Improvements in cloud technologies and processing power have provided a solid foundation for mainstream adoption of machine learning (ML). With the ability to analyze massive amounts of data to derive meaningful insights, ML can give business leaders new ways to innovate, create new revenue streams, improve operational efficiencies, and help all employees make faster, more informed decisions.
In IDG’s 2019 Digital Business Study, 78% of IT and business leaders said their organizations are considering or have already deployed machine learning technologies as part of their digital business strategy. “We’ve seen it day in and day out with customers we support, and organizations in general, that are benefiting by leveraging machine learning,” says Sri Elaprolu, senior leader, Amazon Machine Learning Solutions Lab, a team of data scientists and machine learning experts that helps Amazon Web Services (AWS) customers successfully adopt ML.
ML and artificial intelligence (AI) already are having an impact on many different parts of the business, including:
- Sales & marketing: Personalized experiences for customers based on analysis of activities and behavioral data
- Finance: More accurate forecasting models and fraud detection analysis
- Customer support: Natural language processing that extracts insights and relationships from unstructured text, as well as voice-to-text, text-to-speech, and language translation to improve the customer experience and open up new revenue streams.
- Operations & logistics: Optimized order fulfillment and improved shipping routes for last-mile delivery of items.
- IT: Security threat detection and mitigation, improved efficiencies, and streamlined operations in areas such as software development.
Many sector-specific examples are emerging as well:
- In manufacturing, paper company Georgia-Pacific uses machine learning to detect issues earlier and maintain quality, eliminating 40% of the tears for one of its converting lines.
- In healthcare, technology company Cerner uses machine learning to help predict congestive heart failure up to 15 months before it manifests in clinical tests, improving patient care and ultimately saving lives.
- In transportation, Convoy disrupted the trucking industry by introducing a machine learning-powered model to automate logistics. Convoy’s solution provides better matches for shippers and truckers, allowing them to move freight more efficiently—and lowering costs for both parties.
Amazon is a prime example of how ML can impact every area of the business. Its use of machine learning dates back 20 years, beginning with the ground-breaking recommendation engine on its ecommerce site. “To continue to promote the use of ML throughout the business, ten years ago, leadership made the application of ML a part of yearly planning, asking each business unit to state how they would use ML in their business,” says Elaprolu. “The idea is that every product organization, every line of operation should be leveraging data and thinking about machine learning.”
For businesses that understand the benefits of ML but don’t know exactly where to begin, several tools and processes are available to help them get started. At AWS, for example, customers can participate in the AWS Machine Learning Embark Program, which includes business and technical training and a Discovery Day workshop that gathers business and technical leaders to work with AWS ML experts in the Amazon ML Solutions Lab to prioritize development on the most impactful use cases. The “working backwards” approach focuses on the business outcome first, and then determines the technology and data the organization needs to achieve that goal.
“This approach provides a clear perspective on how you can go from Point A to Point B, helps you prioritize use cases, and what you should think about near term versus recommendations for the long run,” says Elaprolu.
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For more machine learning insights from Sri Elaprolu, check out the new Ahead of the Pack podcast.