Why Every Enterprise Needs a Knowledge‑Management System in the LLM Era

Concept and Planning

In the era of digital transformation, "digital" means both data and algorithm. Data can be turned into information, while algorithms turn information into decisions. Recent policies treat data as an asset, but algorithms alone cannot model complex business scenarios. Large language models such as DeepSeek have moved from simple Q&A to reasoning, allowing organizations to bypass some algorithmic steps and obtain non‑quantitative insights, dramatically boosting productivity.

The author recounts building an automatic ordering system for a retail chain around 2001, which combined inventory, sales forecasts, and store constraints to generate a suggested order list. This system raised a tea‑shop’s profit by about 1 % and demonstrated how a single knowledge‑driven module can lift business performance.

Extending this idea, the author argues that every business module contains “knowledge” that should be captured, managed, and reused. Tencent’s personal knowledge‑management tool ima can collect information in WeChat and apply LLMs to “knowledge‑ify” it, but an enterprise‑grade solution must provide a structured system that separates personal, public, and corporate knowledge while allowing seamless merging and isolation during employee onboarding and off‑boarding.

Personal knowledge is divided into several categories: professional expertise, business‑related knowledge, life knowledge, private knowledge, and passive knowledge received from external analysis. Group knowledge can be shared across teams, and corporate knowledge bases can store standard operating procedures, policies, and other shared assets.

Summary

Using the personal tool ima together with Tencent Cloud’s Large‑Model Knowledge Engine (LKE), a “knowledge ecosystem” can be built. This ecosystem unifies personal and corporate knowledge, enabling employees to retrieve precise answers via AI‑driven Q&A, continuously improve the knowledge base through usage analytics, and turn fragmented data into long‑term strategic insight.

Afterword

The author introduces the concept of “time friends” (knowledge) versus “time enemies” (raw data). Knowledge gains value over time, while data depreciates. A dialogue with DeepSeek is presented to illustrate this distinction:

“We say that something that becomes more valuable over time is a friend of time, while something that loses value is an enemy of time. How should we treat each?”

DeepSeek’s answer (slightly edited) outlines four analytical dimensions and corresponding principles:

Distinguishing dimensions

Anti‑decay: knowledge creates a positive feedback loop; data follows entropy.

Marginal cost: knowledge’s marginal cost declines, data’s maintenance cost rises.

Network effect: knowledge connects people; data remains isolated.

Iterability: knowledge self‑updates; data lacks evolution.

Principles for time friends

Compound‑interest investment: allocate a fixed % of time each year to deep learning.

Structural reinforcement: systematize scattered notes into a knowledge graph.

Ecological niche building: combine skills, industry insight, and network.

Strategies for time enemies

Value‑discount framework: evaluate items with V(t)=V₀·e^{‑kt}.

Dynamic zero‑out system: FIFO + decay algorithm.

Conversion channel: turn short‑term data into long‑term models.

Opportunity‑cost monitoring: discard when maintenance > reset cost.

The author proposes a value‑evolution coordinate system (time on the X‑axis, value density on the Y‑axis). Positive slope items receive exponential investment; negative slope items trigger logarithmic exit. Practical examples compare whisky aging (time friend) with fresh‑food logistics (time enemy), illustrating how to maintain, invest, or discard assets.

Key take‑aways:

Invest continuously in knowledge like a savings account.

Set stop‑loss thresholds for data that become liabilities.

Beware of “false friends” (useless collections) and “false enemies” (skills that actually add long‑term value).