personalization, automated claims management, and fraud prevention are areas where AI is already bringing significant benefits.
• Legal: Analysis of large volumes of legal documents (e-discovery), enhanced legal research, prediction of case outcomes, and automation of routine legal tasks are changing the face of legal practice.
• Manufacturing and Production: Predictive maintenance of machinery, automated quality control, optimization of assembly lines, and generative design are examples of how AI is driving the Fourth Industrial Revolution.
• Pharma: Acceleration of drug discovery and development, analysis of genomic data, personalization of therapies, and optimization of clinical trials are areas of enormous impact.

This chapter on use cases leaves us with a sense of vertigo and wonder. AI is not a single technology, but a universe of possibilities, a versatile language with which we can rewrite the rules in almost every field of human activity. The challenge for every leader, for every innovator, is to become a skilled “director” in this theatre of innovation, choosing the right scenographies (sectors), the right actors (AI technologies), and the right plots (use cases) to create stories of success and lasting value. The possibilities are endless, and the curtain has just risen.

The Strategic Crossroads of AI: Live, Die, Buy, or Try – Decisions Sculpted by Artificial Intelligence

Our journey through the fertile territory of artificial intelligence, guided by the conceptual map of “AI Value Creators,” now leads us to a crossroads dense with strategic implications. If the previous chapters illuminated the what and the how of value creation through AI, this fifth chapter confronts us with fundamental, almost existential decisions for companies navigating the tumultuous waters of digital transformation: “Live, Die, Buy, or Try.” Much, if not all, will be decided by the approach we adopt towards artificial intelligence. It is a warning that echoes the crucial choices that every leader, every explorer, every pioneer has had to face throughout history: adapt and prosper, or resist and risk oblivion.

This chapter does not offer simple answers, but rather a range of profound considerations on critical aspects that go far beyond mere technological implementation. It touches upon the chords of ethics, sustainability, security, and governance, elements that, like the invisible foundations of an imposing building, determine its stability and longevity.

Modern Oracles: Large Language Models (LLMs) and Their Hidden Truths

At the center of many current discussions on AI are Large Language Models (LLMs), systems capable of understanding and generating text with a fluency that borders on human. The book invites us to look at them with a critical and aware eye, going beyond superficial enthusiasm. There are aspects that “people forget to tell you” about LLMs, crucial details for those who must make strategic decisions.

One of these is the “knowledge cut-off date”: these models, however vast, have a temporal horizon beyond which their knowledge does not extend, barring continuous and costly retraining. It is like consulting an all-wise oracle, but whose vision stops at yesterday. Furthermore, there is their tendency to “invent” (so-called “hallucinations”), to generate plausible but untrue information. Understanding these limits is essential to avoid falling into decisional traps.

We cannot ignore the “carbon footprint” of AI. Training these computational behemoths requires enormous amounts of energy. Sustainability must become an integral part of the AI equation, pushing us towards more efficient models and more responsible practices. It is a call not to turn our “AI best friend” into an unwitting enemy of the planet.

Issues such as copyright and the legal implications of using content generated or utilized by AI are minefields that require caution and expertise. What is the “digital essence” of a work created by an LLM? Who holds the intellectual property rights? These are questions that jurists and legal philosophers are still debating, but that business leaders cannot ignore.

The Razor’s Edge: Security, Privacy, and the Ethics of “Steal Now, Fix Later”

The expansion of the digital attack surface, the growing vulnerability of data, and the protection of privacy are concerns amplified by the advent of AI. Every new intelligent system, every new interconnection, can represent a potential breach if not designed with security as an absolute priority. It is like building a fortress: the larger and more complex it is, the more meticulous the design of its defenses must be.

The book introduces, perhaps provocatively, the concept of “Steal Now, Crack Later,” a warning against the indiscriminate collection of data in the hope that future AI technologies can extract value from it, perhaps circumventing privacy regulations. This approach, besides being ethically questionable, exposes companies to enormous reputational and legal risks. Trust, once lost, is difficult to regain.

Conversely, the “virtuous actor” in the AI arena operates with firmly established ethical levers. Fairness, understood as the commitment to avoid discriminatory biases in data and algorithms, is crucial. Bias, like an insidious shadow, can perpetuate and amplify existing inequalities. Recognizing and mitigating it is a moral duty and a necessity for building fair and reliable AI systems.

Robustness, or the ability of AI systems to withstand unexpected or malicious inputs and maintain reliable performance under different conditions, is another pillar. A fragile system, however intelligent, is a risk, not an asset.

Explainability, the ability to understand and illustrate how an AI system arrives at a particular decision, is fundamental for transparency and accountability. We cannot blindly rely on “black boxes,” especially when decisions have significant impacts on people or the business. It is like demanding that a judge not only issue a verdict but also explain the reasons behind it.

Data Lineage, or the traceability of the origin and transformations undergone by the data used to train and operate AI models, is essential to ensure quality, compliance, and to investigate any problems. It is like having a family tree for every piece of data, attesting to its provenance and history.

Navigating the Norms: Regulation and AI Lifecycle Management

The landscape of AI regulation is constantly evolving. New laws and directives are emerging at national and international levels, seeking to balance the promotion of innovation with the protection of fundamental rights and the mitigation of risks. Understanding “the section that wasn’t supposed to be there” – that is, the often complex and unexpected regulatory implications – is an arduous but indispensable task. Companies must adopt a proactive approach, not only to comply with existing laws but also to anticipate future trends and contribute to defining responsible standards.

The management of the entire AI lifecycle, from conception and development to implementation, monitoring, updating, and eventual decommissioning, is a complex process that requires governance, skills, and appropriate tools. It is not a “one-off” project, but an ongoing commitment, similar to maintaining a complex garden, which requires constant care to thrive.

This chapter leaves us with the awareness that strategic decisions on AI are imbued with considerations that go far beyond code and algorithms. They require enlightened leadership, capable of balancing the audacity of innovation with the wisdom of prudence, enthusiasm for new frontiers with a profound sense of ethical and social responsibility. The choices we make today – whether to embrace AI as a transformative opportunity, to risk “dying” by ignoring it, to “buy” off-the-shelf solutions, or to “try” to build our own capabilities – will define our place in the future that artificial intelligence is helping to create.

Skills That Thrill: Forging Talents for the Artificial Intelligence Orchestra

We have reached the final movement of our symphony on artificial intelligence, a chapter dedicated not so much to the technology itself, but to the human capital that animates and directs it: skills. If AI is the new, powerful instrument at our disposal, it is the skilled hands, creative minds, and visionary hearts of people that transform it from cold metal into a value-generating work of art. The sixth chapter of “AI Value Creators,” evocatively titled “Skills That Thrill,” invites us to reflect on how to cultivate and orchestrate the talents necessary to thrive in the age of AI.

This is not a mere technical appendix, but a central issue, perhaps the most critical for long-term success. Because, like a luthier who builds Stradivarius violins but has no musicians capable of playing them, so a company can possess the most advanced AI technology, but without the right skills, it will remain mute and inert.

The Dawn of Skill: A Strategic Imperative

The starting point is a fundamental realization: skills development is not an optional extra, a “nice to have,” but a strategic imperative. In a rapidly evolving world, where knowledge becomes obsolete at an increasing pace, the ability to learn, unlearn, and relearn – so-called “learnability” – becomes the queen of competencies.

The book tackles head-on a question that often hovers in debates about AI: is artificial intelligence a “job destroyer” or a “job creator”? The answer, as often happens in complex issues, is not unequivocal. Certainly, AI will automate some tasks, transforming existing roles. But, at the same time, it will create new ones, requiring unprecedented skills and opening up unimaginable opportunities. The challenge is not so much to resist this transformation, but to ride it, preparing people to face new roles and new responsibilities. It is like when the automobile replaced the horse-drawn carriage: blacksmiths disappeared, but mechanics, automotive engineers, and drivers were born.

The need to “upskill and reskill” therefore becomes a mantra. And in this, “democratized technology” – that is, AI tools that are increasingly accessible and easy to use – plays an ambivalent role: on the one hand, it lowers the entry threshold for some applications; on the other, it requires a deeper understanding of the underlying principles to be used strategically and not merely superficially.

Levers for a Skills Program That Lasts

How, then, to build a skills development program that is not an extemporaneous flash in the pan, but a perennial fire that fuels innovation? The book offers us a series of “levers,” guiding principles to unlock human potential in the age of AI:

1. Start at the Beginning – Hire Employees Who Want to Know “Why”: Intellectual curiosity, the desire to understand not only the “how” but above all the “why” of things, is a distinctive trait of the most precious talents. Seeking people with this hunger for knowledge is the first step.
2. Recruit Digitally Minded Talent: Beyond specific technical skills, a digitally open mindset, familiarity with new technological paradigms, and a propensity for continuous learning are fundamental.
3. Take Count—Inventory Your Skills: Before embarking on expensive training programs, it is essential to map the skills already present in the company. Hidden treasures are often discovered, people with unsuspected passions and abilities that can be valued.
4. Plan for Everyone—A Plan Without Action Is a Speech: Skills development programs must be inclusive, aimed at all levels of the organization, and above all, they must translate into concrete actions, personalized training paths, and tangible growth opportunities.
5. Embrace the Learning (and Forgetting) Curves: Learning is not a linear process. There are learning curves, but also forgetting curves. Training must be continuous, supported by reinforcement mechanisms and practical application to consolidate knowledge.
6. Combine Instruction + Imitation + Collaboration: The most effective learning often occurs through a mix of formal instruction, observation of experts (imitation), and collaborative work on real projects. It is the ancient wisdom of the artisan workshop, applied to the digital age.
7. Culture Matters—Be a Skills Verb, Not a Noun: Corporate culture plays a crucial role. It must promote curiosity, experimentation, knowledge sharing, and the valorization of learning. A “skills culture” is not a static state (a noun), but a dynamic process (a verb).
8. Set the Organizational Tone for AI: Leadership must set an example, showing a genuine commitment to AI adoption and the development of the necessary skills. The tone that comes from the top permeates the entire organization.

The IBM Case and the Final Word: A Call to Action

The chapter is enriched by an illuminating case study: the skills challenge at IBM and the response the company was able to provide. A concrete example of how a technology giant addresses the need to constantly reinvent the capabilities of its workforce.

The final word of this chapter, and of the book itself, is a vibrant call to action. The skills that “thrill” are not only technical but also human – creativity, critical thinking, emotional intelligence, collaboration – enhanced and amplified by artificial intelligence. It is the synergy between humans and machines, the harmony between human intuition and computational power, that creates true magic.

Forging these talents, building these orchestras of human and artificial intelligences, is the greatest and most exciting challenge of our time. It is the safest investment to ensure not only the survival but the flourishing of our businesses in a future that promises to be rich in promise and wonder. Let the music begin.