The next step in AI Optimization is even more unknown than the AI Overview. However, in my opinion, the basic approach is the same as the AI Overview. And it is important to provide valuable information based on the user’s situation and context
. First, I will introduce the features of current generative . AI tools and explain why countermeasures are difficult and why basic countermeasures are not significantly different from the AI Overview.
Information acquisition and output methods differ depending on the generation AI tool
Each generative AI tool collects and processes information on the web in its own way. For example, Gemini has strong ties with the top telemarketing data rankings in Google searches, and Grok from Company X (formerly Twitter) obtains real-time information in addition to web information. These tools, including ChatGPT and Perplexity, have only just been implemented, so effective countermeasure patterns have hardly been found.
However, on the other hand, this situation may be an opportunity to gain first-mover advantage if you keep your antenna up and take measures in advance. By understanding the characteristics of each tool and incorporating them into your company’s marketing strategy, you may be able to differentiate yourself from your competitors. However, these tools are constantly evolving, so you need to be flexible in your response.
How can we deal with black-boxed AIOs?
Currently, generative AI systems do not have emotional Intelligence: how to apply It to Your business culture indicators or analytical tools for businesses, so they are like a “black box” for marketers. There are three important points to understand about this black box state.
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- Information is dynamically generated through dialogue between users and AI
- The AI technical model itself is inference-based and difficult to predict
- AI technology is advancing rapidly, and even if a solution is found, it will change quickly.
The first change is the change in user behavior from “search” to “dialogue” . Generative AI can provide content one after another in phone number qatar response to user interactions. Therefore, it is difficult to find easy-to-understand indicators like search keywords, which have been the premise of SEO measures, and a method for measuring effectiveness has not been established.
Another factor that makes predictions difficult is that the AI technical model itself is inference-based. However, this may improve as technology develops. For example, the consistency of output in certain fields (such as drawing manga characters) has already improved.
In addition, the rapid progress of AI technology also complicates countermeasures. The optimization of the internal structure of the OS itself and websites will surely progress so that AI can be used as an agent to support user behavior. However, it is difficult to predict at this point when and how this will affect marketing activities.