Contact Center: Auto CSAT - AI Transparency Technical Note

Publisher

Cisco Systems, Inc. (Cisco Trust Portal)

URL

https://trustportal.cisco.com/c/dam/r/ctp/docs/white-paper/collaboration/ai-transparency-auto-csat-technical-note.pdf

Published

2025-01-22

Updated

unknown

Accessed

2026-09-05

HTTP status

200

License

Cisco proprietary documentation, all rights reserved; no-redistribution; short excerpts and locators only

Claims citing this source

• The Auto CSAT technical note states that the model's output should not be used in isolation for measuring agent performance penalizing or compensating agents or informing decisions on an agent's employment and right to work. fact in context
• Of the 9 Cisco contact centre AI transparency notes read in this tranche 2 carry an employment decision restriction and 7 are silent. inference in context
• No Cisco source read in this tranche maps any contact centre scoring feature onto any legal classification and the corpus therefore cannot say whether any feature is a high risk system under any statute. fact in context
• None of the 9 Cisco contact centre AI transparency notes tells an administrator how to comply with any named law and the phrase AI Act appears in none of them. fact in context
• The Model Architecture sections of the Agent Burnout Detection and Auto CSAT notes contain a figure and no descriptive text. inference in context
• The Trust Portal record for the Auto CSAT transparency note carries a document expiry date of 22 January 2026, which had already passed at the access date of 5 September 2026, while the record remained marked Active and latest version. fact in context
• The Auto CSAT model is evaluated every 30 days, is retrained on the last 180 days of data if accuracy falls below an acceptable threshold, and is separately retrained every 30 days to account for data drift and bias. fact in context
• The Auto CSAT model is a Cisco native deep learning model that predicts a customer satisfaction score for every customer interaction; the note names no third-party vendor. fact in context
• Cisco states that Auto CSAT output should not be used in isolation to measure agent performance, penalise or compensate agents, or inform employment decisions. fact in context
• The Auto CSAT output scale is defined by the customer; the note gives a 1-to-five scale as an example rather than as the supported scale. fact in context
• The Auto CSAT note announces that an acceptable accuracy threshold triggers retraining and never publishes its value. inference in context
• The PDF document properties of the Call Summaries Agent Burnout Auto CSAT and Topic Analysis notes carry a subject of Webex Messaging Security and keywords naming a Webex Messaging security technical paper, none of which matches the documents' actual subject. fact in context

Cite this source record

APA

WarmTransfer. (2025, January 22). Contact Center: Auto CSAT - AI Transparency Technical Note. WarmTransfer. https://warmtransfer.net/knowledge/sources/cisco-trustportal-tn-wxcc-auto-csat

BibTeX

@misc{warmtransfer-cisco-trustportal-tn-wxcc-auto-csat,
  title  = {Contact Center: Auto CSAT - AI Transparency Technical Note},
  author = {{WarmTransfer}},
  year   = {2025},
  url    = {https://warmtransfer.net/knowledge/sources/cisco-trustportal-tn-wxcc-auto-csat},
  note   = {Cisco Systems, Inc. (Cisco Trust Portal), accessed 2026-09-05}
}