Even though AI continues to thrive and grow, there remain challenges to use the technology. Just some include finding data scientists, determining the right problems to focus on, getting quality data and scaling the models.
No doubt, these problems are even worse for non-tech companies. They generally do not have the expertise or sufficient resources to make AI a success.
“Research shows non-tech companies in particular have struggled to take their AI programs beyond the proof of concept and pilot phases–with just 21% of retail, 17% of automotive, 6% of manufacturing, and 3% of energy companies successfully scaling their AI use cases,” said Jerry Kurtz, who is the Executive Vice President of Insights and Data at Capgemini North America.
But despite all this, there are still a myriad of companies that are beating the odds. And they are becoming much more competitive. “There are many opportunities for non-tech companies to leverage AI to improve efficiency and provide a better customer and employee experience,” said Margaret Lee, who is the Senior Vice President and General Manager of Digital Service Operations Management at BMC.
So what are some of the non-tech companies that have been able to move-the-needle with their AI efforts? Here’s a look at two and the lessons learned.
John Deere: Keep in mind that the company has a long history of innovation, going back to the invention of the steel plow in 1837. The result is that John Deere is a world leader, with a market capitalization of $120 billion and annual sales of over $35 billion.
The company’s AI efforts began with machine vision because of its advantages with GPS connectivity in its equipment and rich datasets. For example, during the spring, the peak data ingestion was 425MB per second or about 50 million sensor measurements per second.
“Our projects have resulted in products in the market, or soon to be in the market, that reduce chemical inputs but sensing weeds from non-weeds and selectively apply chemical to the weeds only,” said Jahmy Hindman, who is the Chief Technology Officer of John Deere. “In addition, this work has led to vision-based automated control systems in combine harvesters that optimize the processing settings of the ‘factories on wheels’ to minimize the grain lost during harvest.”
A key lesson for the company has been the importance of keeping customer needs and wants in mind. “We work tirelessly to help our customers advance their business and feed the world,” said Hindman.
Levi Strauss: An early project for this company came in response to the Covid-19 lockdowns in Europe. Levi Stratus looked for ways to manage the inventory pile-up. To this end, the company gathered unique datasets on dynamic price elasticities and then applied AI to it.
It was a small project but was able to scale quickly. What started as a test in 11 stores in Germany in May 2020 grew to 17 countries in Europe by October. The system was also used for the 11/11 Singles’ Day in China.
“I have three pieces of advice from this experience,” said Louis DiCesari, who is the Global Head of Data, analytics, and AI at Levi Strauss. “First, choose real, commercial problems that are aligned to your company’s strategic priorities, and chunk them into actionable steps. Second, don’t get too hung up on having perfect data or technology, or using the latest algorithms. Instead, embrace agile, deliver minimum viable products, continuously measure the impact, and continue to iterate and add new features. And, of course, set a vision, communicate the progress toward that vision throughout the organization, and invite feedback.”
DiCesari attributes AI to the company’s ability to accelerate innovation and move faster than ever before. “In 2021, we aim to deliver more value and support all countries and functions of the enterprise, infuse data and AI throughout the business, enable new ways of working and continue streamlining processes, while also digitizing assets,” he said.