Nicht alle Unternehmen eignen sich für die Nutzung von Künstlicher Intelligenz.
Currently, AI technology is developing at an unprecedented speed and penetrating into all industries, making it the "new favorite" of many companies. From intelligent customer service, which improves the efficiency of customer service, to intelligent production, which optimizes manufacturing processes, AI seems to draw a development concept with infinite possibilities for companies. However, after nearly half a year of active participation, Mr. Yang has found that not all companies are suitable for embracing AI in this AI technology craze. If companies blindly imitate and introduce AI, they may fall into the dilemma of making huge investments but achieving only meager results.
Why is this the case?
Introducing an AI project in a company is essentially also a digital project. Looking at the history of companies' digital transformation in recent years, it is not difficult to see that even if some companies have sufficient budgets, cases of failed digital transformation still occur frequently. Why is this the case? Mr. Yang has summarized the following five core reasons for failure:
1. Strategic misalignment: Unclear strategy and undefined goals;
2. Organizational resistance: There is always an open or hidden competition between business departments and information departments;
3. Competency gap: Employees have insufficient digital competencies, but many companies are not aware of this. Many corporate leaders still drive digitalization forward with blind optimism. According to relevant research data, training costs double when the proportion of employees over 50 years old in a company is > 40%. When the proportion of employees with an educational level below junior college is 30%, the error rate in data entry is over 25%;
4. Delayed management: The lack of an appropriate management mechanism and processes means that the company cannot adapt to the changes brought about by new technologies. This leads to a significant increase in late - stage costs. Data shows that when a company has no data standards, the late - stage costs for data management account for 28% of the total digital budget;
5. Accumulation of technical debt: Blindly pursuing technological advancement means that the suitability of the technology for business operations is neglected. This leads to a cumbersome and difficult - to - maintain system that then unfortunately has to be rebuilt, resulting in the initial investments going to waste.
If it is already so difficult for companies to implement digital projects, how much higher are the requirements for a company's overall cooperation ability and strategic implementation when it comes to AI, an even more complex and subversive technology. Therefore, Mr. Yang believes that before introducing AI, companies should definitely calmly evaluate their own conditions and clearly define the boundaries of technology application. Blindly following trends will only cause companies to fall into the trap of "AI for the sake of AI". Therefore, Mr. Yang recommends self - assessment based on the following four scenarios:
Firstly, Digital foundation:
Does the company already have good data management capabilities and an information infrastructure? This is the foundation for AI application. Matching with business processes: Does AI technology fit well with the company's core business scenarios and can it really solve practical problems? For example, whether the data quality meets the standards and whether the data silos are connected. This is the key to whether AI can realize its value;
Secondly, Business processes:
Currently, some traditional companies introducing AI seem to have fallen into a trap, that is, the introduction of AI only serves to reduce personnel and cut costs. This is clearly a misunderstanding of the value of AI. The core value of AI lies in "efficiency improvement", and by no means in simple "employee reduction". When adapting to business processes, AI is more suitable for areas that require a large amount of repetitive work, highly precise decision - making support, or real - time response, such as the quality control process in the manufacturing industry, risk assessment in the financial industry, and intelligent recommendation in the retail industry.
Thirdly, Organizational ability:
AI technology places higher requirements on companies' organizational ability. Not only is an inter - departmental cooperation system needed, but also versatile employees, especially those who can integrate technology, business, and management, are rare. Research data shows that a company that really wants to carry out AI - related work must have a proportion of more than 15% of versatile employees, otherwise the success rate of the project will drop significantly. Most importantly, there is the maturity of change management. When companies are faced with the process restructurings and organizational changes brought about by AI, they often lack a systematic strategy. Many companies ignore the adaptation and participation of employees when introducing the technology, which makes the changes difficult to implement and may even trigger internal contradictions.
Fourthly, Economics:
The goal of introducing AI in companies is to reduce costs and improve efficiency. However, if one only looks at short - term cost savings, one will easily overlook the long - term value of AI application. In fact, the real economic value of AI is reflected in efficiency improvement, quality optimization, and business model innovation. Therefore, when evaluating the return periods of AI investments, companies should pay more attention to long - term revenues than short - term cost savings. The ROI cycle should be planned on a 3 - year basis. At the same time, a buffer zone of at least 30% should be reserved in the budget for errors to account for the uncertainties brought about by technological iteration and in - depth application.
The above four dimensions are important considerations that companies should not neglect when introducing AI. When implementing AI, companies must make a systematic plan in combination with their own development stage, resources, and capabilities. If these key factors are ignored, the introduction of AI may not only fail to create the expected value but also increase the company's burden and even trigger a series of problems.
Currently, most business leaders of traditional companies are in a state of AI anxiety. On the one hand, they urgently want to improve their company's competitiveness by introducing AI; on the other hand, they are full of doubts about the actual value, application path, and return on investment of AI. Now, we come to the focus of this article: Which companies are not suitable for introducing AI technology? Mr. Yang has summarized the following points:
Firstly, Companies with a weak digital foundation:
Such companies often lack a complete information infrastructure. The phenomenon of data silos is very prominent, the standardization level of business processes is low, and they cannot even perform basic data collection to support the training and application of AI models. Research data shows that for companies whose critical business process computerization rate is < 50% and which have more than 3 data silos, the success rate of AI application is less than 20%.
Secondly, Companies with non - standardized business processes
For example, in companies for high - end customized clothing, each piece of clothing is designed and manufactured according to the individual wishes of customers. From fabric selection, fashion design to size adjustment, there are very high customized requirements. Data shows that in such companies, less than 30% of the data is standardized. AI models cannot receive enough effective inputs and cannot develop reusable intelligent capabilities. If such companies blindly introduce AI, not only can the expected effect not be achieved, but it may also lead to greater cost waste due to misjudgments of the model and wrong resource allocation.
Thirdly, Companies with rigid management thinking
Such companies often have a strong path - dependence. Management is used to the traditional management model and has a natural aversion to change. Even if AI technology is introduced, the application of AI will be difficult to implement due to the incompatibility of decision - making mechanisms, corporate culture, and the review system with the requirements of digitalization. Research data shows that when the average age of middle - level managers in a company is > 45 years, the acceptance of technology drops by 37% and the resistance to implementing changes increases by more than 50%. In this case, AI not only cannot achieve its expected effect but may also exacerbate internal conflicts and hinder the normal operation of the company.
Fourthly, Cost - sensitive survival companies
Currently, most companies are in a survival mode. Facing a tight capital flow and high cost pressure, these companies often cannot bear the high initial investments for AI technology, including the purchase of hardware, software development, and the recruitment of professionals. Data shows that for companies with an annual turnover of < 500 million yuan, an AI budget of < 1% of the turnover, or a gross profit of < 20% and an ROI requirement of < 18 months, the failure rate of AI projects is over 65%. Such companies should first ensure the health of their cash flows and strengthen basic management, rather than blindly following technological trends.
Fifthly, Technology - illusion companies
Such companies often have overly high expectations for AI technology and believe that AI can solve all problems. They even hope that the introduction of AI technology can quickly overturn the industry structure. At the same time, they believe that buying hardware or a large - scale model is equivalent to obtaining digital capabilities. In fact, such companies often have no clear understanding of the boundaries of AI technology and the implementation path. They have neither a clear plan for application scenarios nor the corresponding technological adaptation ability. Data shows that over 60% of AI projects fail because the requirements are not clearly defined or the technology is separated from business operations.
Then, how can companies scientifically promote AI transformation?
Mr. Yang gives the following recommendations:
1. First digitization, then AI implementation, strengthen the data foundation, connect the data chains, and ensure that the data is accessible, quantifiable, and analyzable.
2. Make rational decisions and act according to one's own capabilities, choose the entry point for AI application in combination with the actual development stage of your company and avoid blindly following trends.
3. Start with small scenarios, verify the value and then gradually expand the application scope to avoid making overly large investments from the start and making it difficult to evaluate the results.
4. Strengthen organizational cooperation, promote management reform, form a team of versatile employees, and improve your company's adaptability to new technologies.
5. Pay attention to technological adaptation, choose suitable AI tools and models in combination with your own business needs and avoid overly focusing on technological advancement and overlooking implementability.
6. Continuously iterate and dynamically optimize, constantly collect experience in AI application, adjust your strategies, and ensure that technology application and company development are synchronized. At the same time, you should build a scientific evaluation system to form a closed - loop feedback from data quality, model performance to business success and improve the sustainability and scalability of AI projects.
7. Strengthen risk awareness, identify and prevent in advance the technological, legal, and ethical risks that AI application may bring and ensure that the use of the technology is legal, transparent, and controllable.
8. Deeply integrate AI application into corporate culture, improve the awareness and acceptance of all employees for digital transformation and create an open, tolerant, and innovative organizational climate.
9. Be down - to - earth and proceed step - by - step in implementing AI changes instead of blindly following trends.
Finally, Mr. Yang wants to say that although AI is powerful, not all companies are suitable for embracing it. Technology is only a means, and commercial value is the goal. Only rational decision - making can really use AI for the growth of companies.