Machine learning algorithms are enabling organizations to supercharge workflow processes across their enterprises. They center around technology that has the ability to learn without being explicitly programmed: machines that can study their mistakes and reprogram themselves to improve their performance over time. Lots of big names are investing R&D dollars into machine learning. Here are some ways it can help improve business operations.
A workflow process is the backbone of just about every common business activity, whether it centers around finance, inventory or another back-office task. These processes started with paper, migrated to email and have now found their way to the cloud. And once the cloud came into play, the stage was set for machine learning to augment workflows across businesses.
Machine learning algorithms are fundamental to digital transformation because they provide the technology needed to tap all that corporate data you’ve been saving across your enterprise for years, analyzing it for deeper insights you can then apply to decision-making. Data-based insights let you streamline both customer-facing and back-end business operations by eliminating guesswork. For example, when you apply machine learning algorithms to a sales workflow process, the technology is constantly learning from its mistakes and reprogramming itself to improve performance. This can give your sales teams an edge in capturing the next deal through better targeting of both prospective and current customers.
Machine learning algorithms are also making their way into cloud-based productivity platforms. The latest release of G Suite (formerly known as Google Apps for Work) touts machine learning features that augment traditional spreadsheet and calendar-management functions.
The next generation of productivity software and machine learning might also include more intelligent document creation tools and processes.
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