Your company receives 50,000 support tickets per month and management wants an AI customer support bot. What questions would you ask first, how would you assess feasibility, what stakeholders would you involve, what KPIs would define success, and what risks would you identify?
I would begin by understanding the business problem, current support processes, ticket types, and expected outcomes. I would then assess whether sufficient data, technology, and operational readiness exist to support an AI solution while defining measurable success metrics and identifying key risks and stakeholders.
Full Answer
This scenario tests a Business Analyst's ability to perform discovery, stakeholder analysis, feasibility assessment, and solution evaluation. Before discussing AI, it is important to understand why management wants a chatbot and what business problem they are trying to solve. The goal may be reducing support costs, improving response times, increasing customer satisfaction, or enabling 24/7 support.
The first step is requirements elicitation. Key questions include the volume and categories of support tickets, common customer issues, current resolution rates, existing support channels, escalation processes, and customer satisfaction levels. Understanding whether repetitive inquiries make up a significant portion of tickets helps determine whether AI automation is a viable solution.
Feasibility assessment should focus on data availability, technology integration requirements, operational readiness, and expected business value. Historical support conversations, knowledge bases, FAQs, and ticket-resolution data are essential for training and validating AI solutions. The analyst should also evaluate integration requirements with CRM, ticketing, and customer support platforms.
Relevant stakeholders include customer support leadership, support agents, IT teams, AI or data science specialists, product owners, compliance teams, and customer representatives. Each stakeholder provides unique insights into operational needs, technical constraints, governance requirements, and customer expectations.
Success should be measured through clear KPIs such as ticket deflection rate, average response time, first-contact resolution rate, customer satisfaction, cost reduction, and chatbot containment rate. Risks include inaccurate responses, poor customer experience, privacy concerns, integration complexity, lack of quality training data, and resistance from support teams who may fear job displacement.
Sample Answer
I would start by understanding the business objectives behind the AI chatbot initiative. I would ask what problems management is trying to solve, such as reducing support costs, improving customer experience, or handling growing ticket volumes. Next, I would analyze current support operations, including ticket categories, escalation rates, response times, resolution metrics, and customer satisfaction scores. I would also assess whether sufficient historical ticket data, knowledge articles, and FAQs are available to support an AI solution. For stakeholders, I would involve customer support managers, support agents, IT teams, product owners, AI specialists, compliance teams, and customer representatives. Their input would help identify requirements, constraints, and success criteria. Key KPIs would include ticket deflection rate, average handling time, first-contact resolution, customer satisfaction scores, chatbot containment rate, and operational cost savings. Major risks would include poor AI accuracy, biased or incomplete training data, integration challenges, privacy concerns, customer frustration due to incorrect responses, and organizational resistance to change. I would document these risks and develop mitigation plans before recommending implementation.
How This Applies by Industry
In a SaaS company, the chatbot may handle password resets, subscription questions, onboarding guidance, and common product troubleshooting requests, reducing support workload significantly.
In ecommerce, the chatbot can automate order tracking, returns, refunds, and shipping inquiries while escalating complex issues to human agents.
In fintech environments, AI support solutions must address strict compliance and security requirements while assisting customers with account-related inquiries.
Frequently Asked Questions
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