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5 Enterprise Best Practices to Profit from Machine Learning

Machine learning (ML) is one of the top digital initiatives in which companies are investing substantially. However, many do it to stay relevant without legitimate preparation. Mere deployments would not convert them into successful ventures nor bring solutions for existing problems. In this article at InformationWeek, Jessica Davis shares 5 best practices that successful organizations embraced to gain profits.

Profiting from Machine Learning

A survey done by The Economic Intelligence Unit and SAP reveals 5 enterprise best practices that are common with successful companies. The report conveys that larger companies struggle to adopt ML faster due to in-house resistance. Nevertheless, they are more successful in finding out the right talent to implement the technology than SMBs. The smaller enterprises, in turn, are leveraging cloud in place of resource pool and gaining knowledge through open source platforms. The report suggests that there are some enterprise best practices that help these companies to profit from machine learning:

  1. Prioritizing the Venture: The C-level executives are well aware of the strategic value that machine learning is going to bring to the company. So, they are ready to try out new ways to leverage the technology.
  2. Continuous Ideation: 31% of the respondents believe that machine learning ventures have helped them be active in terms of ideation. They also changed their operating models continually to stay on track.
  3. Revenue Expectations: 48% find that they are profiting from their ML adventures. The other 48% foresee 6% increase in revenue from 2018 to 2019.
  4. Investing in Home-Grown Capabilities: 58% of fast adopters invest in upgrading in-house capabilities rather than outsourcing them, compared to 39% of slow adopters. The increased consumer satisfaction drives this trend.
  5. A Holistic Approach: These companies are implementing machine learning across their organizations, even in customer-facing and product departments. Furthermore, 41% are experiencing better customer response due to ML adoption.

The survey has further suggestions:

  1. Conduct workshops to train C-level executives and business unit leaders in machine learning. This would help them understand the benefits faster.
  2. Fish out the ML ventures or use cases that were successful.
  3. Start small with specific processes where you would encounter less risk. You could then implement it across the entire unit.
  4. Compile a manual that the senior management can utilize to address adoption questions from various departments.
  5. Identify processes that need localization for better ML venture gains.

To view the original article in full, visit the following link:


Indrani Roy

Indrani Roy is currently working as a Content Specialist for CAI Info India. She has knowledge in writing blogs, product descriptions, brand information, and coming up with new marketing concepts. Indrani has also transcribed, subtitled, edited, and proofread various Hollywood movies, TV series, documentaries, etc., and performed audio fidelity checks. She started her career by articulating a knowledge base for an IT client, and, eventually, went on to create user manuals and generate content for a software dashboard. Writing being one of her passions, reading books is naturally her favorite pastime. When not lost in the world of letters, she is a foodie, movie buff, and a theater critic.

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