With the commercialization of artificial intelligence, it is difficult for Sister Qiao to cook without rice.
Although AI business monetization is difficult, there are still many companies that continue to deepen their layout. The reason for digital and intelligent product technology talents is that AI will be an indispensable capability for enterprises in the longer term, just like today's cloud infrastructure.
The positioning of AI products determines how to think about its value system, and commercialization should not be the first priority, but how to make the deep integration of AI technology and business play an irreplaceable role in key business nodes. Just like water, electricity, and coal, today you will think about whether the cost of water, electricity and coal is reasonable? Or ask another question.
If you renovate your house today, will you refuse to install it because the cost of laying water pipes and electrical wiring is too high? Imagine if there was no water and electricity in the home, would the house still be inhabitable? If AI is positioned as infrastructure, it means that the computational logic of the input-output ratio does not work. Instead of calculating one-time inputs on a single cost of a line of business, focus on its reusability and long-term utility.
For multi-business unit group enterprises, more emphasis is placed on the contribution of AI capabilities to the global business rather than to the contribution of single point services. At the same time, it is necessary to consider the current status of the digital capabilities of enterprises, and what kind of AI products are most suitable to invest in at what nodes.
If there is a lack of information system and data cannot be opened, the large-scale introduction of AI products in such a state may bring great disaster. Better than you bought a set of high-end cooking tools but lacked high-quality fresh ingredients, it is difficult for a woman to cook without rice. The long-term value of AI is better than the short-term input-output ratio. Prerequisites for AI commercialization:
1. Complete information system.
2. Data interconnection.
3. Innovation of organizational mechanism.
Get value information. Public number: Xia Yehua. Video number: Eva product strategy.
