“Hyper-personalization” is a popular emerging trend, but is it possible for CS teams to truly do this at scale today, even with AI? How much responsibility does marketing have here?

The key question is what we want AI to achieve. If it's about understanding customer behavior and recommending ways to enhance their experience, AI can be valuable. However, if it's about guessing personal details, the margin for error is high. AI can help understand product usage and recommend additional features, but it requires a strong data foundation. AI can be a significant asset in helping customers utilize more of a product's capabilities. Many users only tap into a fraction of what a solution offers. By leveraging AI, companies can encourage customers to explore and benefit from more features, ultimately enhancing their experience and satisfaction.

Working at a generative AI company, I can say that while AI has potential, it largely depends on the quality of available data. Many companies struggle with data accuracy, which hinders AI's ability to personalize effectively. While some companies might achieve pockets of success, widespread implementation is still a challenge.

I haven't seen AI achieve hyper-personalization at scale yet. The risk is that AI might not always get it right, and there's always nuance in customer situations. While AI has potential, especially for experimentation, it requires careful implementation to avoid errors.

Hyper-personalization is possible, but AI is only as effective as the data it has access to. If teams aren't recording necessary information, AI can't generate highly personalized outputs. For AI to work effectively, it needs quality data input. Without it, achieving true personalization is challenging. AI could potentially read account notes and personalize interactions, but without accurate data input, it won't be effective. Human oversight is still necessary. AI's potential lies in recommending how customers can maximize product use, closing the gap between current and potential usage.