WAIC 2025 Recap: AI Agents Take Center Stage

The 2025 World Artificial Intelligence Conference (WAIC), themed “Intelligent Era, Shared Future”, brought together over 800 companies and 3,000+ cutting-edge innovations. One of the biggest highlights? A rare public keynote in China by Geoffrey Hinton — Turing and Nobel laureate — who explored the provocative question: “Will digital intelligence replace biological intelligence?”

But the real spotlight this year was on AI Agents. Beyond the buzz, they represent a paradigm shift — from using AI as a tool to integrating it as a core operator in enterprise workflows.

This session breaks down:

• What “AI-native thinking” really means
• The core logic of AI Agents
• How to evaluate and select the right AI Agent solution
• Real-world examples that show the path forward

I. From Digital 1.0 to AI-Native: A New Cognitive Framework for Business

Past: Digitization 1.0 — Humans Adapt to Systems

In the early digital era, humans were the glue between systems — manually logging into platforms, moving data, and pushing approvals. Think of a typical e-commerce operator juggling inventory, logistics, and finance portals daily.

Present: AI-Native — Systems Adapt to Goals

In the AI-native era, the AI Agent acts as the central brain. It understands goals (“launch a new product”), coordinates backend systems (SKU setup, inventory check, logistics alignment, budget approval), and only surfaces decisions that truly require human judgment.

Two Breakthroughs of AI-Native Thinking

1. Shift of Control: From AI as a tool to AI as the process orchestrator
   AI proactively breaks down objectives, allocates tasks, and drives execution. For example, a virtual assistant schedules meetings, books flights, and sends invites — all on its own.
2. Full-Chain Automation: Breaking data and workflow silos
   AI Agents bridge operations across departments — linking customer touchpoints, internal systems, and vendor pipelines for end-to-end automation. A customer order might automatically trigger inventory deduction, logistics scheduling, and post-sale follow-up.

Bottom Line: This unlocks smarter digitization, reduces operational load, and enables flexible, goal-driven workflows that adapt at speed.

II. Evolution of Human–AI Collaboration: From ChatGPT to Agents

There’s growing confusion around ChatGPT, Copilot, and AI Agents. But their differences matter — especially for businesses aiming to delegate work, not just generate content.

III. Enterprise-Grade AI Agents: Four Core Dimensions

To avoid hype traps and identify enterprise-ready AI Agents, tech leaders should evaluate solutions across four core dimensions: business logic depth, data security, deployment maturity, and risk governance.

1. Business logic penetration (Does it understand your domain?)
2. Data security & Compliance (Can you trust it with your core assets?)
3. Deployment readiness (Can you scale it without breaking things?)
4. Risk management (Will it break your systems or reputation?)

The full list of 12 evaluation criteria, key questions, and real-world examples for assessing enterprise AI agents is exclusively available to our paid clients.

Contact us to request access to the 12 AI Agent selection criteria.

IV. Real-World Case: AI Agents in Healthcare

Use Case 1: Clinical Diagnosis Support

Old Way: Radiologists manually review scans, taking 30+ minutes per report.

AI Agent Way:

• Instantly fetches data from EMR, PACS, LIS.
• Uses AI imaging models to detect and flag anomalies.
• Summarizes and presents only key decisions for doctor sign-off.
  → Result: 80% time saved, doctors become strategic decision-makers.

Use Case 2: AI-Accelerated Research

Old Way: Trial-and-error over a decade.

AI Agent Way:

• Mines millions of case records to identify drug candidates.
• Simulates drug–gene interactions.
• Generates real-time research reports and next-step suggestions.
  → Result: 40% faster from bench to market.

V. Conclusion: Becoming an AI-Native Company in 2025

Step 1: Understand the value.
AI Agents reduce cost, boost agility, and free humans for strategic work.

Step 2: Start small.
Test high-impact scenarios like customer support or supply chain first.

Step 3: Scale intentionally.
Expand across functions, building a digitally autonomous architecture.

The age of AI Agents is here. They won’t just support your business — they’ll help redesign it from the ground up.

Are you ready to lead the transformation?