We are already experiencing the 100x rule by directly using generative AI.
These experiences must now be translated from the individual to the organization.
The book presents a five-step process for AI management that will increase your capabilities and those of your organization by 100 times.
Stage 1 is sufficient for a one-person business or small company, but for slightly larger companies, it is necessary to develop to stages 2 and 3, and for global corporations, to stages 4 and 5.
The book explains what resources are needed to reach each stage, what to keep in mind, what preparations organizations must make, and, most importantly, how individuals should position themselves and develop themselves going forward.
The author of this book, Jae-seon Hwang, Vice President of SK Discovery DX Lab, wrote this book based on his experience leading various AI projects in corporate settings, from the DX era to the current AX.
So it contains very realistic and systematic content.
The author has previously provided great inspiration to many managers by defining DX (digital transformation) as “changing organizational habits.”
Now, this message is being redefined as “AX (AI Transformation) is about changing organizational habits.”

The most important premise of this book is that “AI transformation is not something that can be achieved by the decision of one or two executives.”
Of course, the fact that top management sponsorship is essential remains unchanged.
However, as AI's penetration extends to office automation, business processes, and the entire company's decision-making process, virtually every employee will inevitably encounter AI in some form.
In this context, this book aims to serve as a basic guide for both executives and practitioners.
--- p.24

DX also had its limitations.
At some point, the question arose: "We've adopted the technology, but what next?" While RPA and big data analytics had taken root within the company, the organization's core decision-making still relied heavily on executives' intuition and experienced expertise.
Although data analysis reports were referenced when developing sales strategies, the final decision was based on the “executive’s intuition.”

--- p.34

While DX emphasized efficiency, data integration, and the digitization of existing business models, AX leverages generative AI across the enterprise to reorganize decision-making and creative capabilities. The digital infrastructure established during DX undoubtedly serves as a solid foundation for AX.
Employees familiar with cloud environments, big data platforms, and collaboration tools are less resistant to the introduction of generative AI.
The technical difficulty of linking with internal systems is also low.

--- p.45

To put it simply, the strategy is to first attract the attention of many members by providing them with experience using external generative AI services, create small success stories, use these as stepping stones to properly integrate internal data and infrastructure, and then gradually internalize in-depth AI capabilities throughout the organization to increase expertise.
And ultimately, we will reach the agent stage where AI can recognize goals and perform tasks independently without human commands.
Then, we dream of a commercialization stage where we can sell the final, advanced AI model to the outside world, although this is optional.

--- p.67

We also need to examine what management's expectations are about AI and whether those expectations are realistic.
If some executives overly believe AI is omnipotent and pressure it to produce amazing results in a short period of time, the burden on working-level staff will increase, and if the actual model performance falls short of expectations, it may be labeled a failure.
Conversely, if management is too conservative and takes the attitude of "what if errors occur when applying AI in the field, let's just stick with the existing methods for now", it will be difficult to even proceed with the pilot stage.
In other words, when evaluating AI readiness, the attitude and reality of management also become important points.

--- p.126

Even in the era of digital transformation (DX), there was a saying that “70% of the success of digital transformation depends on the CEO.”
No matter how much the business wanted to adopt IT technology, if the CEO didn't show interest, the budget wouldn't be approved and there was no way to control organizational resistance.
However, conversely, when a CEO delivers a strong message, "We will definitely transform our company into a digital company," they can overcome many obstacles and achieve massive innovation. I believe this trend will intensify in the AI ​​era.
We must declare that direct AX is our essential survival strategy and guarantee investment in budget and talent.

--- p.145

We will examine the pros and cons of the integrated, separated, and hybrid models of the existing IT organization and the AI ​​team, which is a dedicated AX organization, and examine how to structure the CoE.
Through a hypothetical case study, we examine organizational operations, decision-making structures, internal regulations and authority management principles that must be observed when utilizing generative AI or RAG, and the importance of AI ethics committees and bias monitoring.
Finally, we will also summarize the guidelines required when utilizing external APIs such as ChatGPT.

--- p.170

Talent strategy and training are key pillars of organizational and cultural innovation in the AX era.
Companies face countless questions, ranging from "Which is better: external hiring or internal development?" to "Should we extend prompt engineering to general employees?", "How will we establish and evaluate new roles like AI managers and AI operators?", and "What performance and reward systems will we use to attract and retain talent?"
The conclusion we can draw from this is that the human resources strategies we learned during the DX era are insufficient. Given the speed and explosive power of AI, partial training or limited external recruitment alone will be difficult to achieve sustainable innovation.
Companies should pursue a more integrated strategy, such as operating long-term AI bootcamps or in-house academies to massively retrain internal personnel, while simultaneously recruiting and leading externally, formalizing roles like prompt engineering and AI manager as internal promotion paths.

--- p.198