How can you track AI's impact on user satisfaction?

Incorporating AI into your solutions requires clear objectives, and these objectives may vary depending on the type of AI, such as generative AI. Ask: Why are you using AI? If it's generative AI, is the aim unique content generation or innovative design patterns? Pinpoint key metrics, like engagement for content or user satisfaction for UX enhancements. Establish benchmarks for a before-and-after AI comparison. Emphasize feedback, especially for generative outputs, as user perceptions can vary widely. Always look ahead, considering both the changing landscape of user needs and the rapid advancements in AI technologies.

The key metric for me is what percentage of actions and decisions can be taken autonomously by AI, without user involvement. This applies to any business sector.

Net Promoter Score (NPS) can be misleading if not interpreted correctly. Many times, when NPS increases, we tend to attribute it to our own actions and efforts, but when it decreases, we blame it on external factors that are beyond our control (such as AWS outage, bad weather, holidays, etc). This is a cognitive bias, which can prevent us from learning from our mistakes and improving our performance. To avoid this, we need to find more reliable and relevant metrics that directly correlate with our business goals and reflect the true value we deliver to our customers. User feedback is only useful if we can use it to make data-driven decisions that move the needle in a specific metric that is good for the business.

Success stories. People may give negative ratings because their criteria is not suitable for AI or they are just trying to keep the situation as it is. Success stories infect people with change. They want to be just as successful.

User feedback is very valuable! Ot can be used yo improve the model functionality and make the design user friendly. More importantly, the input feedback could be used to enhance the training and the learning of the AI models.

To track AI's impact on user satisfaction, it's essential to test and optimize your AI solution. This involves ensuring that your AI is performing well, delivering accurate and reliable results, and meeting user expectations. Methods like A/B testing, multivariate testing, or experimentation can be employed to achieve this. Additionally, tools like Optimizely, Firebase, or TensorFlow can help in the testing and optimization process, allowing you to continuously evaluate and enhance your AI-driven user experience.

Collecting user feedback and analyzing user behavior are great to understand user satisfaction at specific points in time. But to understand if AI solutions are going to satisfy user needs long term, we need to monitor AI performance over time (accuracy, hallucinations etc.) and also understand if users' trust towards AI solution is increasing over time.

Tracking AI's impact on user satisfaction involves a combination of quantitative and qualitative methods. Implement user feedback mechanisms like surveys or sentiment analysis to gauge user sentiment before and after AI implementation. Analyze user behavior data, such as engagement patterns or conversion rates, to assess how AI-driven enhancements influence user interactions and outcomes, providing a comprehensive view of the impact on user satisfaction.

Processes need to be completely overhauled. The introduction of AI in the current process gives an insufficient increase in productivity and effect. We need to reinvent many industries.