Artifical intelligence and machine learning are being deployed in innovative ways to help with the global response to COVID-19. These data science use cases help increase public understanding of what is possible using AI and ML, but there’s still a long way to go, specifically when it comes to businesses looking to leverage these technologies to help weather this pandemic.
For one, as more businesses yield the benefits of natural language processing-powered analytics and conversational interfaces, the demand for single-vendor solutions is likely to increase. Once C-level executives realize they can ask AI assistants to generate reports for them on the fly, they’ll want this functionality to work across their business and not just in the one department where they’ve rolled out the new technology.
This demand for single-vendor solutions makes sense: if data is scattered in applications that use different data models, it makes things more difficult than they need to be. On the flip side, when all of an organization’s data is on one platform, it’s much easier to feed it into an AI or ML algorithm. And the more data that’s available, the more useful predictions and machine-learning models will be.
As more and more businesses are being pushed toward digital transformation during this pandemic, expect to see a rise in AI and ML adoption. Here are three things that will take center stage in the AI and ML space:
Hyper-personalization is poised to increase productivity for business software users around the world. This decade, we saw the rise of algorithmic — rather than chronological — social media timelines, which increased usage for the Twitters, Instagrams and Facebooks of the world. For business software, there is a huge opportunity to create interfaces that intelligently direct the user’s attention. Imagine: a customer relationship management system that learns from an organization’s sales history and guides reps to work more productively based on their work habits.
2. AI data cleansing becomes ubiquitous
Across a variety of businesses, we expect to see a lot more places where AI data cleansing gets implemented. Smaller organizations will begin to expect AI functionality in applications such as spreadsheets, where they’ll be able to parse information out of addresses, or swiftly clean up inconsistencies. Larger organizations will benefit from AI that makes their data more consumable for analytics, or preps it for migration from one application to another.
3. Auto-tagging hits the mainstream.
As any current-generation smartphone owner has discovered, today’s devices can recognize and tag objects in photos, making personal photo libraries easily searchable. In the coming year we will begin to see this applied to business applications — auto-tagging information to make it much more accessible. Today, organizations can find their top customers in a CRM system by running a report and sorting it by revenue. In the coming years, however, they will be able to search for “top customers,” and the CRM will instantly know what they’re looking for.
In certain scientific circles, there is a belief that the world’s first 200-year-old human has already been born. That’s a lot to wrap your head around, but a similar philosophy can be applied to AI and ML. While we do expect major movement in the areas discussed above over the coming years, there will no doubt be another swath of innovations and revelations that perhaps no one has thought of — yet.