{"id":77910,"date":"2024-06-06T00:00:00","date_gmt":"2024-06-06T07:00:00","guid":{"rendered":"https:\/\/www.salesforce.com\/?p=77910"},"modified":"2025-03-27T16:22:17","modified_gmt":"2025-03-27T23:22:17","slug":"ai-actions","status":"publish","type":"post","link":"https:\/\/www.salesforce.com\/news\/stories\/ai-actions\/","title":{"rendered":"Consumer Chatbot Technology Isn\u2019t Enterprise-Ready \u2013 Here’s Why"},"content":{"rendered":"\n

Editor\u2019s note: Einstein 1 Platform is now the Salesforce Platform. For the latest, go <\/em>here<\/em><\/a>.<\/em><\/p>\n\n\n\n

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With so much business interest in adopting the latest and greatest AI chatbots, it\u2019s pretty easy to miss a fundamental truth behind the hype: consumer chatbot technology isn\u2019t enterprise-ready \u2013 and probably never will be.<\/p>\n\n\n\n

Why? Because AI assistants are only as good<\/a> as the data powering them. And, as some organizations are now learning, most large language models (LLMs)<\/a> rely on information gleaned from public websites \u2014 not your more critical business and customer data. This means consumer bots simply can\u2019t adequately help employees sell more products, market more merchandise, or be more productive in their daily engagements and activities given the lack of personalization and business context needed within a workplace.<\/p>\n\n\n\n

A different approach is required to reach the vision many people have in mind for AI<\/a>, where it progresses from living in conversational chatbots that can do a handful of party tricks \u2013 like creating pretty good emails, essays, blogs, and graphics \u2013 to acting autonomously and sending out digital agents capable of identifying business-critical needs and addressing them with little or need for direct human involvement.<\/p>\n\n\n\n

At Salesforce, we see a clear path to AI that takes action and runs routine tasks autonomously, all while adhering to trusted business rules, permissions, and context. Instead of just relying on LLMs, which mainly focus on understanding and generating human-like text without the ability to independently execute tasks, future AI assistants will rely on large action models<\/a> (LAMs), which integrate decision-making and action-taking processes.<\/p>\n\n\n\n

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At Salesforce, we see a clear path to AI that takes action and runs routine tasks autonomously, all while adhering to trusted business rules, permissions, and context.<\/p>\nJayesh Govindarajan, Senior Vice President, Salesforce AI<\/cite><\/blockquote>\n\n\n\n

We\u2019ve been marching toward that vision for a while now and are making considerable progress. <\/p>\n\n\n\n

The march to autonomy<\/strong><\/h2>\n\n\n\n

The journey started with Salesforce Data Cloud<\/a>, a trusted, hyper-scale data engine, built on the Einstein 1 Platform<\/a>, that unlocks and harmonizes trapped data from across the enterprise and third-party data repositories to help companies act on their data. With Data Cloud, enterprises can effortlessly activate their data to automate workflows, personalize customer interactions, and build smarter AI. <\/p>\n\n\n\n

In addition, because we believe a move from generative AI to autonomous AI is on its way, with the prompt becoming the primary user interface and interacting with a variety of underlying language models, we released Einstein Copilot<\/a>, the industry\u2019s first and only conversational, enterprise-class AI assistant. Integrated across the Salesforce stack, Einstein Copilot is grounded in all of an organization\u2019s data<\/a>, whether it\u2019s behind their firewall or sitting in someone else\u2019s data lake or colocation facility. <\/p>\n\n\n\n