Company Context

You’re the PM for HelpDeskPro, a B2B customer support platform used by 12,000 companies (primarily SaaS and e-commerce) to manage email + chat support. The product has 4.5M end-customer conversations/day flowing through it. HelpDeskPro charges per agent seat and has an enterprise tier with SLAs and compliance features.

HelpDeskPro competes with Zendesk and Intercom. Over the last year, both competitors have heavily marketed “AI-first support,” and several of your largest customers are asking what your AI roadmap is.

Your leadership team wants to launch AI Smart Replies: suggested draft responses that agents can insert/edit while replying to tickets.

User / Market Scenario

HelpDeskPro’s core users are support agents and team leads. Your user research (30 interviews across 10 customers) identified three common personas:

You also have quantitative signals:

• Median first response time (FRT): 18 minutes (enterprise target is <15)
• Median handle time (AHT): 9.2 minutes per ticket
• Macro usage: 62% of tickets use at least one macro
• Reopen rate: 8.5% (tickets reopened within 7 days)
• CSAT: 4.42/5 average; enterprise customers are sensitive to drops

The Proposed Feature

AI Smart Replies would appear in the ticket composer as 3 suggested drafts. Agents can:

• Insert a suggestion with one click
• Edit before sending
• Provide feedback (“helpful / not helpful”)

The initial model is an LLM-based system fine-tuned on anonymized historical tickets. Engineering estimates 8–10 weeks to ship an MVP.

Why This Is Hard (Workflow Risk)

Leadership assumes Smart Replies will reduce AHT. But your design partner customers raised concerns:

• Agents already rely on macros; Smart Replies could conflict with existing macro governance.
• Some teams have strict brand voice and compliance disclaimers.
• Specialists worry about hallucinations causing incorrect troubleshooting steps.
• Team leads want to understand how Smart Replies affect coaching, QA sampling, and accountability.

In other words, the feature could speed up replies—or it could disrupt established workflows, increase reopen rate, and create compliance risk.

Your Task (Deliverables)

Walk the interviewer through how you would assess the impact of AI Smart Replies on existing workflows and decide whether/how to launch.

1. Map the current workflows (agent + lead) and identify where Smart Replies would intervene. Use a Jobs-to-be-Done lens.
2. Define a set of workflow impact hypotheses (positive and negative). What would you expect to change, and why?
3. Propose an evaluation plan: research + instrumentation + experiment design to measure workflow impact before and after launch.
4. Make a scope and rollout recommendation (MVP + phased expansion). Include what you would not ship initially.
5. Identify risks and mitigations (quality, compliance, brand voice, agent trust), including a rollback plan.

Constraints

• Timeline: MVP must be ready for a customer conference in 10 weeks.
• Resourcing: 6 engineers total across the org are shared; you realistically get 3 engineers + 1 designer + 1 data analyst.
• Compliance: Some customers are in regulated industries (fintech/health). Smart Replies must support audit logs and allow customers to disable AI per workspace.
• Latency: The composer must stay responsive; Smart Reply generation must be <800ms p95 or show a non-blocking fallback.
• Data policy: You can train on historical tickets only if they’re anonymized; some enterprise customers require no data retention.

What the interviewer is looking for is not “AI ideas,” but a rigorous approach to understanding and measuring workflow impact and making trade-offs under constraints.