You would frequently try to wrap your head around the importance of the inclusion of AI in your business. Read more to know what to ask yourself before proceeding.
AI is quickly becoming all the rage in businesses today. Many have successfully used it to drive down costs and increase revenue—every company’s dream.
Yet AI shouldn’t be a goal in and of itself. It’s a very powerful tool and often transformational, but it’s silly to sprinkle AI on a project for its own sake. AI models typically aren’t cheap or easy to produce, so using AI unnecessarily will waste money and time instead of saving it.
So should your business be using AI? Maybe. It depends on the problem you’re trying to solve. To help you understand when AI is helpful and when it’s a waste of resources, here are two examples of AI usage, one unnecessary and one additive to the company’s business goals.
Unnecessary AI Usage: Marchesa’s “Cognitive Dress”
At the 2016 Met Gala, Marchesa, a fashion brand specializing in women’s clothing, debuted what they called a “cognitive dress.” Some weeks prior to the event, they had partnered with IBM’s Watson division to develop a dress that incorporated AI in some way.
The result was a high-fashion dress covered in LEDs, which lit up in different colors. The patterns of the lights were determined in real-time by Watson, which analyzed the tone and sentiment of tweets about the dress, and transmitted the results to a small computer embedded in the fabric. If the tweets were positive, the lights would behave in one way, and if they were negative, they’d do something else.
Did Marchesa achieve anything with their cognitive dress? If the goal was to somehow add AI to a piece of clothing, then one could argue the answer is “yes.” It certainly generated a lot of publicity for both Marchesa and IBM, although no further AI partnerships developed.
But the AI piece of the dress wasn’t particularly complicated, nor did it serve any business purpose. This dress wasn’t made to be sold; it was merely an excuse to sprinkle AI onto fashion. So while it was a cool product and provided publicity, it did not drive revenue in a way that justified the expense of producing it. Publicity alone might have been a worthy enough goal for Marchesa and IBM, but for most companies,
AI needs to impact the bottom line more directly.
Smart AI Usage: Solving a Business Problem
In contrast, an elite fabric company based outside of Milan married AI with fashion in a way that greatly enhanced its business.
A major part of this company’s business involves designing and supplying raw fabric materials for high-end fashion designers such as Gucci and Louis Vuitton. Their campus is an enclave of artists and illustrators who create drawings, some of which eventually become fabric. Because they’ve been doing this for more than 75 years, they might have, for example, 5,000 different polka dot designs spanning decades, some of which were chosen and manufactured at the time, and some of which were not.
Clients often came to them and requested designs similar to some examples, and they’d put their artists to work coming up with new options. Even though they had an enormous back catalog of drawings of fashion fabrics, they had no way to locate, say, some third-choice design from the 1970s that would probably be exactly what their customer wanted in 2020, because their warehouse contained thousands and thousands of files and fabric swatches organized by year.
Enter AI. The company created an AI visual-based similarity search, which could identify designs from their back catalog that were visually similar to an example or even a specific aesthetic that their customer wanted. Suddenly, those 5,000 polka dot designs could be used again. They didn’t always need to have their artists create new drawings for every customer request because they could give them ten options they already had on file.
This AI usage solved a specific business problem and provided a far more efficient way of serving their clients, saving them time and money in labor costs.
Identify Problems AI Should Solve
AI is a useful tool, but it’s not the right tool for every situation.Just as you wouldn’t use a screwdriver on a nail, you shouldn’t use AI for every problem your business faces.
As Cassie Kozyrkov, Chief Decision Scientist of Google, said, “Find a good problem to solve and may the best solution win. If you can do it without AI, so much the better. ML [machine learning]/AI is for those situations where the other approaches don’t get you the performance you need.”
For more advice on when and how to use AI in business, you can find Real World AI on Amazon.
Alyssa Rochwerger is a customer-driven product leader dedicated to building products that solve hard problems for real people. She delights in bringing products to market that make a positive impact for customers. Her experience in scaling products from concept to large-scale ROI has been proven at both startups and large enterprises alike. She has held numerous product leadership roles for machine learning organizations. She served as VP of product for Figure Eight (acquired by Appen), VP of AI and data at Appen, and director of product at IBM Watson. She recently left the space to pursue her dream of using technology to improve healthcare. Currently, she serves as director of product at Blue Shield of California, where she is happily surrounded by lots of data, many hard problems, and nothing but opportunities to make a positive impact. She is thrilled to pursue the mission of providing access to high-quality, affordable healthcare that is worthy of our families and friends. Alyssa was born and raised in San Francisco, California, and holds a BA in American studies from Trinity College. When she is not geeking out on data and technology, she can be found hiking, cooking, and dining at “off the beaten path” restaurants with her family.
Wilson Pang joined Appen in November 2018 as CTO and is responsible for the company’s products and technology. Wilson has over nineteen years’ experience in software engineering and data science. Prior to joining Appen, Wilson was chief data officer of Ctrip in China, the second-largest online travel agency company in the world, where he led data engineers, analysts, data product managers, and scientists to improve user experience and increase operational efficiency that grew the business. Before that, he was senior director of engineering at eBay in California and provided leadership in various domains, including data service and solutions, search science, marketing technology, and billing systems. He worked as an architect at IBM prior to eBay, building technology solutions for various clients. Wilson obtained his master’s and bachelor’s degrees in electrical engineering from Zhejiang University in China.
Wilson Pang
CTO at Appen
Wilson Pang joined Appen in November 2018 as CTO and is responsible for the company’s products and technology. Wilson has over nineteen years’ experience in software engineering and data science. Prior to joining Appen, Wilson was chief data officer of Ctrip in China, the second-largest online travel agency company in the world, where he led data engineers, analysts, data product managers, and scientists to improve user experience and increase operational efficiency that grew the business.
Alyssa Rochwerger
Director of Product Management at Blue Shield of California
Alyssa Rochwerger is a customer-driven product leader dedicated to building products that solve hard problems for real people. She delights in bringing products to market that make a positive impact for customers. Her experience in scaling products from concept to large-scale ROI has been proven at both startups and large enterprises alike. She has held numerous product leadership roles for machine learning organizations. She served as VP of product for Figure Eight (acquired by Appen), VP of AI and data at Appen, and director of product at IBM Watson. She recently left the space to pursue her dream of using technology to improve healthcare. Currently, she serves as director of product at Blue Shield of California.