Artificial intelligence needs to be 'fed' before it can be implemented

In recent years, generative artificial intelligence has rapidly gained popularity and become the "new favorite" of digital transformation for enterprises. Whether it's customer service, product development, or internal collaboration, AI is highly anticipated. But reality is not so ideal. According to an industry survey, 67% of companies have not been able to smoothly enter the practical stage of their generative AI projects. It seems that AI is very intelligent, why is it so difficult to actually implement it?

The answer is actually very simple - the data is "not awesome".

Many companies believe that with AI tools, they can immediately transform their business. But in fact, the key to whether AI can work is what data you feed it. Low quality, chaotic, and irrelevant data not only fails to 'feed' AI, but also 'poison' it. At this point, the role of data scientists becomes crucial.

Data Scientist: The 'Translator' Connecting AI and Business

Data scientists are not "bookworms" engaged in scientific research, but experts who can transform complex data problems into commercial value. Their work covers multiple aspects such as data organization, model training, and algorithm optimization. In AI projects, they mainly address three key challenges:

1. Data chaos: Although there is a lot of data in the enterprise, most of it is unstructured, isolated, and even unrelated to the business. Data scientists will screen, integrate, and clean this data to ensure that it is' clean, relevant, and usable '.

2. Model complexity: AI models are not "plug in and run", they require repeated experimentation and optimization. Data scientists will strike a balance between model accuracy, running speed, cost, and compliance, and recommend the most suitable technical solutions.

3. The results are difficult to understand: AI is often criticized as a "black box", and the lack of transparency in the results makes it difficult for management to trust. Data scientists can translate complex model outputs into understandable business language, helping teams build trust.

Not just going online, but more importantly 'nurturing' it

AI is not a one-time deal. Just as humans can change with environmental changes, AI models may also experience performance degradation due to changes in data, user behavior, and other factors. Many pilot projects fail because no one continues to maintain these systems.

Data scientists know how to monitor model performance and make timely adjustments when problems are discovered, such as updating data sources, retraining models, modifying parameters, etc. Their job is like the "doctor" and "coach" of AI systems, ensuring that AI continues to bring value through continuous evolution.

The key to the successful implementation of AI is not algorithms, but people

Nowadays, more and more companies are realizing the importance of data scientists. A survey shows that 41% of customer experience teams are planning to recruit data scientists or AI experts to ensure that AI transformation is truly implemented and has long-term effects.

Ultimately, no matter how advanced AI technology is, if there is a lack of understanding and management of data, it will only be "talk on paper". To truly create business value for enterprises, AI must put effort into data, and data scientists are indispensable "behind the scenes heroes" in this AI revolution.