How AI Is Redefining Recommendation Strategies and Product Management Careers

Overview

This talk focuses on recommendation strategies in the AI era, the definition and ability model of strategy products, and the career growth path for product managers.

1. Definition of Strategy Product and Ability Model

Strategy product is a role that seeks a global optimum within constraints by driving projects, establishing evaluation systems, and comprehensively assessing project benefits.

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

Project driving: project lubrication, PRD detail clarification, and influencing decision makers.

Evaluation system: primary and secondary metrics.

Comprehensive benefit assessment: subjective experience measurement and data measurement.

Global optimum: measured by information volume and time waste during project retrospection.

2. Three Generations of Product Managers

Product managers have evolved from classical product (pre‑2010) to strategy product (2014‑2023) and now AI‑native product (post‑2023). Core competencies are grouped into four modules: user empathy & product architecture, data analysis, model understanding, and project management.

All managers need empathy and architecture skills; data analysis is essential for strategy and AI products; model understanding differs—strategy products focus on recommendation/search models, while AI products require large‑language‑model knowledge. Project management revolves around OKR formulation.

3. Simplified TD for Strategy Product

The simplified TD matrix rates four capability dimensions on a 0‑3 scale, with total scores guiding promotion thresholds. It helps team members identify gaps and plan skill upgrades.

4. Actual Workflow

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

5. AI‑Era Strategy Product Trends (Six Questions)

Will search‑recommendation strategy product be replaced by AI product managers? No; search‑recommendation solves connection problems, while AI solves computation. They complement each other.

Difference between current AI chatbox mode and ideal future product? Current checkbox mode is limited; future breakthroughs depend on hardware upgrades, edge models, and wearables, leading to new roles such as creators and scientists.

Is the product manager role still necessary? What is the ultimate goal of recommendation strategies? PMs bridge business logic, data flywheel, and demand matching; future AI assistants will enhance personalized recommendation.

Should product managers switch to AI product management now? LLM progress outpaces product evolution; entering leading companies early provides first‑ticket advantage, but both old and new technologies will coexist for years.

Difference between service recommendation and content recommendation? What is AIGS? Service recommendation differs in spatio‑temporal exclusivity, tolerance, environment, and fairness. AIGS (AI‑Generated Service) aims to provide accurate, easy‑to‑use interactions.

What improvements are needed for large models and what are the current training insights? Focus on context length, reasoning depth, and instruction compliance; next‑token prediction drives knowledge acquisition; reinforcement learning and tool use enhance capabilities.

6. 2024 Observations and Reflections

Click‑through duration is driven by quality, interest, and scenario factors. Audio content relies heavily on scenario; verbs in button text increase user actions. Models now need “coarse‑grain” features, while strategy rules still require “fine‑grain” features.

Post‑2000‑born professionals should strengthen empathy, data analysis, causal inference, and model understanding to stay relevant. Continuous learning and building a “learning‑oriented organization” are essential for future competitiveness.

Thank you for listening.