Assembled brings agentic workforce management to Five9's Intelligent CX Platform for contact centers
Contact centers managing blended workforces of human agents, AI agents, and BPO partners could not rely on legacy WFM tools built for static, single-channel, non-AI operations, leading to overstaffing, understaffing, and inability to adapt when AI handles part of the load.
Legacy workforce management tools with static forecasting models and manual scheduling could not handle the complexity of modern multi-channel contact centers; MTM specifically experienced constant performance issues and slowness at scale.
DailyPay reduced scheduling time by 65%, improved SLA performance by 7%, and saved 9,600 agent hours per month with over $1M in annual productivity gains.
MTM reduced schedule generation time by 50% and shifted its WFM team from fighting system limitations to higher-value analytics work.
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Frequently asked questions
What did this team achieve with this AI workflow?
DailyPay reduced scheduling time by 65%, improved SLA performance by 7%, and saved 9,600 agent hours per month with over $1M in annual productivity gains.
What tools did this team use?
Assembled, Five9.
What results were reported?
scheduling time reduction (DailyPay): 65%; SLA performance improvement (DailyPay): 7%; annual productivity gains (DailyPay): over $1M; agent hours saved per month (DailyPay): 9,600 (source-reported, not independently verified).
What failed first in this deployment?
Legacy workforce management tools with static forecasting models and manual scheduling could not handle the complexity of modern multi-channel contact centers; MTM specifically experienced constant performance issues…
How is this call center ai AI workflow structured?
Multi-channel interactions arrive → AI demand forecasting → AI schedule generation → Real-time adherence monitoring → Cross-platform data sync → Coverage optimization output.