AI News, How to Determine the Best Artificial Intelligence Application Areas in Your Business
- On Sunday, September 30, 2018
- By Read More
How to Determine the Best Artificial Intelligence Application Areas in Your Business
Episode Summary: This week’s episode of the AI in Industry podcast focuses on two main questions.
This week, we interview someone who has spoken with a number of CTOs and CIOs about early adoption strategies for machine learning for customer service, marketing, manufacturing and other applications.
He has over 20 years of experience in developing large scale systems for telecommunications, media, automotive and financial services industries.
Prior to AWS, he built the fleet management system called TATA Fleetman for automotive original equipment manufacturer TATA Motors, as well as the third-party application programming interface gateway for enterprise smart payment systems at Ezetap.
(Note: This interview was part of a broad series of interview with executives and Indian government officials, focusing on the economic impact and opportunity of artificial intelligence in India.
That full report is available online, titled Artificial Intelligence in India – Opportunities, Risks, and Future Potential.) (2:43) You speak with developers and business leaders, encouraging them to adopt some of Amazon’s machine learning (ML) capabilities.
Madhu Shekar: When we look at the challenges that enterprises face, whether it is customer service, sales and marketing, quality control—these are the areas I see ML in.
For example, we look at companies using machine learning in customer service or manufacturing companies looking at sales and retail execution.
Because machine learning requires a huge volume of data, determine the areas where you have the best—not the most—data and start building your competencies and mechanics.
If you are a fast-moving consumer goods company with a great amount of data on retail distribution or consumer buying behavior, focus the machine learning in that space.
(5:20) I interviewed someone that said prioritizing efficiencies may not be the best way to apply ML, and here you are saying “think about competitive advantage first”.
For example, one executive asked “do you want to get started with machine learning because you want to achieve advantages in, for instance, customer services or sales where they want to use ready-made solutions such as Amazon Polly or the Amazon Lex chatbot engine.
MS: When a business faces challenges in data organization, we start looking at the data lake to determine what is clean and unclean data.
Once this is set, the next step is to build the data pipelines to process and cleanse the data, and make it available across the entire organization.
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