Summary of the Core Content
This article uses the story of Chopin and the "unfinished piano" as a metaphor for the current state of strategic management: new tools such as AI have emerged, but old theoretical frameworks are unable to explain new business realities such as platformization, ecosystem development, and digitalization. Strategic management is in a transitional period of being "unfinished." The article argues that while AI has reduced the cost of information acquisition, the scarce value of strategy has shifted from "information" to "judgment, decision-making, and organizational change." The way strategies are implemented has also changed from a top-down, linear planning approach to a collaborative, iterative process involving humans and machines. New strategic "grammar" (underlying frameworks) will not emerge out of nowhere; they must be developed through collective exploration in real business practices.
I. Strategic Management is in a Transitional Period of Being "Unfinished"—Just Like Pianos in Chopin's Time
During Chopin's lifetime, pianos did not have a unified standard of design: sound quality, structure, and range were all evolving, and there was no clear answer to the question of what a piano should look like. Chopin chose the delicate Pleyel piano, explored its capabilities while composing music, and in doing so, contributed to the development of the piano's expressive potential.
Today's strategic management is similar to pianos from that era: past theories (such as Porter's competitive analysis and core competence theory) can explain traditional business models, but they are insufficient when dealing with new phenomena like AI, platform ecosystems, and digitalization. For example, within the same industry, some companies have successfully transformed using AI, while others have stagnated. Old theories cannot explain why this is the case. Companies recognize the importance of AI, but they struggle to determine how to allocate budgets, adjust KPIs, or decide whether to cut old business operations—there are no clear answers within the existing frameworks. This is a transitional period where "new tools have arrived, but old languages are no longer effective," and strategies are still in the process of being defined.
II. AI Has Shifted the Scarce Value of Strategy from "Information" to "Judgment and Action"
In the past, companies would spend weeks or even months gathering industry data, and the ability to obtain information was itself a strategic advantage. Now, AI can quickly collect and organize information, making it less scarce. However, new challenges arise:
- Having access to a lot of information does not guarantee accurate judgment (for example, distinguishing between structural changes in the industry and short-term noise);
- Knowing that transformation is needed does not mean being able to put it into action (for example, companies may hold many AI-related meetings, but the organizational structure and evaluation systems remain unchanged).
Therefore, the value of strategy has changed: in the past, it was about who could obtain information; in the future, it will be about who can make correct judgments, make decisions, and drive genuine organizational change. AI can help you see the facts, but it cannot help you cut old business operations or adjust利益 distributions—these are the truly valuable aspects of strategy.
III. AI Has Reconstructed the Way Strategies Are Implemented—from "Planning" to "Iterative Circulation"
Previously, strategic management followed a linear process of "top management sets plans → middle management assigns tasks → lower-level employees execute." With the addition of AI, this has become a collaborative cycle:
1. Humans and machines together perceive market changes (AI quickly collects data, humans interpret trends);
2. Humans make critical decisions (for example, whether to invest in AI-related initiatives);
3. Machines assist with execution (for example, AI optimizes resource allocation);
4. Judgments are revised based on results (for example, if an investment direction turns out to be wrong, adjustments are made promptly).
Strategy is no longer about predicting the future in a fixed plan; it is about quickly testing and adjusting. For instance, front-line teams can use AI feedback to adjust strategies when they find that higher-level plans do not match reality—the key is not how much of a plan has been executed, but whether one is willing to admit mistakes and make changes.
IV. New Strategic "Grammar" Will Not Appear Out of Thin Air; It Must Be Developed from Practice
New strategic frameworks cannot be created by scholars writing books alone; they must be derived from real business actions:
- For example, how companies determine the boundaries between human and AI decision-making (what tasks must be done by humans and what can be automated);
- How to redesign processes (for example, involving front-line teams in strategic adjustments);
- How to handle failures during transformation (for example, how to balance the interests of old employees when cutting back on old business operations).
This new "grammar" is not the result of one person's work; it is the outcome of collaboration among researchers, entrepreneurs, and technicians—just as Chopin did not write a "piano playing guide"; he developed his classic expression through practice. Today's companies do not need to wait for a "perfect strategic template"; they define these templates in their own actions.
V. In an Unfinished Era, It is the Time of Definers
In an era with established templates, everyone just follows them (for example, analyzing industries using Porter's Five Forces model); in an era without templates, those who can organize the scattered new phenomena (AI, platforms, ecosystems) into clear frameworks become the definers.
Just as Chopin composed music before the piano standards were set, today's companies do not need to wait for strategic frameworks to be finalized. Participating in their definition is the greatest opportunity. Some companies are already experimenting with collaborative decision-making processes involving humans and machines or adjusting evaluation systems for the AI era; these practices are the seeds of new strategic "grammar."
In Conclusion: In the AI era, strategy is not about waiting for others to provide answers; it is about defining them yourself. Uncertainty is the best time for innovation.