Bringing Real-Time AI To The Core Of Your Business
Artificial intelligence (AI) is big news right now thanks to a wave of viral applications such as ChatGPT and Dall-E, which have captured the public’s imagination.
Tools such as these and many others demonstrate the quantum leap that has taken place over the last few years in what AI is capable of. And this wave of innovation is set to hit the world of business, too, with a new generation of AI-infused apps set to redefine what is possible.
The most transformational will be those capable of achieving “real-time” AI. All AI is powered by information (data), and real-time data is the most valuable data of all. After all, what’s going to be the most helpful when it comes to making decisions – knowing what’s happening right now or knowing what happened a week ago?
Some of the most useful and renowned AI-powered applications have built their success on real-time AI. Think of Netflix, which uses up-to-the-minute information to recommend what movie or show you should watch next. Or Uber, which uses real-time AI to match passengers with drivers as quickly as possible. Even Google and Amazon rely on real-time AI to put adverts in front of users and recommend products to buy based on the latest shopping trends.
For the latest edition of my podcast, I spoke to Ed Anuff, who is Chief Product Officer at Datastax – which helps businesses operationalize data and put it to work in real-time AI applications. We spoke about how businesses that are able to move quickly into the real-time AI space will steal a lead on their competition, as well as be better placed to meet and exceed their customers’ expectations.
When we talk about AI in a business context today, we generally mean machine learning (ML). ML involves algorithms that are able to become increasingly efficient at carrying out their jobs as they are trained on bigger and bigger datasets. Real-time AI refers to ML algorithms that are capable of processing and learning from streaming data that’s captured as it happens.
Banks and credit card companies monitor financial transactions in real-time as they happen in order to identify ones that might be fraudulent.
E-commerce businesses such as Amazon analyze customer behavior in real-time in order to offer products and services that they are most likely to want to buy at that moment.
Online advertising platforms like Google and Facebook analyze what users are searching for in order to serve up more relevant ads that have a higher probability of leading to sales.
Self-driving car manufacturers monitor activity around the vehicle in order to help it safely navigate to its destination while avoiding hazards.
Cyber security specialists analyze network and email traffic in real-time to attempt to predict where hacking attacks or phishing communications may come from.
Anuff tells me, “AI and ML are about making predictions, based on … a set of observations … and in a real-time context those predictions are typically ‘what’s the next product you might be interested in?’, ‘when will my package arrive?’ ‘how long until my Uber arrives?’
“Real-time is about doing this continuously based on all of the data events that are happening [right now] … real-time means something that’s not after the fact.”
Where does real-time data come from?
Business activities and interactions can be broken down into events, and each of these events, if captured in a way that makes it possible to analyze, can become a piece of a real-time streaming dataset.
