Why Enterprise Large‑Model Digitalization Is So Hard: Key Challenges and Capabilities

1. Why digital‑intelligence projects?

According to the “AGI Road” view, the current wave of large‑model AGI is the path that every enterprise’s digital‑intelligence transformation must follow. Under this path the long‑term goal must be clarified.

2. Required human capabilities

Ultimate thinking : OpenAI’s perspective suggests that each business line can eventually be handled by one person plus an AGI. The remaining person must possess four basic abilities and, at a higher level, three superior abilities.

Basic abilities

Business understanding : ability to describe business differences and guide AGI to achieve business goals.

Value judgment : ability to assess the value of business outcomes and feed that back into model upgrades.

AGI usage ability (tool proficiency):

Understand AGI’s capability boundaries, near‑term changes, and long‑term expectations.

Use various AGI tools to accomplish business objectives.

Risk‑identification ability :

Strong awareness of compliance, data, and incident risks.

Build effective risk‑monitoring systems with AGI.

Learning‑transfer ability :

Quickly master new AGI tools and understand core data for rapid migration.

Proactively learn market changes and explore AGI tools for efficiency gains.

Superior abilities (mid‑ to long‑term):

Business innovation : uncover real customer needs and market competition.

Systemic thinking : goal → data → model → application → feedback → optimization loop.

Influence : drive industry change according to innovative plans.

The most important thing in large‑model digital‑intelligence projects is to cultivate the above talent.

3. Who is transformed by digital‑intelligence?

Historical analogies:

Steam era → replaced small‑workshop manual labor.

Electricity era → replaced assembly‑line junior blue‑collar repetitive mechanical work.

Internet era → replaced flow‑line skilled workers with repetitive but relatively complex processes.

Mobile‑Internet era → replaced low‑level white‑collar tasks such as simple spreadsheets and basic customer service.

AGI era → replaces senior white‑collar “wheel‑reinventing” work like repetitive modeling, coding, and report writing.

Typical jobs that may be displaced include junior designers, junior programmers, and junior lawyers.

4. Current mainstream technologies

RAG, CoT, Agent, DeepResearch remain hot topics for large‑model construction.

5. Enterprise large‑model upgrade path

The recommended three‑step roadmap:

Knowledge Q&A

Process embedding

Decision support

First layer – Knowledge Q&A (foundation):

- Function: intelligent Q&A, knowledge retrieval
- Technology: basic RAG, semantic search
- Data: structured knowledge base
- Organization: data governance, prompt engineering

Second layer – Process embedding (automation & intelligent collaboration):

- Function: business process automation, optimization
- Technology: Agentic RAG, AI Agent, CoT
- Data: dynamic business data, multimodal
- Organization: process re‑engineering, cross‑functional teams

Third layer – Decision support (strategic empowerment):

- Function: strategic decision support, comprehensive analysis
- Technology: Deep Research, IRCoT, multi‑agent
- Data: cross‑source integration, multi‑hop search
- Organization: strategic culture, ethical oversight

The core logic of the three‑step approach:

Technical capability progression : from simple retrieval to complex reasoning, each layer builds on the previous one.

Data quality requirements : data volume and quality grow exponentially from basic Q&A to complex business data fusion.

Organizational trust : early success cases are needed to gain trust before relying on AI for strategic decisions.

6. Hot sub‑topics and capability boundaries

RAG‑related techniques – retrieval optimization, context filtering, decoding control, efficiency improvement.

Agent‑related techniques – autonomous task execution, multi‑agent collaboration, human‑machine interaction, situational awareness.

CoT (Chain‑of‑Thought) techniques – reasoning path optimization, multi‑step reasoning, explainability, self‑supervised learning.

DeepResearch techniques – literature mining, trend analysis, data integration, knowledge‑graph construction.

Multimodal techniques – cross‑modal learning, modality fusion, multimodal generation, modality alignment.

Capability limits of single technologies (as observed):

RAG: excellent retrieval accuracy but struggles with reasoning and stable context understanding.

Agent: strong task decomposition and iterative interaction, yet abnormal case handling and tool invocation are unstable.

CoT: clear step analysis, but innovative reasoning and logical stability are limited.

DeepResearch: good summarization, but breakthrough discovery and hypothesis generation are unstable.

Multimodal: strong visual understanding, but complex scene comprehension and cross‑modal association are unstable.

Recommended stable combinations: RAG+Agent, CoT+DeepResearch, Multimodal+Agent. Unstable or difficult combos: RAG+CoT, Agent+DeepResearch, Multimodal+CoT.

7. Typical error rates

Illustrated error‑rate charts (omitted here) show OCR error around 8 % for legal contracts and similar figures for other modalities.

8. Enterprise large‑model challenges

Typical cases :

Business expects a multimodal Agent to handle all documents, but OCR error (~8 %) limits performance.

A bank plans an RAG‑based credit‑review system, then must redesign when multimodal capabilities evolve.

Intelligent客服 improves satisfaction but does not directly increase revenue.

A factory’s quality‑inspection Agent needs new labeled data for added defect types, causing a 50 % cost overrun.

A bank’s fast‑track RAG credit system later discovers Agent‑driven dynamic risk control, leading to high rework cost.

A retailer spends a million‑level budget yet still requires human handling for complex complaints, achieving only 20 % of expected ROI.

Difficulty obtaining business buy‑in : digital‑intelligence is positioned as talent‑level upgrading rather than job replacement; a layered talent model (knowledge operator → process optimizer → strategic decision‑maker) is proposed to energize the organization.

Difficulty reaching technical boundary consensus :

Different stakeholder expectations (executives want disruptive ROI, front‑line staff fear replacement, IT seeks controllable innovation).

Information asymmetry: mis‑judgment of RAG, Agent, CoT capabilities, cross‑departmental perception gaps, rapid 2025 technology iteration.

Difficulty evaluating ROI :

Soft benefits (efficiency, user experience, innovation, risk reduction) are hard to quantify.

Complex cost structure: compute, data preparation, system integration, training, management, risk‑control.

Key success factors (four pillars):

High‑level strategic consensus and sustained support.

Clear value‑driven goals and AI‑business hybrid talent.

Forward‑looking technical architecture and AI literacy.

Incremental implementation path with flexible investment decisions.