Strategy Product Definition, AI‑Era Trends, and Career Path in Recommendation Systems

Introduction – The session focuses on recommendation strategies in the AI era, the definition and capability model of strategy products, and the growth path for strategy product professionals.

1. Definition and Capability Model of Strategy Products

Strategy product is a role that, within constraints such as legal regulations, user experience guidelines, and resource limits, drives projects, establishes evaluation systems, and comprehensively assesses project benefits to achieve a global optimum.

Constraints: legal limits, user experience design, resource limits.

Project driving: project lubrication, PRD clarification, key decision‑maker involvement.

Evaluation system: primary and secondary metrics.

Comprehensive benefit assessment: subjective experience measurement and data measurement.

Global optimum: measured by the proportion of top performance across parallel scenarios.

2. Three Generations of Product Managers

Product managers evolve from classical product (pre‑2010) focusing on user empathy, product architecture, and project management, to strategy product (2014‑2023) requiring data analysis and model understanding, and finally to AI product (post‑2023) that is AI‑native.

All PMs need four capability modules: user empathy & product architecture, data analysis, model understanding, and project management. The first is universal; the latter three grow in importance for strategy and AI products.

3. Simplified Talent Development (TD) Framework

The TD diagram shows a four‑dimension rating (0‑3) for each capability; the total score is the sum of the four dimensions. Minimum scores define role levels, e.g., a level‑4 PM must achieve at least 4 points overall.

4. Practical Workflow

Projects consist of three layers: logic, implementation, and interaction. Both strategy and AI products start by clarifying OKR and KA. Strategy products focus on funnel model optimization; AI products focus on data production and alignment.

5. AI‑Era Strategy Product Trends – Six Key Questions

Q1: Search‑recommendation strategy products will not be replaced by AI PMs; they solve connection problems while AI solves computation.

Q2: Current checkbox mode is a “Dos” era; future breakthroughs depend on hardware upgrades, big‑bang interaction, and wearable devices.

Q3: PMs remain essential for business logic, data flywheel, and demand matching; AI assistants will augment but not fully replace them.

Q4: LLM progress outpaces product evolution; entering top‑tier companies is crucial for learning core technology.

Q5: Service recommendation vs. content recommendation differ in spatio‑temporal exclusivity, tolerance, environment, and fairness; AIGS (AI‑Generated Service) provides context‑aware, low‑risk services.

Q6: Large models need improvements in context length, reasoning depth, and instruction compliance; training involves next‑token prediction, chain‑of‑thought, and reinforcement learning.

6. 2024 Observations and Reflections

Click‑through duration is driven by quality, interest, and scenario factors; audio content relies heavily on scenario. Longer single‑click duration indicates higher decision cost. Text and image optimizations (e.g., using verbs) improve ROI.

Feature engineering now requires “coarse‑grain” features for powerful models, while strategy product rules still need “fine‑grain” features.

Future teams must become learning‑oriented organizations; individuals should strengthen empathy, product architecture, data analysis, causal inference, and model understanding to stay relevant.

Overall, the session provides a comprehensive view of strategy product roles, their evolution, skill requirements, and future directions in the AI‑driven recommendation landscape.