Have you ever wondered how long your customers are willing to wait for an AI decision? In today’s fast-paced digital world, the answer is not very long.
Unfortunately, many modern data stacks are designed for periodic reporting and business intelligence, where latency only becomes an issue if daily, weekly, or monthly reporting deadlines are missed. However, when it comes to AI, automation often involves live decision-making where even a short delay can impact customer trust. While research shows that a brief delay can actually improve trust, this effect only lasts for a few seconds. Customers are quick to move on if they are forced to wait beyond their expectations.
The consequences of using legacy data stacks for AI can be severe. Some state-of-the-art feature types can be too slow to implement, causing data scientists to avoid using them. By ignoring valuable signals in the data, AI systems achieve reduced accuracy. Unacceptable latency and incorrect decisions in AI systems can irreparably damage the customer experience and your brand value.
The consequences of unacceptable latency in AI systems can be devastating, irreparably damaging customer experience and brand value. To prevent this from happening, it’s crucial to design an AI-ready data stack that can deliver timely and accurate decisions. This means using a feature engineering tool that can automatically generate optimized SQL and looking for intelligent orchestration capabilities such as caching of feature values and their components. By doing so, you can ensure that your AI system is capable of delivering real-time decisions without sacrificing accuracy or customer trust.