The AI Effect on Software & Product Lifecycle
Jane.ai Founder on The Facts and Myths of AI (Part 3)
Unlike traditional software, AI is constantly adjusting how it calculates outputs and how it reacts to new inputs. In other words, standard software is “set in its ways” until a full-scale update comes along. Conversely, artificial intelligence takes real-time feedback to optimize its output for future engagement. Along these lines, one of AI’s biggest impact of software development is collecting feedback from customers.
For example, the “old way” of releasing software would not include a real-time feedback loop once it’s available on the cloud. Instead, product teams would have to rely on anecdotal feedback from customers to make improvements. Today, AI software allows for instant customer feedback that drives enhancements to outputs. Using our product as an example, every response that our software generates for customers allows users to give the output a thumbs-up or thumbs-down. As a result, we’re able to gauge and record user feedback immediately to improve our future outputs.
Most importantly, these real-time feedback loops are the foundation for AI’s ability to continuously learn.
Simply put, it’s taking software to brand-new levels of capacity. For example, it’s like shifting from algebra to statistics when you think about the complexity of end results. In other words, algebra problems end with a single outcome or solution – which is similar to traditional software outputs. Conversely, artificial intelligence deals with probabilistic ranges to come up with an output. Simply put, it’s a whole new level of sophistication for software products.
Furthermore, AI’s capacity for generating effective outputs comes from learning in similar ways to human beings. In other words, artificial intelligence is capable of recording “clarification” when it doesn’t understand a new inquiry. For example, if you’re having a conversation with someone and don’t understand something – you’re going to ask them for clarification. Along these lines, AI products are asking the same questions when they face an input that’s brand-new. Said differently, users will receive a “did you mean A/B/C” response from the software to get a better idea of the request’s intent.
Ultimately, the key to this engagement is not only recording the responses but also learning from them to improve future outputs. In the end, these data points are driving future enhancements because the software is capable of “remembering” every new input to constantly improve future outputs.