Company Context

HelpDeskPro is a B2B SaaS customer support platform used by 12,000 mid-market companies. It earns revenue through seat-based subscriptions and premium automation add-ons, and competes with Zendesk and Intercom by positioning itself as easier to deploy for lean support teams.

Problem

The company has built a new AI feature that drafts suggested replies for support agents based on past tickets and help center content. Engineering believes the model quality is strong, but adoption in beta is inconsistent: only 28% of eligible agents use the suggestions weekly, and several design partners say the feature feels "impressive but not essential." Leadership wants to know whether this technical solution creates real value for end users, the business, or both.

You are the PM responsible for deciding whether to invest further in this feature, reposition it, or narrow the scope. Assume the current beta serves 150 customer accounts, average support handle time is 11 minutes per ticket, and enterprise customers are asking for proof of ROI before upgrading to the paid AI tier.

Deliverables

1. Define the primary user problem this AI reply assistant should solve and which user segment to prioritize first.
2. Explain how you would determine whether the solution drives value for the end user, the business, or both.
3. Recommend the MVP feature scope and what you would deprioritize for the first commercial launch.
4. Define success metrics and an experiment or validation plan.
5. Identify the key trade-offs and risks in bringing this feature to market.

Constraints

• MVP must launch within 10 weeks.
• Only 1 ML engineer, 2 backend engineers, and 1 designer are available.
• The model cannot increase agent workflow latency by more than 1 second.
• Responses must remain auditable for regulated customers.
• The team can only instrument lightweight analytics before launch; no major data platform rebuild.