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Or they might want 20% of their training data from customer support and 25% from pre-sales. You have filtering, normalization, some sort of augmentation, and you have to annotate the data,” says Jelinkova. For example, they can take a thousand-page document, have it ingested by the model, and then ask the model questions about it.
Savarese says this approach allows for a comprehensive evaluation of the practical business utility of AI in a wide range of CRM use cases, including sales and service scenarios. We want to expand with more metrics, use cases, data, and more annotations.” He adds that this is only the first iteration of the benchmark.
Adoption of Copilot so far tends to be in what he refers to as pockets, which matches how McKinsey reports that most gen AI deployments are happening in specific departments: marketing and sales, service and support, and product development. To evaluate the tool, the team created shared guidelines for what a good response looks like.
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