Product managers often implement AI features like chatbots simply to follow trends without validating user needs. For instance, one online school launched an AI career consultant, but students preferred a simple checklist of market-relevant skills, which was also cheaper to maintain.
Poor data quality leads to unreliable AI performance. One platform connected an AI assistant to an outdated knowledge base, causing the bot to recommend obsolete information. Effective implementation requires structuring data into machine-readable formats and ensuring content is verified and up-to-date.
Measuring success solely by interaction volume is a mistake. In one case, an AI assistant failed to reduce the workload for human tutors because students did not trust the AI's answers. Success should be tied to business metrics, such as reduced task-processing time or improved course completion rates.
Automating critical processes without human oversight can cause user frustration. One system automatically failed students on essays using rigid algorithms without an appeal process. Managers must provide escalation paths to human staff for complex scenarios or disputes.
Ignoring operational costs, such as token usage and infrastructure, can threaten project budgets. One school had to replace an AI-based testing tool with a classic version after users repeatedly retook tests to improve scores, causing costs to exceed the marketing budget.
Security and user experience are essential. Managers must prevent the leakage of sensitive data when using external LLMs and ensure AI is integrated seamlessly into the user journey rather than isolated in separate tabs. Continuous feedback loops are necessary to prevent feature degradation.
