Summary of Key Points
This article exposes the bubble in the industry narrative that “every company needs AI.” In the past two years, numerous companies have rushed to adopt AI initiatives, but the vast majority have achieved little to no results. The root cause lies in a common misconception among the public: equating the improved efficiency of individual AI tools with increased corporate profitability, while overlooking the actual survival needs, foundational requirements, and high costs associated with implementing AI. Only a few companies are truly benefiting from AI, with most opportunities lying with suppliers that have the capability to provide practical, actionable solutions.
1. The Misconception about AI Tools: Personal Efficiency Does Not Equal Corporate Profitability
Many people believe that if employees can use AI to write content or generate reports more quickly, it will directly lead to higher corporate profits. This is a significant misconception:
- At the individual level: AI tools can indeed save time—for example, completing a report that would normally take three hours in just one hour.
- At the corporate level: What companies want is increased revenue and reduced costs, not just faster employee productivity. If employees use AI to save two hours but then waste that time or produce content that does not generate new customers, the savings are meaningless. The core goal for businesses is to grow sales and cut expenses; AI must directly help them secure orders and eliminate inefficiencies.
2. The Real Challenges Faced by Companies
The industry often portrays AI as a necessity, but it ignores the real difficulties faced by most companies:
- The primary concern of small and medium-sized enterprises (SMEs): Surviving is their top priority. Many SMEs, such as small restaurants or factories, struggle with daily issues like securing customers and managing cash flows. They lack access to targeted customer data and efficient operations, making AI an unimportant consideration for them.
- Lack of foundational capabilities: Many companies do not even have a solid digital foundation. For instance, customer information may be stored in Excel spreadsheets, and business processes rely on verbal communication, leading to disorganized data and poor management. Attempting to implement AI without these basics not only requires investing in tools and training staff but also involves reengineering processes, further depleting their already limited resources.
3. Real-World Data Debunks the Myth: 90% of AI Projects Fail
Relevant international studies confirm this misconception:
- According to MIT research, 95% of corporate AI projects fail to be successfully implemented.
- Only 2% of companies realize tangible benefits from AI.
- Corporate AI tools (such as intelligent customer service systems and data analysis platforms) are often abandoned within 4–6 months due to employee unfamiliarity or subpar performance.
- Even for companies with a better digital foundation abroad, the implementation process is challenging; in China, where data scarcity and chaotic processes are common, the success rate of AI projects is even lower. As a result, many companies have begun to slow down their AI investments.
4. The High Cost of AI Transformation
The public often assumes that implementing AI simply involves purchasing a few tools. However, this is far from the case:
- AI requires a systematic approach: Effective AI solutions require building a customized “context layer” (a combination of company-specific data and process rules), which typically requires the effort of at least 100 person-months (e.g., 10 engineers working for 10 months) and is therefore very costly.
- Long and uncertain return on investment: Even if companies invest in AI, they may not see immediate benefits, and whether it will ultimately be profitable remains uncertain. For example, a company could spend millions on AI without seeing returns for years, making such investments a high-risk undertaking.
5. Who Can Really Benefit from AI?
Only a few companies can truly benefit from AI—those with substantial financial resources, a solid digital foundation, and stable business operations. The real opportunities lie with suppliers who can help companies overcome practical challenges related to data management, process optimization, and staff training. In other words, AI is not a panacea; companies need to carefully consider whether it is necessary for their needs before investing.
In summary, AI is not a silver bullet. Before rushing into AI initiatives, businesses should ask themselves: “Do I really need AI? Can it help me solve my survival challenges?” Otherwise, the investment will be in vain, leading only to wasted money and unnecessary anxiety.