---
title: "Open Day Recap | Future Business Intelligence — B2B × Foundation Models & AI Co-Evolution"
author: "Unique Research"
sourcePublication: "Unique Research (FFCAP English library)"
originalPublishedAt: "2024-06-06T16:00:00+08:00"
canonical: "https://ffcap.cn/en/research/20240606a0625400"
source: "https://ffcap.cn/en/research/20240606a0625400"
language: "en"
cnSourceUrl: "https://view.inews.qq.com/a/20240606A0625400"
---

# Open Day Recap | Future Business Intelligence — B2B × Foundation Models & AI Co-Evolution

_Unique Research · Recap · June 6, 2024_

Unique Research distilled this Open Day on how B2B industries co-evolve with foundation models and AI — speaker highlights plus roundtable Q&A. Photos attach on ingest (11 images).

## Cheng Junjie — Founder & CEO, Chuansheng Marketing (传声营销)

**1.** Data is the core asset; every activity must orbit data generation, analysis, and application. In the AI era, **knowledge** is the new high ground — knowledge bases fuel intelligence — but the durable lever for productivity remains **workflow** innovation.

**2.** Users want workable solutions, not magic tools. Copy tools must rest on solid copy theory; first define great structure, then use AI.

**3\. PUJD model** for copy: _Pain Point_ → _Unique_ solution → _Joy Point_ → supporting _Data_ (e.g., 30% energy save; ¥30k investment → ¥3M savings). Structured input is distilled from excellent copy patterns. For intelligent CS, jumping straight to Agents can be imprecise — product description theory must come first. Evolution from **3W1H** (Why / What / How) to **3W2H** (+ Who / How Much) — case knowledge + pricing/commercial terms — plus unstructured non-product knowledge.

## Lu Yue — VP, Yida Tech (壹沓科技)

**1\. Four stages:** standardization → informatization (human→software) → digitization (software→human, e.g. RPA; ≥50 definitions of “数字化” in their survey) → digital-intelligence (software→software→Agent; Gartner hyperautomation: task → process → cognitive → hyperautomation with LLMs).

**2.** Almost every industry enters “AI+.” Narrow AI vs AGI (reason, plan, learn, communicate, perceive). Rebuild commercial logic.

**3\. Three work modes:** Embedding → Copilot (chef + prep robot) → Agent (instruct; agent executes).

**4.** Today’s UIs are for humans (freeze workflow → role UI). Tomorrow Agents may hit databases directly — no classic UI. Commerce entry may be a dialog or humanoid: search, compare, buy on preference.

## Liu Yuchen — Founder & CEO, Yiyan Tech (意言科技)

**1.** AI Agents decompose tasks and self-reflect — System-1 execution plus System-2 analysis; learn and adapt end-to-end like humans.

**2.** Agents chase goals with environment feedback and tools; LLMs unlock strategies that pre-LLM agents lacked.

**3.** Scale alone isn’t enough — add reasoning, reflection, and tool-calling loops.

**4.** Andrew Ng: Agent workflows are undervalued; GPT-3.5 + agent loop can beat bare GPT-4. Four patterns: Reflection, Tool Use, Planning, Multi-agent Collaboration.

**5\. Fast win — sales:** mine historical calls/notes for intent and visit progress; low-risk, wide coverage.

## Roundtable Q&A (abridged, full fidelity to CN)

**Q: How much do base LLM capabilities matter?**  
A: Raw LLMs hallucinate (e.g., “681 × 2526?”). Precise apps use function calling / tools (Python, etc.). Depth of tooling depends on business complexity — hundreds of nodes vs a simple search/companion bot.

**Q: Can AI help B2B marketing collect competitor intel?**  
A: Classic social listening — once costly (servers + heavy custom). LLMs now find, analyze relevance, and draft reports cheaper.

**Q: Who buys Agents in B2B? Where to land first?**  
A: Decision with the department that benefits — CX/marketing for service bots; sales VP for pre-sales bots; ops/production for training Agents. Whoever benefits pays.

**Q: Will GPT-6 replace Agents?**  
**Liu:** Transformer LLMs compress world knowledge — strong at summary, weak at true creativity/reasoning unless new paradigms appear; more params can look like reasoning without being it.  
**Lu:** Base-model progress alone won’t overturn verticals — Altman noted even GPT-5-class models struggle where private enterprise data matters; China focus on private vertical models + Agents.  
**Cheng:** Private knowledge and workflow sediment stay stable — irreplaceable IP regardless of model generations.

**Q: LLM value in CRM?**  
A: (1) Faster/cheaper existing workflows (reports that once cost ¥100–150k). (2) Agent-to-agent and workflow chaining across SaaS A/B/C — CRM notes → Excel → report → email — rebuilt as connected Agents.

**Q: Platform vs vertical? Agent vs RPA? NLP vs LLM coexistence?**  
A: RPA simulates mouse/keyboard; Agents driving RPA is a key path — complementary, not contradictory. Don’t treat LLMs as universal — big data mining and RPA still win some jobs. In CS, classic NLP intent/keywords still needed for latency. Fine-tuned small models can score/classify with less hallucination; mix by scenario.

_Photos / slides attach on ingest._

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Editorial English edition published on UniqueCapital: https://ffcap.cn/en/research/20240606a0625400
On-site reading page: https://ffcap.cn/en/research/20240606a0625400