Five books to help entrepreneurs understand the AI era
In the current era, AI is advancing at full speed. However, while everyone is talking about AI, how many people truly understand it? Facing the complex and diverse information, entrepreneurs concerned about the progress of AI may inevitably sigh about being "dazzled by the blooming flowers." The following five newly published AI-related books are selected to help entrepreneurs comprehensively grasp AI issues, ranging from technical operations, decision-making management, and innovation implementation to deeper political and philosophical reflections and the evolution of the history of civilization.
#1
Amid the global frenzy brought about by generative AI, the most pressing practical question for entrepreneurs is always: How exactly can AI bring profits? "The Profitable AI Advantage: A Business Leader’s Guide to Designing and Delivering AI Roadmaps for Measurable Results" (hereinafter referred to as "The Profitable AI Advantage") by German data scientist Tobias Zwingmann is a practical guide tailored for corporate managers. The author has led data analysis and artificial intelligence projects in several European companies and has long been concerned about the business effectiveness of AI implementation. This book aims to help companies build AI projects that can bring measurable results from concept to implementation.
Different from many best-selling AI books that emphasize technological breakthroughs, "The Profitable AI Advantage" sets a clear starting point from the very beginning - AI projects must serve business strategies and profit goals. Zwingmann has repeatedly emphasized in his blog that in the past decade, a large number of AI projects were shelved at the "proof of concept" (PoC) stage. This is not because the technology is unfeasible, but because these projects have no connection with the company's profitability. Therefore, he advocates that companies should first focus on whether AI applications can demonstrate value on the financial statements.
The book unfolds in a progressive structure, from identifying business value, laying a solid data foundation, conducting small-scale prototype tests, and organizational collaboration to measuring performance and continuous optimization. Zwingmann compares this closed-loop process from value identification to result measurement to the "AI advantage flywheel." The book is interspersed with multiple cases from corporate practices, showing how AI creates economic returns in different fields. For example, an insurance company improved its premium model by predicting customer churn rate with AI, thereby significantly increasing customer retention; a large pharmaceutical company optimized its clinical trial process with AI, shortening the R & D cycle. These examples illustrate Zwingmann's core argument - the AI that can truly bring profits often relies on process reengineering rather than algorithmic breakthroughs.
The most insightful part of Zwingmann's work lies in the analysis of organizational culture. He believes that the biggest enemy of AI projects is not algorithmic errors, but "managerial inertia" - too many companies regard AI as an outsourced task, allowing the technology and business departments to operate independently. Therefore, he advocates cultivating middle - level leaders who understand both business and data in key departments, making AI an integral part of daily business rather than a technical experiment. In addition, he also proposes that quantifiable indicators should be established at the beginning of an AI project to ensure that the results can be tracked and the return on investment can be evaluated. These indicators include not only financial dimensions such as cost savings and revenue growth but also business dimensions such as operational efficiency, prediction accuracy, and customer satisfaction. In his view, an AI project without a measurement system is just an expensive experiment.
Compared with those AI books with grand narratives, "The Profitable AI Advantage" does not discuss how AI will change the world but focuses on how AI can create value on the budget sheet. The writing style of the whole book is clear and well - organized, similar to a business consulting report. Its limitation also lies here - it is more inclined to the operational level and pays little attention to the reflections on AI at the organizational, ethical, or social levels. However, for entrepreneurs in the AI implementation stage who hope to bridge the "last mile from project to profit," "The Profitable AI Advantage" is undoubtedly a reliable action guide.
The Profitable AI Advantage: A Business Leader’s Guide to Designing and Delivering AI Roadmaps for Measurable Results
Author: Tobias Zwingmann
#2
In 2024, management consultant Geoff Woods published "The AI-Driven Leader: Harnessing AI to Make Faster, Smarter Decisions" (hereinafter referred to as "The AI-Driven Leader"), which discusses the core theme of "how leaders should think in the AI era." Woods himself once served as the Chief Growth Officer of Jindal Steel & Power in India and has long been a strategic consultant for multinational companies. The so - called "AI - driven" in the title does not mean replacing humans with algorithms but enabling leaders to achieve cognitive upgrading in their decision - making methods.
Woods emphasizes that AI should be regarded as a "thought partner" rather than a cold automated tool or an all - knowing answer machine. The real value of AI does not lie in replacing human thinking but in amplifying the radius of human thinking. Therefore, leaders should not only use AI but also learn to communicate and co - think with it. Woods points out that excellent leaders will embed AI in their decision - making chain to accelerate information analysis, reveal potential patterns, and help them gain insights into complex business situations.
In this process, the way of asking questions determines whether AI can generate truly valuable insights. AI is like a mirror. The answers it gives you depend on the questions you ask. If a leader only asks "which solution is the best," AI will mostly return a statistically average answer; but if the leader can break the question down into "which solution can balance long - term benefits and risks under the conditions of limited resources and market fluctuations," the answer generated by AI will be more in - depth. Through this interactive way, AI can free leaders from the flood of information and focus on high - level strategic issues instead of getting caught up in trivial details.
At the same time, this book also expands its perspective to the role of AI in corporate governance and ethics. In terms of data quality, leaders need to ensure that the information source is real, updated in a timely manner, and structurally balanced. Otherwise, no matter how sophisticated the AI model is, it is just "garbage in, garbage out"; in terms of algorithmic goals, leaders need to carefully define the meaning of "success" - is it maximizing profits, or enhancing social impact and brand trust? Once the indicator setting deviates from the value orientation, AI may, under the guise of the "optimal solution," replicate old biases or trigger new risks. More importantly, although AI has strong computing power, it lacks value perception - it cannot distinguish between "effective" and "right." In other words, AI can make analysis faster and information more comprehensive, but it cannot decide "what is worth pursuing" for you. For example, when evaluating overseas market expansion, AI can simulate risks and returns under different scenarios, but ultimately, how to make a choice still requires leaders to make a judgment based on the company's mission and values. This also reminds leaders that the most important thing is a sense of responsibility: even if AI provides analysis suggestions and prediction results, the final decision still has to be made by humans, and the responsibility cannot be shifted to algorithms. Based on this governance idea, AI can truly become a tool to enhance leaders' decision - making ability, and then ensure the compliance and ethics of organizational decisions while pursuing efficiency and innovation.
For entrepreneurs, "The AI-Driven Leader" provides a calm and unique perspective: the development of AI technology forces leaders to understand more clearly what is unique to humans - judgment, value, and responsibility. A true leader is not the one who masters more data but the one who gives more meaning to data.
The AI-Driven Leader: Harnessing AI to Make Faster, Smarter Decisions
Author: Geoff Woods
Publisher: AI Thought Leadership
#3
Discussions associating AI with innovation are common nowadays. However, although most corporate managers can feel the potential of AI, they have difficulty answering a more fundamental question: How can AI truly become a source of growth and innovation? In the new book "Artificial Intelligence for Business: Harness AI for Value, Growth and Innovation" (hereinafter referred to as "Artificial Intelligence for Business"), Swiss digital consultant Kamales Lardi attempts to establish a systematic framework to answer this question. For corporate managers, "Artificial Intelligence for Business" is a guide on how companies can reshape their value - creation methods with AI.
Lardi has more than 20 years of experience in corporate digital transformation and is currently the CEO of Lardi & Partner consulting company. The core concept throughout the book is that AI cannot be simply regarded as a technical project but as a value strategy. If a company treats AI as an automated tool, it can at most improve efficiency locally; only when AI is integrated into the business model and organizational culture can it bring structural competitive advantages.
The structure of the whole book can be roughly divided into three parts. The first part focuses on the core concepts and business potential of artificial intelligence, introducing the operating mechanisms of key technologies such as AI, machine learning, natural language processing, and generative AI in the corporate environment and how they are combined with business strategies. The second part focuses on the business application scenarios of AI, using cross - industry cases to show the specific ways in which AI creates value, such as customer insights and personalized marketing in the retail industry, intelligent supply chains and predictive maintenance in the manufacturing industry, fraud detection and risk assessment in the financial service industry, etc., emphasizing the role of AI in efficiency, customer experience, and innovation. The third part turns to the implementation conditions and ethical challenges at the organizational level, discussing the leadership, data and technical infrastructure, cultural transformation, and responsible AI governance framework required for the successful deployment of AI.
The most inspiring part of this book is to bring "innovation" back to management reality. Lardi points out that the real value of AI lies in expanding human capabilities and reshaping the organizational collaboration logic. She emphasizes that AI should not be regarded as an isolated technology but needs to be embedded in a broader digital ecosystem. Only when AI works in synergy with technologies such as the Internet of Things, cloud computing, and blockchain can enterprises form the ability of continuous innovation and competitive advantages. At present, AI has clear application paths in multiple industries: in the retail industry, it helps enterprises create personalized customer experiences; in the manufacturing industry, it assists in predictive maintenance and efficiency optimization; in the medical field, it promotes the innovation of diagnostic assistance and patient services; in the financial service industry, it realizes risk management and automated processes through real - time analysis. This also inspires us to think about how AI can create greater value through collaboration with other digital technologies and management systems in these industries. For example, in the retail industry, AI can combine AR fitting mirrors, voice assistants, and supply - chain data to form a full - link experience from demand prediction to fulfillment response; in the manufacturing industry, AI can combine Internet of Things sensors and digital twin technology to not only detect and prevent equipment failures in advance but also continuously optimize the operating efficiency of the entire production line through virtual simulation and real - time feedback; in the medical field, AI can collaborate with wearable devices and electronic medical record systems to build a closed - loop health management system covering both in - hospital and out - of - hospital services; in the financial service industry, the risk - warning mechanism of AI can be integrated with the transaction transparency of blockchain to further strengthen the accuracy of trust and supervision.
Different from many best - selling books advocating the "AI revolution," the idea of "Artificial Intelligence for Business" is quite practical. Lardi reminds managers that the primary reason for the failure of AI projects is often not technology but insufficient organizational preparation - such as poor data quality, lack of cross - departmental collaboration, and learning culture. She advocates that enterprises should establish a systematic AI governance structure, introduce ethical review and responsibility assessment mechanisms in the decision - making process, and ensure that technological innovation goes hand in hand with social trust. Responsible AI is the cornerstone of trust and long - term value.
Artificial Intelligence for Business: Harness AI for Value, Growth and Innovation
Author: Kamales Lardi
Publisher: Kogan Page
#4
In the current era, entrepreneurs may be more concerned about how to use artificial intelligence to create greater business value, while Mark Coeckelbergh, a professor of the philosophy of technology at the University of Vienna, puts forward a deeper view: AI is not just a pure technical tool but a political force - it not only shapes our choices but also restructures the social power structure. His new book "The Political Philosophy of AI: An Introduction" (hereinafter referred to as "The Introduction") reveals the political logic behind AI technology.
Coeckelbergh's starting point for argument is extremely clear - technology is not value - neutral. The design, deployment, and use of every technology imply the setting of "who can act and who can decide." This is especially true for AI because it transfers decision - making from the hands of people who can be questioned to invisible algorithms. The whole book is based on real - life cases and is analyzed around four important concepts in political philosophy.
First, freedom is redefined in the algorithmic era. In the past, freedom meant individual choices based on free will; but when algorithms start to predict and shape human behavior, choices are out of the scope of free will. Algorithms analyze data, predict your next move, and quietly guide your decisions through seemingly neutral recommendations and prompts. Thus, freedom is transformed into a "predictable controllability."
Second, equality is often distorted by historical biases in machine - learning systems. Algorithms are based on existing data, and these data often carry gender, race, and regional biases. In the process of extracting rules, machine learning not only does not eliminate these biases but also solidifies them and then packages them as an "objective judgment." Thus, AI institutionalizes biases in a more efficient way.
Third, democracy loses its original publicity under the filtering mechanism of algorithms. When the circulation of information is controlled by platform algorithms, the public discussion space gives way to the logic of attention. Algorithms are guided by click - through rates and dwell times. As a result, public discourse becomes entertainment - oriented and fragmented. Democracy is no longer a practice of rational dialogue among citizens but a by - product of the attention economy.
Fourth, power is redistributed in the AI governance system. As enterprises and governments generally adopt algorithmic decision - making systems, the form of power shifts from human - to - human domination to code - to - human shaping. The judgments of decision - makers are embedded in algorithms, forming an invisible rule structure; while ordinary people are degraded from decision - making participants to "data objects" to be calculated and predicted. This "algorithmic power" does not rely on explicit coercion but subtly shapes people's choices and desires through ubiquitous recommendations and evaluations.
Furthermore, Coeckelbergh points out that as AI undertakes more and more social functions, the chain of responsibility becomes blurred. Who should be responsible for the decisions of algorithms? Is it programmers, enterprises, users, or those invisible training data? In this uncertainty, a paradox of modern politics emerges: the higher the efficiency, the weaker the responsibility. The author uses the term "algorithmic bureaucracy" to describe this phenomenon - we thought we had got rid of the slowness of human bureaucracy, but we have welcomed the indifference of automated bureaucracy. Therefore, if entrepreneurs defend themselves by claiming "technological neutrality," they are actually avoiding political issues. Coeckelbergh reminds us that every deployment of AI is not just a pure technical decision but a value choice, specifically, the embodiment and redistribution of social values at the algorithmic level. For example, when the government decides to deploy face - recognition algorithms in the police field, it is not only introducing a new tool but also making a value choice of "which is more important, security or privacy." Similarly, when an enterprise decides to use AI to screen resumes, it is also making a value choice between prioritizing efficiency and fairness.
For entrepreneurs, the most important inspiration from "The Introduction" is that AI is not only a business tool but also a "governance philosophy" behind it. Therefore, corporate leaders need to establish a "reflective technological culture." When applying AI, we must think about several key questions: What does our algorithm optimize, and who does it exclude? Once the system makes a mistake, who has the right to explain? These questions bring technological governance back to the ethical and institutional levels - we should not only ask "can we" but also "should we." Only in this way can entrepreneurs be promoted from "AI users" to "AI governors."
