This article series is designed to provide you with insight on the ways artificial intelligence (AI), machine learning (ML), and deep learning (DL) can be (or already are being) utilized in a variety of industries.
Although artificial intelligence (AI), machine learning (ML), and deep learning (DL) have been around for more than half a century, we still aren’t anywhere close to being able to create complex machines that possess all the characteristics of human intelligence. However, we have learned how to get computers to perform certain tasks – especially repeatable tasks – as well as, or in some cases, better than human beings. And, we have learned how to embed AI, ML, and DL into business processes to generate insights that can have a profound impact on the way an organization sets themselves apart from their competitors.
In fact, AI, ML, and DL are being used in this manner today to:
- Disrupt industry leaders (by enabling smaller companies with fewer resources to create innovative techniques that challenge the established way of doing business – for example Uber and Airbnb)
- Expand a company’s demographic reach (by predicting customer behavior, evaluating customer sentiment towards products and services offered, and using predictive analytics to gain insight into how a new product or service will be accepted)
- Personalize interactions with customers
- Improve IT operational efficiency
- Detect fraud – not only after it has taken place – but also before a fraudulent transaction is completed
- Improve employee and/or operational productivity (by looking at behavioral patterns of employees or characteristics of targeted operations)
The use cases for AI, ML, and DL are endless. And research has shown that companies are rapidly becoming adopters. In a recent study titled, The State of Machine Learning Adoption in the Enterprise, O’Reilly noted that “49% of organizations reported they were exploring or looking into deploying ML, while a slight majority of 51% claimed to be early adopters.” Moreover, Forbes’ Roundup Of Machine Learning Forecasts And Market Estimates, 2020 states that one in ten enterprises now use ten or more AI applications.
Chatbots, process optimization, and fraud analysis lead in use cases. And according to MMC Ventures’ The State of AI Divergence, 2019, report (referenced in the Forbes report just cited), the most prevalent AI applications include consumer/market segmentation (15%), computer-assisted diagnostics (14%), call center virtual assistants (12%), sentiment analysis/opinion mining (12%), face detection/recognition (11%), and HR applications such as those used to screen resumes (10%).
In the next part of this article series, we’ll take a look at how AI, ML, and DL are being used in the financial sector.
The State of Machine Learning Adoption in the Enterprise: (O’Reilly)
Roundup Of Machine Learning Forecasts And Market Estimates, 2020 (Forbes)