What Makes AI Projects Different from IT Projects

What Makes AI Projects Different from IT Projects

One of the biggest hurdles to AI adoption and integration is a lack of proper expectations about applying AI in an existing business. Executives and their teams often go into the process blind because so few companies have learned these important lessons and challenges and because even fewer have successfully adopted AI in a way that delivers ROI.

Artificial Intelligence at Square

Artificial Intelligence at Square – Two Use-Cases

Square is a financial services company that aims to “build common business tools in unconventional ways so more people can start, run and grow their businesses.” Founded in 2009 in San Francisco by Twitter Co-Founder Jack Dorsey and Jim McKelvey, Square reports total net revenue of $9.5 billion for 2020.

Artificial Intelligence at Amazon

How Amazon Uses AI in eCommerce – Two Use-Cases

Amazon is the largest online retailer in the world by market cap. Founded in 1994 in Seattle, Washington, as a book-selling platform, Amazon has become a household name offering a wide variety of products and services. As of 2020, online retail product sales account for most of the company's net revenues, followed by third-party retail seller services, Amazon Web Services, and subscription services.

Document Search and Discovery in Banking - An Analysis of the Field

An Analysis of AI-Powered Document Search Capabilities in Banking

The financial services industry is buried in paperwork, and the NLP use-cases in banking and insurance grow every year.

Artificial Intelligence at Tesla

Artificial Intelligence at Tesla – Two Current Use-Cases

Founded in 2003 as Tesla Motors, the electric vehicle and clean energy company based in California currently has a market cap of over $700 billion - making it more valuable than the top seven automakers combined. Today, Tesla is well-known for its electric vehicles but the company also produces products for sustainable energy generation and storage such as solar panels, solar roof tiles, and more to enable “homeowners, businesses, and utilities to manage renewable energy generation, storage, and consumption”. 

AI at Walmart

AI at Walmart – Comparison to Amazon, and Two Unique Use-Cases

Sam Walton opened his first Walmart in Rogers, Arkansas in 1962, capitalizing on his twelve years of success running Walton's 5&10 in downtown Bentonville seven miles down the road. Walton founded his company on the idea that retail could succeed by offering great value and great service. His competitors thought his idea was doomed to fail. Today, almost sixty years later, Walmart has transformed itself into a multinational corporate supergiant ranked as the largest company in the United States by gross sales in 2020, according to the Fortune 500.

AI at Apple

Artificial Intelligence at Apple – Two Current Applications

Founded by Steve Jobs, Ronald Wayne, and Steve Wozniak in 1976, Apple is a global technology corporation specializing in consumer devices, software, and online services. 

AI at General Electric

Artificial Intelligence and Digital Twins at General Electric

General Electric (GE) was founded in 1889 by J.P. Morgan and Anthony J. Drexel who came together to finance Thomas Edison’s research and merge their companies together. Originally, GE was an industrial and consumer products company but today, more than 130 years later, GE has transformed itself into a multinational, digital industrial corporation ranked as the 33rd largest company in the United States by gross sales in 2020, according to Fortune 500.

Artificial Intelligence at Coca-Cola

Artificial Intelligence at Coca-Cola – Two Current Use-Cases

Today, Coca-Cola is the world’s largest beverage company, selling over 500 soft drinks in more than 200 countries. In 2020, Coca-Cola had over 80 thousand employees worldwide.

Three Ways to Leverage Industry Expertise for an AI Career

Three Ways to Leverage Industry Expertise for an AI Career – A Guide for Non-Technical Leaders

As artificial intelligence makes its way into more industries and workflows, more and more non-technical team members will be charged with leading AI projects. The next wave of AI catalysts will be familiar with AI at a conceptual level (read: executive AI fluency), but will mostly be expert in bridging AI's capabilities to important business workflows and objectives.