Enterprise AI Transformation in Practice: Breaking Through from Agent Demo to Production Deployment

From the Compute Race to the Agent Race: AI's New Inflection Point

Over the past two years, the AI industry's keywords have been compute power and large models — whoever had more parameters and stronger compute infrastructure would seize the advantage. But now, the industry's focus has quietly shifted to a new direction: Agentic AI.

Agentic AI is one of the most significant technology paradigm shifts in the AI industry since 2024. Unlike traditional generative AI (such as ChatGPT-style Q&A interactions), Agentic AI emphasizes the ability of AI systems to autonomously plan, invoke tools, perceive their environment, and execute multi-step tasks. Gartner listed Agentic AI as one of the most impactful technology trends for the next three years in 2024, predicting that by 2028, 33% of enterprise software will incorporate Agentic AI components. The core logic behind this shift is that pure model capability improvements (parameter scale, training data volume) have entered a phase of diminishing marginal returns, and only by combining model capabilities with external tools, data sources, and business process orchestration can real productivity value be unlocked.

However, behind the hype lies a harsh reality for enterprises. As Dify's Asia-Pacific General Manager, Lusha Chen, pointed out in an interview on Bilibili's Zeno's Internet World: agent development is like "watching gods battle" — impressive demos keep popping up one after another, but when it comes to actual production scenarios, they rarely deliver real value. Enterprises are stuck — the technology is sexy, but the implementation is painful.

This article draws on the core insights from that interview to dissect the real pain points of enterprise AI transformation and the path forward from workflows to intelligent agents.

The Essential Difference Between Agents and Chatbots: From "Can Talk" to "Can Work"

To understand the value of Agentic AI, we first need to clarify its fundamental difference from traditional AI applications.

Previous AI applications mostly took the form of Chatbots — you ask, they answer. Agents are different: they can actually do work for you, autonomously close loops, and make decisions independently, having evolved from conversational tools into genuine production tools.

However, Lusha Chen emphasizes that Agents are still largely operating at the "workflow" level. They target production scenarios with strong demand, established SOPs (Standard Operating Procedures), and repetitive patterns. SOPs are a management methodology where enterprises codify best practices into repeatable execution steps, widely used in manufacturing, retail, and financial services. In the context of AI agents, SOPs serve as the "decision skeleton" — defining what actions to trigger under what conditions, how to handle exceptions, and when humans should intervene. Workflow Orchestration is the technical means of digitizing SOPs, linking multiple AI call nodes, decision logic, and human approval steps through structures like DAGs (Directed Acyclic Graphs). Compared to letting large models reason freely, the advantage of structured workflows lies in determinism and auditability — non-negotiable hard requirements in scenarios like financial compliance and medical diagnostics.

For large enterprise clients like Procter & Gamble and Walmart, the three most critical workflow characteristics are:

• Controllable: Processes execute as expected
• Traceable: The process is auditable and trackable
• Human-AI coordination: Humans and AI can collaboratively intervene

Only by meeting these conditions can enterprises achieve over 99% stability. This is why production-grade agents can't rely on a single large model "winging it" — they must be deeply integrated with structured workflows.

Workflows Are the Hands and Feet; Data and SOPs Are the Brain

Lusha Chen offers a vivid analogy: what Agents deliver are the "hands and feet," but they need to mobilize the "brain" and thinking — they need to know what to do today and how to do it.

This "brain" is essentially the enterprise's data combined with its internal SOP knowledge. Only by invoking workflows can Agents know precisely what tasks to perform. In other words, without private enterprise data and business knowledge, even the most powerful model is a tree without roots.

What Enterprise Decision-Makers Care About Most: ROI and Security Compliance

Through extensive interactions with clients across the Asia-Pacific region, Lusha Chen has found that the concerns of C-level decision-makers are remarkably consistent, whether domestic or overseas.

The most critical question is: "What do I need to invest to get the results I want?" This is fundamentally an ROI (Return on Investment) question. Enterprises are extremely budget-sensitive, so Dify's approach is to let rapid PoCs (Proof of Concept) do the talking — using real customer deployment cases to demonstrate achievable efficiency gains to decision-makers.

PoC (Proof of Concept) is a critical decision-making phase in enterprise technology procurement, typically lasting 2-6 weeks, using the customer's real data and scenarios to validate the feasibility of a technical solution. In AI projects, PoCs are especially important because large model performance is highly dependent on specific data and scenarios — a model that excels on general benchmarks may perform far worse on enterprise private data. A successful PoC typically needs to quantify three dimensions: task completion rate (accuracy), execution time compared to human speed (efficiency), and Token consumption versus labor costs (economics). For C-level decision-makers, PoCs are the bridge that translates technical narratives into financial language.

Beyond ROI, the second major concern is security and compliance. Enterprise data is their lifeline, and whether it is "visible, transparent, and controllable" directly determines whether AI can be confidently introduced into production environments.

Dify's Approach: Building Enterprise AI Applications Like Lego Blocks

As an AI company founded in 2023, Dify's entry point is crystal clear: how to make large language models truly applicable in enterprises, especially by integrating with their private data.

To this end, Dify has built a large language model application platform that supports both private deployment and cloud deployment. It can place large models at different workflow nodes, enabling models to understand enterprise data and ultimately facilitate data invocation across scenarios like FAQ and chatbot applications, helping enterprises build their own AI applications.

Lusha Chen positions Dify as a neutral and flexible workflow platform. It features an open Marketplace where numerous contributors independently publish plugins that users can directly invoke within workflow nodes. This open ecosystem is a key differentiator from closed solutions.

Gateway Plugins: Solving the "Last Mile" of Overseas Model Access

The interview mentioned that Sanlea AI Gateway joined Dify's Marketplace platform as a popular plugin. It primarily solves network issues when enterprises go global — such as DNS resolution, network jitter, and access stability for overseas model platforms.

AI Gateway is a middleware layer between the enterprise application layer and large model APIs, similar to the role of an API Gateway in traditional microservice architectures but optimized specifically for LLM invocation scenarios. Its core functions include: request routing (distributing calls to different model providers), load balancing (handling rate limiting and failures from individual providers), Token metering (precisely tracking costs per invocation), cache acceleration (reusing responses for similar requests to reduce latency and costs), and security auditing (logging all request-response pairs for compliance checks). For enterprises going global, AI Gateways also need to address special challenges of cross-border networking, such as DNS pollution, TCP connection establishment delays, and availability differences among model service providers across regions.

For production-grade applications, network security and stability are paramount. Through gateways, enterprises can:

• Gain visibility into Token traffic, enabling more precise ROI tracking
• Flexibly integrate with various models, avoiding vendor lock-in
• Leverage underlying acceleration capabilities for faster and smoother invocation experiences

This combination of "application platform + underlying network services" is essentially lowering the technical barriers for enterprise AI deployment.

The Next 3-5 Years: Two Major Bottlenecks for Enterprise Agentic AI Deployment

When discussing how enterprises should prepare for Agentic AI, Lusha Chen identified two major bottlenecks: data and people.

Bottleneck One: The Data Silo Problem

Many enterprises haven't even completed foundational digital transformation or cloud-native migration. Their data remains fragmented and siloed. When AI attempts to access this data, it often "can't make sense of it." Without a clean, unified data foundation, intelligent agents are a non-starter.

Data Silos stem from the historical evolution of enterprise IT systems — different departments purchased systems from different vendors at different times (e.g., SAP for ERP, Salesforce for CRM, Workday for HR), each maintaining independent databases with different data formats, naming conventions, and update frequencies. According to McKinsey research, large enterprises have an average of over 400 independent data sources, of which only about 20% are effectively utilized. For AI agents, data silos mean they cannot access complete business context — for example, a customer service Agent that can only access the ticketing system but cannot connect to order and logistics systems cannot provide truly valuable end-to-end service. Common solutions include data lake/data mesh architectures, unified API gateway access, and knowledge graph construction.

Bottleneck Two: The "AI Readiness" Misconception

The second bottleneck is more subtle. Lusha Chen points out that 90% of business personnel may think "I opened Doubao today and used an AI tool, so I'm already AI Ready" — but that's far from the truth.

True enterprise-grade AI needs to integrate with enterprise data, meet compliance requirements, and be applicable by business teams — even built by business personnel themselves as Agents. This requires the organization's assets (data, SOPs) and human assets (awareness, skills) to drive forward in parallel, enabling the organization to truly become AI Native.

AI Native is an extension of the Cloud Native concept, referring to a state where an organization designs and operates everything — from underlying architecture and business processes to personnel capabilities — with AI at the core. Unlike "AI-enabled" (layering AI tools on top of existing processes), AI Native means business processes themselves are redesigned around AI capabilities — for example, not "using AI to help humans write reports," but "letting Agents automatically generate reports while humans only review and make decisions." Achieving AI Native requires transformation on three levels: technology (unified data foundations, model-as-a-service), processes (redefining the division of labor in human-AI collaboration), and organization (establishing AI literacy training programs, adjusting performance evaluation criteria). Currently, enterprises that have truly achieved AI Native status globally are few and far between, with most still in the transition from pilot projects to scaled deployment.

Conclusion: The Road Is Long, but the Journey Begins Beneath Our Feet

Enterprise AI transformation doesn't happen overnight. As Lusha Chen puts it, this process is "long and not easy, but the road is right under our feet, and we have to walk it step by step."

Whether it's Dify enabling enterprises to build AI applications as easily as assembling Lego blocks, or underlying network services consolidating resources into platform-level capabilities, the fundamental goal is the same — lowering the barriers to enterprise AI deployment so that AI can go far and stay stable.

From the race for compute power and models to today's pursuit of real production-grade agent value, the AI industry is shifting from "technical showmanship" back to "business fundamentals." Whether an enterprise can overcome the dual bottlenecks of data and people will determine if it can truly enter the fast lane toward AI Native.