From a PMI-CPMAI perspective, concerns about quality and customer satisfaction must be addressed first at theplanning level, not only reactively once the chatbot is live. For AI-enabled services such as a customer service chatbot, the project manager is expected to define aformal quality management approachthat covers: what “quality” means for this AI system (e.g., accuracy of responses, relevance, tone, response time), how it will be measured, and which controls and tests will be applied throughout the lifecycle.
Acomprehensive quality assurance (QA) plantypically includes: clearly defined quality criteria and success metrics, test strategies (unit tests, conversation flow tests, usability tests, bias checks), acceptance thresholds, evaluation datasets, user journey scenarios, procedures for handling low-confidence outputs, and mechanisms for ongoing monitoring once in production. PMI-CPMAI guidance on AI lifecycle management stresses that these elements must be designedbeforewide rollout so that risks to customer experience are proactively controlled rather than discovered ad hoc.
Actions like beta testing, setting up monitoring teams, or doing regular performance reviews are valuable, but they areindividual techniquesthat should exist inside an overarching QA framework. The best initial step that a project manager should take, given generalized concern about potential quality issues, is therefore todevelop a comprehensive quality assurance plan for the chatbot.
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