Summary of Key Points
The widespread adoption of AI is achieving a form of "intelligent equality" – professional skills that previously required long-term development (such as customer service, copywriting, and analysis) can now be quickly acquired through general-purpose large models, leading to an overall improvement in business efficiency. However, this also brings two "traps of mediocrity": firstly, companies may overly focus on achieving tangible efficiency improvements (e.g., optimizing old processes) at the expense of innovation; secondly, reliance on AI can result in the externalization of decision-making, leading to homogenized outputs and a loss of independent thinking. To overcome these challenges, companies need to shift from merely using AI tools to managing intelligent capabilities tailored to their own needs. This involves accumulating data, fostering cross-departmental collaboration, leveraging their strengths, and cultivating a learning culture to transform general AI into unique, replicable core competencies.
1. Intelligent Equality: AI puts companies on an equal footing
Previously, only large corporations or experienced teams possessed certain professional skills (e.g., writing compelling marketing copy or handling customer inquiries efficiently). Now, with the use of general-purpose large models like ChatGPT and Wenxin Yiyan, these capabilities are accessible to everyone. It's similar to how smartphones have made computers commonplace; basic competencies have been leveled out. For example, small companies can now offer intelligent customer service with comparable speed and quality to larger firms, and startups can use AI to create plans that are as effective and professional as those developed by experienced planners. The key question becomes not whether a company has access to AI but whether they can effectively integrate it into their operations.
2. Two Traps of Mediocrity: Avoid being misled by AI
Trap 1: Focusing solely on visible efficiency and neglecting the future
When using AI, companies often start with easy-to quantify tasks (e.g., reducing costs through intelligent customer service or saving time with office assistants). These efforts yield quick results and impressive numbers, so resources are directed towards them. However, this can lead to a narrow focus, as companies overlook the exploration of new business opportunities and unmet customer needs. For instance, an e-commerce company may use AI to improve its customer service response times by 20%, but if it doesn't use AI to analyze customer feedback, it could fall behind when competitors launch products that better meet customer demands.
Trap 2: Relying on AI for answers and losing the ability to think independently
Over time, relying on AI can cause companies to delegate decision-making to the models:
- Homogenized outputs: Everyone uses the same models, resulting in similar copywriting and planning approaches that lack individuality.
- Lack of exploration: New employees rely on AI-generated answers without attempting to think critically for themselves.
- Anchor effect: The first answer provided by AI becomes the standard for all subsequent decisions, limiting creative thinking.
3. Four Core Drivers of Success: Determining how far your AI can go
After achieving intelligent equality, what determines a company's success? According to the article, there are four key drivers:
1. **Data potential**: Your unique data is invaluable and cannot be copied by others. General models rely on public information, but your customer feedback, transaction records, and internal risk data are proprietary. For example, a chain restaurant can use AI to analyze customer ordering patterns and discover that young people prefer low-sugar milk tea on weekends afternoons, allowing it to launch targeted new products.
2. **Collaborative potential**: Effective cross-departmental collaboration is essential for real efficiency improvements. If sales forecasts are not shared with the supply chain or customer issues are not communicated to R&D, local optimizations can create more complexity. True AI empowerment integrates various business functions, such as directly sharing sales predictions with the supply chain and promptly addressing customer issues with product development teams.
3. **Strength amplification potential**: AI should be used to enhance your core strengths. For instance, experienced content creators can integrate their selection methods into AI to automate data collection; beverage companies can standardize new product training to speed up the process from development to market launch.
4. **Cultural potential**: A culture of continuous learning is crucial for leveraging AI effectively. Companies that encourage employees to share best practices and learn from failures can maximize the value of AI.
4. Three Steps to overcome these traps
To avoid the pitfalls, companies need to take the following actions:
1. Allocate resources for uncertain innovation**: Don't devote all AI resources to optimizing existing processes; instead, allocate some to exploring uncharted areas (e.g., analyzing subtle customer feedback or testing new business models). Start with small-scale experiments and be willing to accept failures, while setting clear boundaries (e.g., within 10% of the budget).
2. Support employee development**: Train employees in using AI effectively, starting with basic guidance, progressing to independent decision-making, and eventually enabling them to train the AI models.
3. Accumulate proprietary knowledge**: True AI capabilities are built on internalized knowledge. This can be achieved by testing different models with your company's data and processes to ensure that your business insights and methods are not lost when switching models.
In conclusion
AI provides valuable tools, but it cannot determine the future for you. The winners will be those companies that transform general AI into their own unique competitive advantages. As technology becomes more widespread, skills that require long-term development (such as data analysis, cultural understanding, and core competencies) become even more valuable. Don't settle for being average users of AI; instead, strive to become leaders in leveraging its full potential.