We are in an exciting time when it comes to technology. New innovations are helping us better streamline our operations, connect with our customers, and create new digital products. But there is one technology that’s not new, even though some in the tech space think it is: artificial intelligence (AI) and machine learning (ML). While AI and ML are not new, what is new, are the capabilities to execute on this technology.
The foundations of AI and ML date back to the 1950s and 1960s when modern technology struggled to leverage it. These technologies were the stuff of science fiction and fantasy, with its applications often only seen in labs. Technologists were limited by the technology, but that’s not the case anymore. Fast forward 50 years and AI and ML are being used for everything from predictive maintenance to facial recognition technology.
Emerging technologies like that of AI and ML have often been thought of as unachievable, mostly because of the state of technology at the time. But now, when the technology is advancing faster and more frequently than ever before, technologies like AI and ML are becoming more of a reality. That being said, don’t let yesterday’s technology constraints holding us back from innovating today.
If you’ve been following this series for a while, you know that you can’t have emerging technologies without the foundational technologies in place (you wouldn’t run before you can walk, would you?). In the case of AI and ML, you wouldn’t have these concepts in place without first having strong analytics, application integration, and a cloud strategy. Only when you have this, will your AI and ML have the environment that it needs to thrive in.
Over the last year, we’ve seen AI and ML advance in terms of the technology and how it can benefit businesses and consumers. Before, we were only able to apply advanced and sophisticated models for small data sets. Now, with these technological advancements, we’re now able to apply these models to large data sets and streaming data.
We’ve seen advancements in natural language processing (NLP), which has given way to unlimited applications and use cases. One such use case is Google’s Smart Compose feature that it brought to Gmail that helps users compose emails faster based on the existing copy, anticipating what words would come next.
But it’s not just technology companies who are getting in on the action; companies across other industries like manufacturing, retail, and education are implementing AI and ML to create better services and better customer experience. For example, retailers such as StitchFix, use algorithms to create a better StitchFix box based on customers’ preferences. Through their “Style Shuffle” section on a user’s profile, customers can approve or reject pieces of clothing shown to them, allowing StitchFix to get an understanding of a user’s style preferences. The algorithm takes this feedback into account, adjusting itself as it goes along to show more relevant clothing items in addition to using this information to select pieces to send customers in their StitchFix boxes.
What once was thought of as unattainable technology, AI and ML are becoming more widespread and common in the technology stack a business uses to improve customer experience, create connected digital products, and optimize operations. All three of these ingredients are crucial for digital transformation success. Emerging technologies such as AI and ML help companies drive digital maturity and digital transformation quicker and more efficiently than ever before. This just goes to show that with today’s technology landscape, anything is possible.