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Editor’s note: This article draws on insights from the CIO Dive and CFO Dive live virtual events on October 30. You can watch the sessions on-demand.
Generative AI has emerged as a technology that companies can easily access.
Executives quickly saw the potential to improve efficiency and upgrade the customer experience by deploying chatbots, document summarization tools, and digital assistants across everything from call centers to engineering teams.
However, over the past few years, widespread adoption has been hindered by ongoing concerns about AI bias, model hallucinations, and data privacy, making this technology incredibly difficult to scale at the enterprise level.
“Deploying generative AI carries some risk for any company that wants to use it.” Jaime Montemayor, Chief Digital and Technology Officer, General Millssaid while wednesday CIO Dive and CFO Dive Live Virtual Events.
Nonetheless, General Mills was well-positioned for early adoption and launched an internal generator. AI chatbot call Mills Chat to february.
When the food industry giant set out to lay the digital foundation for its AI technology, it didn’t focus on large-scale language models. Several years before OpenAI delivered its first ChatGPT iteration in November 2022, the company began building its infrastructure to support machine learning. MontemayorAppointed to lead digital transformation at General Mills i.n February 2020.
As part of its preparations, the company later migrated its data to the cloud for better analytics capabilities. affiliated with google cloud 3 years ago. General Mills has also found success expanding its ML capabilities.
“We’ve built dozens of features across the company.” Montemayor said. “We’ve deployed it across all functions of the organization – marketing, sales, supply chain – and it’s driving business value.”
Cross-functional value
From cloud and data modernization to AI adoption, C-suite buy-in for digital transformation efforts helps align IT initiatives with business goals. Building relationships across multiple company units can help sustain a project after it gets underway.
“It takes a village to succeed with AI in any company.” Montemayor said. “One of the first things we did a few weeks later was ChatGPT We have begun to build cross-functional subgroups of our senior leadership team.”
The executive team included heads of HR, legal, finance and supply chain. general mills.
Securing funding is especially helpful as companies progress from pilot to full implementation and costs begin to pile up. In addition to approving funds, finance executives can help IT identify metrics for return on technology investments.
“The finance team is the team that helps us keep score.” Montemayor said. With support from the finance team, companies can clearly see where investments or new features need to be accelerated or scaled back.
that Mills Chat Assistant is primarily a writing and summarizing tool. It was produced using Google’s PaLM 2 distributed as a model About 900 users Early this year. Currently, this tool is in the hands of: 20,000 General Mills employees; According to Montemayor.
The company’s generative AI wins were the result of workforce improvements and technology investments.
“When we started this journey, we were a traditional company and we knew we had to invest, especially in data science.” Montemayor said. “Slowly but surely over the past four years we have been building the talent base to revitalize our AI program.”
one of the keys to Montemayor It’s about measuring the capabilities of your technology against your business priorities. He also emphasized the importance of pragmatism in new technologies.
“I spend a lot of time collaborating with my colleagues to ensure we have the right balance between what is possible and what is possible as we continue to experiment and grow our pipeline of initiatives in the core AI machine learning or generative AI space. It’s truly achievable.”
