How to Turn Large‑Model AI into Real Business Value: Insights from SF Express

Amid the AI wave sweeping across industries, how can cutting‑edge technology truly embed in business scenarios and create quantifiable value? Lu Xinting, senior product operations manager for large‑model AI at SF Tech, draws on extensive experience at ByteDance and SF Express to reveal the deep logic of efficient large‑model product operation.

Identifying high‑value AI scenarios requires a systematic process centered on business value, technical feasibility, and implementation risk. It demands deep business understanding, awareness of AI capability limits, objective data and organizational readiness assessment, and careful prioritization and pilot validation to focus resources on the most rewarding opportunities.

Business volume: high‑frequency, essential needs (e.g., couriers asking hundreds of times daily whether a battery can be shipped) lead to rapid deployment of a Q&A robot that resolves over 97% of standard shipping queries.

Model accuracy: for international logistics HS‑code recognition, the AI model must exceed 90% accuracy before launch.

Demand matching: for cumbersome form‑based ordering, AI enables one‑sentence or image‑based ordering by quickly recognizing user intent.

Only when a scenario is stable, user‑base fixed, and feedback abundant can a large‑model product truly operate.

Core AIGC product operation framework consists of four key modules: product definition, model training, product launch, and product operation.

The closed‑loop is formed through “product design + data operation + organizational coordination”:

Product design transforms complex technology into simple or no‑action experiences, such as context‑aware dialogue summarization and voice/image input replacing text.

Data operation tracks usage frequency and scale horizontally, and node conversion vertically, enabling targeted model improvements based on feedback.

Organizational coordination aligns product and business teams weekly, using usage data to iteratively optimize models—like an engine, fuel line, and chassis working together.

Designing user incentives and feedback mechanisms involves three tactics:

Offer tangible benefits: demonstrate time saved (e.g., a courier saves 3 minutes per query, allowing more deliveries).

Lower barriers: enable simple interactions such as a single spoken command or image upload to check shipping eligibility.

Listen to complaints: continuously gather user feedback, conduct regular surveys, and iterate rapidly; the courier service robot now has over 500 k users and 6 M conversations because it truly helps earn money.

Product‑Market‑Fit (PMF) verification for large‑model products differs from traditional internet products by requiring model accuracy validation. While AI hype raises expectations, real PMF is achieved only when accuracy meets business thresholds. Unlike fixed answers in conventional products, AI‑generated responses can vary, so consistent accuracy and satisfaction are essential before scaling, as demonstrated by SF’s AI warehouse assistant pilot.

Overall, Lu Xinting’s insights emphasize that AI must serve concrete scenarios, with value anchored in business pain points, and that a systematic “three‑metric” evaluation, closed‑loop engine model, and incentive‑feedback strategy are key to turning AI from a showcase into a practical driver of industry upgrade.