Paper Type
ERF
Abstract
Business-to-business (B2B) software-as-a-service (SaaS) customer experience is shaped by how support requests move across service tiers and how coordination unfolds once cases enter human workflows. This study examines a global B2B SaaS provider that replaced a rule-based chatbot at the entry tier (Level 0, L0) with an AI conversational agent at the same tier and analyzes changes in tickets handled by human agents at Level 1 and above (L1+). Using comparable seven-month pre- and post-periods, the analysis draws on service records for tickets that reached human agents, including routing indicators, interaction measures, and resolution outcomes. Early findings indicate coordinated shifts after the L0 replacement: a larger flow into L1+ work, improved resolution outcomes after an initial transition period, and a recomposition of L1+ work toward troubleshooting-oriented categories. These patterns motivate computationally intensive theorizing of pathway change in AI-mediated service systems.
Paper Number
1813
Recommended Citation
K R, Subisha; Kausikan, Revanth; Mathew, Saji K; and Gnewuch, Ulrich, "Replacing Rule-Based Bots with AI Agents: Customer Experience in B2B SaaS Support" (2026). AMCIS 2026 Proceedings. 18.
https://aisel.aisnet.org/amcis2026/sig_hci/sig_hci/18
Replacing Rule-Based Bots with AI Agents: Customer Experience in B2B SaaS Support
Business-to-business (B2B) software-as-a-service (SaaS) customer experience is shaped by how support requests move across service tiers and how coordination unfolds once cases enter human workflows. This study examines a global B2B SaaS provider that replaced a rule-based chatbot at the entry tier (Level 0, L0) with an AI conversational agent at the same tier and analyzes changes in tickets handled by human agents at Level 1 and above (L1+). Using comparable seven-month pre- and post-periods, the analysis draws on service records for tickets that reached human agents, including routing indicators, interaction measures, and resolution outcomes. Early findings indicate coordinated shifts after the L0 replacement: a larger flow into L1+ work, improved resolution outcomes after an initial transition period, and a recomposition of L1+ work toward troubleshooting-oriented categories. These patterns motivate computationally intensive theorizing of pathway change in AI-mediated service systems.
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