AI News, How to Determine the Best Artificial Intelligence Application Areas in Your Business

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.

Infuse Your Business with Machine Learning (Google Cloud Next '17)

With machine learning, instead of programming a computer, you teach a computer to learn something and it does what you direct it to do. In this video, Lak ...

SAM Data Insights Enables Digital Transformation

Imagine having the deep data insight to lead your company to a more modern and secure digital organization and the ability to respond quickly to new business ...

Artificial Intelligence: Building the Business Case for AI (CXOTalk #246)

Artificial intelligence can make companies dramatically more efficient, but investing in the technology can come with risks and complications. Tiger Tyagarajan ...

DATA & ANALYTICS - Build smart applications with your new superpower: cloud machine learning

Recorded on Mar 24 2016 at GCP NEXT 2016 in San Francisco. Visual effects rendering is a computationally intensive process where one second of ...

Practical Business and Marketing Advice for Dominating 2019 | Keynote at NAC | Philippines, 2018

I'm fired up about this keynote that I gave at the National Achievers Congress in Manila in the Philippines, but at the same time, even though it's not a new ...

Predictive Maintenance & Monitoring using Machine Learning: Demo & Case study (Cloud Next '18)

Learn how to build advanced predictive maintenance solution. Learn what is predictive monitoring and new scenarios you can unlock for competitive advantage.

IBM Watson Machine Learning: Continuous Learning on Watson Data Platform

This video shows you how to build models that learn over time with Watson Machine Learning and Data Science Experience allowing data scientists and ...

How Publishers Can Take Advantage of Machine Learning (Cloud Next '18)

Hearst Newspapers uses Google Cloud Machine Learning infrastructure to automate and create value in the newspaper business. A recent case study has been ...

How to Build Flexible, Portable ML Stacks with Kubeflow and Elastifile (Cloud Next '18)

Building any production-ready machine learning system involves various components, often mixing vendors, and hand-rolled solutions. Connecting and ...

DATA & ANALYTICS - IoT - from small data to big data: Building solutions with connected devices

Recorded on Mar 23 2016 at GCP NEXT 2016 in San Francisco. Businesses can improve visibility into the health of their products and services using data from ...