Senbote: Key to Enterprise AI Agents is Empirical Business Loop

At WAIC 2026, Senbote chairman Yu Linyi noted that enterprise AI has shifted from 'tech finding scenarios' to 'scenarios refining tech.' Citing a Harvard Business Review survey that 85% of agent projects fail because AI never enters real business processes, Senbote's counter is the BER system: every agent must first run in Senbote's own brand Keyu's real business, producing quantifiable ROI, before client delivery. For example, the GEO agent "Haoxian" boosted recommendation rate from 0 to over 90% in Keyu, then achieved 96% new product recommendation rate at Haier. The KOL marketing agent "Haoling" improved AI-selected KOL spillover rate by 13% over humans. Yu stressed that Senbote's core strength is not model size but 20 years of industry methodologies serving Midea, Haier, etc., such as multi-source mapping, ACCS, P-C-R for GEO, and twin-tower matching, CVI-5D for KOL marketing. These are distilled into algorithms and skills embedded in the CeMeta AI engine. For companies deploying enterprise agents, Senbote recommends choosing scenarios with high digitization, high budget, and clear criteria, then iterating agents with empirical results.