The Impact of Machine Learning Across Verticals and Teams

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Machine learning, deep learning, and artificial intelligence are buzzwords for good reason—these technologies are fundamentally shifting the nature of business, society, and our lives. More importantly, across many verticals, they’re shifting from being disruptive technologies to being foundational and table stakes for businesses to remain competitive.

One way to slice the data science space is into descriptive analytics, predictive analytics, and prescriptive analytics. Machine learning—also known as predictive analytics—is the realm of predicting the future, such as whether a customer will churn or not, and more general classification tasks: Is an email spam or not? Is a tumor in a diagnostic image benign or malignant? Data scientists might also be interested in why a model makes a prediction, not just the prediction itself.

Let’s take a closer look at practical applications of what this means for business leaders. Corporate training initiatives for all vertical markets must ensure a strong baseline of skills across teams and roles.

Now, let’s look at several examples of machine learning’s impact across various verticals.

Machine learning is now foundational for fraud detection, process automation, algorithmic trading, and robo-advisory. Retail Machine learning is reinventing supply chain management by optimizing supply and demand planning, improving shipping processes and reducing transportation expenses, and improving strategic sourcing.

Burgeoning industries are growing rapidly with machine learning. LegalTech is imagining a future in which machine learning is leveraged to predict outcomes of court cases based on natural language analysis of precedents. AgTech (agriculture technology) is deploying drones at scale to capture footage, and machine learning is being used to estimate crop yields.

There are also many gains in the development of ML algorithms that are vertical-independent, supporting different business functions.

Machine learning helps filter applicants in the hiring flow—but hiring models must be carefully monitored so as not to perpetuate social biases at scale.

Machine learning is used for call center routing and chatbots.

Machine learning algorithms are used for paid advertising, customer churn prediction, and targeted nurture campaigns.

In fact, any company that has an app can benefit from leveraging machine learning to determine the most effective push notifications, and any organization that has a website can leverage machine learning to personalize their customer experience by surfacing content and features that are most relevant.

Find out more about machine learning best practices in The Definitive Guide to Machine Learning for Business Leaders.