Data-Driven AI is the future of customer experience, François Ajenstat told us at a recent interview. François is Chief Product Officer at Amplitude, the company behind a digital analytics platform aimed at helping B2B and B2C businesses build better products, websites and ecommerce experiences through behavioural data. François stressed the significance of data-driven AI within analytics but also delivered a clear warning: Don’t fall in love with what you have built! Focus on delivering second-to-none customer experiences instead. He emphasised that implementing chatbots without purpose isn’t beneficial, noting that too often, in this new world, businesses rush to add chatbots but it doesn’t make “anybody happier. No. People are still frustrated.”
Data-Driven AI Is the Future of Customer Experience
AI Integration and Implementation in analytics, what does it mean?
François Ajenstat. While AI capabilities have existed for years through statistics and machine learning, generative AI has opened new possibilities. We’ve integrated this through “Ask Amplitude,” allowing natural language queries with visual responses. Thus, users can simply ask questions about their most engaged users and receive actionable insights.
I could ask the system, “Who are my favourite readers, for instance?”
Absolutely. And you’d get the answer in a flash and the system would suggest what actions you should take and how you should engage them. Alternatively, it could help you visualise the journey of those users.
This is what you call the 3 key aspects of Data-driven AI Implementation
F.A. Indeed, we focus on three different areas with a massive potential impact.

* Simplification first: removing complexity through natural language interfaces. We’ve had speech-to-text capabilities for years, but users often found this feature intimidating. There used to be a learning curve before you could use it properly. Now it’s a lot easier. You just ask a question in natural language and it brings the result for you automatically.
* Augmentation is the second area: it’s about enhancing human capabilities rather than replacing them. A great example of that might be if you’re analysing some data and you want to understand the outliers* or what the key drivers are. Help me understand the root cause of this problem. This is where you can unleash AI to really drill into the data on your behalf and come up with insights. So we’ve added those capabilities in our product. We’ve also added what we call a data assistant, which will tell you automatically where there are data quality issues or improvements.
* Last comes Automation: this is where you find workflows and you ask AI to execute tasks on your behalf. It could be about automatically engaging users. It could be around guiding those users by delivering the right content, images, text, based on given use cases. Enabling 24/7 execution of routine tasks while allowing marketers to focus on strategy. The key thing is to engage the user at every single touch point and use AI to make every interaction a little better so you can drive a better outcome.

*Outliers (statistics): a data point on a graph or in a set of results that is very much bigger or smaller than the next nearest data point.
Do you think that AI is made for beginners or super experts like Steve Yegge?
F.A. Every time new technologies emerge, it causes fear, uncertainty,