How Softbank scaled an AI agent-powered sales model, saving 250k hours a year

SoftBank's sales organization had customer intelligence scattered across meeting minutes and memos that couldn't reliably feed the CRM, and sellers spent only about 20% of their time with customers versus internal coordination. SoftBank built a suite of Dataiku-powered AI agents: an insight-extraction agent structuring meeting data around sales metrics, a standardization agent unifying opportunity stages for forecasting, and research/chat agents generating account materials and answering deal-status questions -- a working prototype launched within one month and then scaled across regions. Results: 90% of sellers report higher-quality, more efficient data-driven selling, 80% of customer conversations now link directly to CRM opportunities, and sellers save roughly 20 hours a month each, projecting 250,000+ hours saved annually. SoftBank's Shintaroh Imano said the goal is to 'automatically capture every conversation, without requiring sales input as a trigger, and have an AI agent deliver the right outputs to the sales team at the right time.'

Dataiku is a low-code/no-code AI platform whose Prompt Studios and LLM Mesh let teams prototype and A/B-test candidate LLM approaches before committing to a production workflow.

This sits in the Design & Stress-Test stage of the AI use-case lifecycle: Named examples from the tools pharma teams use to scope, prototype and benchmark an AI use case before committing to build it — data-catalog, sandbox and evaluation platforms.