During her more than two-decades-long career in technology, Danielle Brown has only worked at multinational companies that are at least a century old.
Brown spent 16 years at chemical giant DuPont before joining boat manufacturer Brunswick in 2016 as chief information officer. Wanting to work for a larger company, she left that role in 2020 to take on a similar position at household appliance maker Whirlpool.
“You need to know IT, but you also need to know the business,” says Brown.
At DuPont, Brown learned to connect the dots between IT and the more than 10 business sectors the company operated in at the time, ranging from agriculture to paints to electronics. At Brunswick, she was more narrowly focused on maritime technologies and expanding the company's e-commerce business. At Whirlpool, she says technology and data underpin all of the company's strategic initiatives and is therefore a big part of her job.
Before Brown joined, Whirlpool was caught up in the Internet of Things (IoT) hype cycle. It was a period when many manufacturers were attaching sensors to products like household appliances, automobiles, and exercise gear, in an effort to collect data that could be used to help consumers and spur future innovation.
Whirlpool followed the trend. It added Wi-Fi-enabled water leak detectors to appliances that it pitched as being able to lower a customer's water bill and "smart" ovens that could get software updates automatically, like the newer "air fryer" mode, to go along with traditional baking and roasting settings.
Many manufacturers, burned by a consumer pushback against IoT, have since become more pragmatic. Brown says Whirlpool can learn from the shakeout as it now bets on generative AI technologies, such as when to use AI and how to organize and cooperate across business units.
Whirlpool is still mostly in the “discovery and piloting” phases of generative AI, Brown says. That means focusing on a few key areas, including helping software developers write code using Google's Gemini and GitHub tools.
Whirlpool is also testing using large language models to digest its trove of text-based product information that customer service representatives rely on to handle customer inquiries and make recommendations. Brown is also looking at new ways to organize structured and unstructured data from a variety of departments—legal, finance, marketing, and communications among them—and create summaries and insights from that data.
For generative AI, Whirlpool's tests mostly rely on Google's tech products, including the tech giant's BigQuery data warehouse, which has some AI tools built within it.
Brown is more cautious about deploying generative AI to customers, partly due to concerns about governance and ethics. Another worry is costs: Whirlpool operates in a sector with low profit margins, which means tech spending must be kept under control. For customers, who pay a lot of money for Whirlpool appliances, cost is also at play. If a customer's refrigerator is broken, and the company's AI chatbot fails to give accurate advice about how to fix the problem, customers can quickly grow frustrated about the food inside spoiling.
“We want to make sure we’re making it better, not harder, for the customer,” says Brown.
John Kell
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