Learn the business value of AI’s various techniques

It is important for business managers to know which AI and machine learning techniques to deploy for which business problems.

For AI implementations requiring transparency and explainability, companies may want to stay away from deep learning techniques, which can result in so-called black box algorithms that are difficult for humans to understand. In these cases, Globant’s Lopez Murphy finds clients turning to decision trees or logistic regression algorithms for explicitly reporting the impact of a variable.

Recommender engines, employed to great effect “Many of these techniques [e.g., recommender systems and decision trees] have been available and used before deep learning, but are as relevant today, if not more so than they were before,” Lopez Murphy said. These types of applications are also able to take advantage of data that is generally more available, curated and relevant than what is required to build deep learning applications.

Debu Chatterjee

Debu Chatterjee, senior director of platform AI engineering at ServiceNow, said the IT services software company regularly uses a variety of machine learning capabilities outside of deep learning to drive business value from AI, including classification, identifying similarity between things, clustering, forecasting and recommendations. For example, in service management, incoming tickets are initially read and routed ServiceNow also uses machine learning for pattern recognition. During a major event, many people call the service organization, but each IT fulfiller only sees one incident at a time, making it nearly impossible to manually recognize the overall pattern. Chatterjee said clustering techniques using machine learning can recognize the overall patterns to identify a major incident automatically, which can help to reduce the overall time to resolve incidents and events.

Solving business problems with machine learning

According to Sachin Vyas, vice president of data, AI and automation products at LTI, an IT consultancy, some of the most popular machine learning-based prediction and decision-making applications used to solve specific business problems include the following:

• Classification algorithms help scan through data to arrive at classes or categories of outcomes. For example, customer sentiment analysis from various posts, comments and other feedback can be collected, and sentiment can then be classified as positive or negative.
• Clustering algorithms aim to identify clusters of entities having similar traits. This is seen in marketing where a cluster of people identified by the algorithm as having expressed positive sentiments about a product are targeted for “new offers” campaigns.
• With back-dated time series data available, you can analyze various seasonal, temporal and external influences within your data and project future numbers with simple forecasting algorithms: e.g., predicting the effect that a marketing campaign will have on sales.
• If your intent is to arrive at a potential number — a revenue upswing or the number of products you will be able to sell by market or geography — then you will need to apply regression algorithms.