We\u2019ve already seen the impact of AI in service<\/a>. Nearly seven years ago, Salesforce launched Einstein for Service<\/a> to give agents AI-powered capabilities. These have included recommended next-best actions and responses to customer inquiries, as well as automating case summarisation.<\/p>\n\n\n\n Generative AI is about to take service operations to the next level of efficiency and personalisation.<\/p>\n\n\n\n With generative AI layered onto Einstein 1<\/a> Service, we\u2019ll have the ability to automatically generate personalised responses for agents to quickly email or message customers. We\u2019ll be able to train the AI across all the case notes ever written by every agent at the company to automatically generate drafts of knowledge articles for human review, drastically cutting the time to create knowledge and making it easier to keep articles up to date. The enhanced relevance and quality of knowledge across the company will make self-service<\/a> portals and chatbots more valuable, freeing human agents to spend more time deeply engaging on complex issues and building long-term customer relationships.<\/p>\n\n\n\n We will also see benefits in field service<\/a> with generative AI<\/a> for both frontline service teams and customers. Frontline workers will save time in the field with automated reports. AI-generated guides will help new employees and contractors<\/a> to onboard quickly and brush up on their skills with ongoing learning resources. Customers will be able to troubleshoot common issues on their own with knowledge-based articles<\/a>.<\/p>\n\n\n\n The current wave of generative models is very powerful, but in a small number of cases, they can generate biased and even harmful outputs, as well as made-up facts (called \u201challucinations\u201d). This is why keeping a human reviewer in the loop, whether it\u2019s a service agent or knowledge expert, will be important for the foreseeable future. Given the extensive opportunities and challenges related to generative AI, Salesforce recently published the five guidelines for trusted generative AI development and explained the potential for generative AI in enterprise tech and how to balance this transformative tech with reality and risks.<\/p>\n\n\n\n Layering generative AI on top of Einstein<\/a> capabilities will automate the creation of smarter, more personalised chatbot<\/a> responses that can deeply understand, anticipate, and respond to customer issues. This will power better-informed answers to nuanced customer queries, helping to increase first-time resolution rates. With generative AI tapping into customer resolution data to analyse conversation sentiment and patterns, service organisations will be able to drive continuous improvement, identify trends, and accelerate bot training and updates. <\/p>\n\n\n\n Over time, we plan to use generative AI to draft knowledge articles not only based on case notes but also Slack conversations, messaging history, and data across Einstein 1 to accelerate agent case resolution and shift even more support cases to self-service experiences. This will alleviate pressure on call centres and agents.<\/p>\n\n\n\nSuper-powered chatbots <\/span> <\/h2>\n\n\n\n
Auto-generate knowledge articles <\/span> <\/h2>\n\n\n\n
Fast-track case swarming <\/span> <\/h2>\n\n\n\n
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