Core Insight: The real problem isn't the 'last mile,' but the disconnect in knowledge transfer. The most valuable knowledge in companies remains trapped in the minds of a few people. AI Job Standards Hub's solution is—not to make 'all knowledge' explicit, but to first standardize 'job behaviors.'
A Miscalculated 'Last Mile'
The tech world likes to use 'last mile' to describe the gap between technology and application. But for enterprise AI deployment, this metaphor has actually misled the industry—the real problem isn't the 'last mile,' but the disconnect in knowledge transfer.
Typical Scenario
A SaaS company used AI to assist product managers in writing PRDs. After deployment, they found:
- ✕PRD format inconsistent with company template
- ✕Terminology usage habits differ
- ✕Judgment criteria deviate from company values
- ✕Basic concept understanding varies
The root cause is—that this company spent a decade building its 'product methodology,' but it was never made explicit, standardized, or reusable. The most valuable knowledge still resides in the minds of a few and in Slack chat logs.
Knowledge Management's Ancient Problem, AI Era's New Solution
Knowledge management is an ancient problem in management. Thirty years later, most companies' knowledge management remains at two extremes—Wiki docs nobody reads, or private notes only the author can understand.
AI Job Standards Hub's New Approach
Not to make 'all knowledge' explicit, but to first standardize 'job behaviors.'
The brilliance of this approach: reducing the granularity of knowledge management from 'knowledge itself' to 'behavioral norms.' Rather than asking AI to 'learn the product manager's way of thinking,' tell it 'what is the standard process for a product manager to do this.'
Deconstructing the Design Philosophy Behind 'Role Standard Package'
AI Job Standards Hub's core product is 4 files: Role Definition, Operating Rules, Workflow, Output Standards. On the surface they're technical documents, but behind them lies a complete design philosophy:
Role Definition = Capability Boundary
Establishes responsibility boundaries in human-AI collaboration. Let AI know it's a product manager, so it won't overstep to make technical decisions.
Operating Rules = Risk Control
AI inherits red-line experience from day one on the job, instead of being corrected only after each mistake.
Workflow = Explicit Methodology
Translate implicit methodology into AI-executable operation instructions, solidified into actionable guidelines.
Output Standards = Quality Baseline
Answer 'what does good look like,' let AI self-evaluate after generation, instead of dumping half-finished work on humans.
Why Is This Only Possible Now?
Technical Prerequisite
After GPT-4, AI can understand role definitions, execute complex tasks, and maintain behavioral consistency
Ecosystem Prerequisite
Notion/Feishu/Jira proliferation, 'process explicitization' already has cognitive foundation
Market Prerequisite
AI anxiety spreading, business owners see potential but don't know how to deploy
Business Logic: Standardization is the prerequisite for scaling. AI Job Standards Hub reduces the cost of 'making AI professional' from custom development to standardized download.
What Scenarios Are Suitable, What Aren't
checkSuitable Scenarios
- • Highly standardized work (PRD, code standards, operation plans)
- • Knowledge-intensive but low creativity demand
- • Scenarios requiring scaled replication
closeUnsuitable Scenarios
- • Vertical domains highly dependent on industry know-how (medical, legal, finance)
- • Work requiring deep customer relationship maintenance
- • Fields requiring continuous innovation
In Conclusion
It's not about letting AI replace humans, but letting humans and AI each play their greatest value in a clearer division of labor system.
Because before AI truly changes the business world, it needs to learn one thing first: knowing what position it stands on. Knowing what position it stands on.