{"id":13130,"date":"2024-02-22T01:16:37","date_gmt":"2024-02-22T01:16:37","guid":{"rendered":"https:\/\/www.salesforce.com\/?p=13130"},"modified":"2026-03-16T04:23:55","modified_gmt":"2026-03-16T04:23:55","slug":"ai-at-scale","status":"publish","type":"post","link":"https:\/\/www.salesforce.com\/in\/blog\/ai-at-scale\/","title":{"rendered":"This Company Saved Millions with AI \u2013 Here’s How"},"content":{"rendered":"\n

The big trend<\/h4>\n\n\n\n

You can’t scan the headlines lately without seeing buzz around generative artificial intelligence (AI). The product innovations are only beginning<\/a>. But even with the best technology out there, you’ll still be faced with a key question: How can you implement AI at scale in a way that maximises the return on your investment? Let’s look at one model company you can learn from.<\/p>\n\n\n\n

Breaking down silos <\/h4>\n\n\n\n

Schneider Electric<\/a>, a global energy management and industrial automation company, has formalised an AI program under a new Chief AI Officer and scaled it to every corner of the company. Its vision, \u201cdata and AI first,” is already paying dividends. For example, the company has saved millions by using AI to more accurately forecast and manage inventory demand. <\/p>\n\n\n\n

The backstory you might need <\/h4>\n\n\n\n

Enterprise AI<\/a> use has already doubled since 2017, but few companies are seeing\u00a0significant return on their upfront costs\u279a<\/a>, and a majority have failed to scale AI\u279a<\/a> beyond the pilot stage. Analysts say the reasons include a lack of skills, complex programming models, upfront costs, and a lack of business alignment.<\/p>\n\n\n\n

What you can do now <\/h4>\n\n\n\n

Take cues from Schneider Electric: <\/p>\n\n\n\n