FDE最好的位置 不在客户那里

This article shares the exploration experience of 3Chat, an AI top-performing sales agent brand, on positioning its FDE (frontline delivery enablement) role. Key insights are as follows:

1. Core conclusion: The optimal place for FDE is not closest to customers, but closest to product iteration. In the AI era, FDE cannot scale by expanding headcount, and must follow a productization path.

2. Organizational adjustment experience: 3Chat adjusted the affiliation of its FDE team three times: it was placed under the sales department and then the customer success department, both of which led to misaligned goals or slowed product iteration. Finally, placing FDE under the product and R&D department, directly managed by the CTO, achieves the shortest path from customer feedback to product iteration. This conclusion aligns with the exploration results of Chinese HR tech giant Beisen.

3. Current implementation status: 3Chat has fully internalized the entire FDE workflow into its 3Chat Builder product, which can automatically complete 60% to 70% of standard AI Agent building work. The core unsolved challenge is performance evaluation for AI Agents. 3Chat is currently testing AI-powered automatic evaluation through conversation trajectory analysis, and FDE roles are gradually transitioning into auditors.

This article introduces the growth model of AI-powered conversion tool 3Chat, offering multiple insights for brands’ customer acquisition and marketing. Key takeaways are as follows:

1. New product value: 3Chat positions itself as a conversion-focused AI top-performing sales agent that can directly replace customer service and sales staff to help merchants sell products. Its 20-person team has already reached $1 million ARR, maintained double-digit monthly growth for consecutive months, and its business model has been initially validated.

2. High adaptability: Merchants only need to clarify their business goals to Builder, which will automatically generate an AI sales agent tailored to their needs, fitting the customer acquisition and conversion logic of different industries and products. Merchants can also adjust the agent independently for high flexibility.

3. Cost and model advantages: Compared with traditional SaaS delivery, the delivery cycle is shortened from dozens of person-days to 1-2 hours, with higher delivery certainty, less rework, and far larger profit margins than traditional models. Furthermore, AI Agents are paid from marketing budgets based on performance, which aligns better with brands’ customer acquisition investment needs than the traditional IT budget payment model of SaaS, matching current trends in online customer acquisition.

This article outlines new opportunities, business models, and risk warnings in AI-powered customer acquisition and conversion for online sellers. Key insights are as follows:

1. New growth opportunity: AI top-performing sales agents have reached maturity, able to replace human customer service and sales staff to complete the full customer acquisition and conversion process. They help sellers reduce sales labor costs and improve conversion efficiency, representing a new growth track.

2. Advantages of the new model: This tool follows a productized delivery path, where Builder automatically completes over 60% of the building work, with only a small amount of human labor handling complex requirements. Non-core work such as internal system integration is outsourced to ecosystem partners. Sellers can also adjust their agents independently to fit their own business needs, and delivery costs are only a fraction of traditional software.

3. More favorable payment terms: Traditional SaaS is paid from corporate IT budgets, pushing the industry into a race to the bottom on pricing. AI Agents are paid from marketing budgets based on performance, which better matches sellers’ investment expectations for customer acquisition tools.

4. Risk warning: The performance evaluation system for AI Agents is still immature, and there is no unified, clear acceptance standard for both buyers and sellers. Continuous testing and adjustment are required in the early adoption stage.

This article shares a new path for corporate digital transformation in the AI era, offering insights for manufacturing factories pursuing digitalization and e-commerce expansion. Key takeaways are as follows:

1. Enlightenment on transformation direction: A founder with 10 years of experience in manufacturing-focused SaaS and nearly 4,000 clients has validated that "SaaS + AI" is a dead end, as the two have completely different product logics: SaaS is built on deterministic if-else workflows, while AI Agents operate on a continuous closed-loop logic of intent understanding → execution → adjustment. Factories should not blindly overlay AI onto existing SaaS systems for intelligent transformation, but need to choose the right path.

2. Enlightenment on organizational operations: Whether digitizing production or sales functions, companies should shorten the path from business feedback to product iteration, and place delivery-related roles closer to the iteration end rather than just the client end, to enable faster optimization of products and services.

3. Reference for business opportunities: Mature AI top-performing sales agent tools are already available to help factories complete customer acquisition and conversion. Factories can use these tools to reduce labor costs for sales and customer acquisition, and focus more on product production and R&D, fitting the division-of-labor model of "building quality products + letting professional tools handle sales".

This article sorts out industry development trends, customer pain points, and viable solutions for the delivery sector in the AI Agent era. Key insights are as follows:

1. Industry development trend: Large language models have upended the original technology stack of software delivery. FDE, the traditional delivery product manager role, is transitioning from a human role to a productized capability. It has become industry consensus that delivery should rely on productization rather than headcount expansion, which has been validated through different paths by industry leader Beisen and 3Chat.

2. Core industry pain points: Rework costs for traditional SaaS delivery often exceed the cost of the original delivery, eroding all profits and leading to low delivery certainty. In the AI Agent era, the probabilistic nature of Agents renders traditional precise evaluation and acceptance methodologies obsolete, and the industry lacks a unified, mature performance evaluation standard.

3. Viable solution direction: Companies can internalize the full FDE workflow into AI products, letting the AI product handle most delivery work for standard scenarios, retain a small internal team to handle complex scenarios, and open non-core system integration work to ecosystem partners to form a layered division-of-labor system. This model greatly improves delivery certainty and reduces costs, while allowing ecosystem partners to profit. Performance evaluation can be tested with AI-powered solutions, letting AI automatically analyze business trajectories to generate evaluation results, with human staff conducting review and auditing.

This article shares practical experience in organizational structure and delivery system building for AI Agent platforms, offering a reference for operation and development of similar platforms. Key insights are as follows:

1. Organizational structure adjustment experience: The affiliation of FDE (delivery role) requires iterative optimization. Placing FDE under the sales department leads to goal misalignment: sales prioritize closing deals while FDE prioritizes sustained delivery outcomes, creating a timeline mismatch. Placing FDE under customer success turns it into a pure service function, slowing product iteration. The optimal solution is to place FDE under product and R&D, directly managed by the CTO, to shorten the path from customer feedback to product iteration. This conclusion has been cross-validated by both Beisen and 3Chat.

2. Productized delivery direction: Platforms should internalize the full FDE workflow into their own products to build automated building tools that handle most delivery for standard scenarios. This greatly improves delivery efficiency, reduces labor costs, and delivers project profit margins several times higher than traditional delivery models.

3. Ecosystem partnership model: Platforms can build a four-layer division-of-labor delivery system: AI handles standard scenario building, in-house FDE handles complex scenarios and reviews, ecosystem partners handle non-standard system integration, and merchants adjust agents independently. This division of labor lets ecosystem partners earn solid profits while reducing the platform’s pressure from non-core businesses.

4. Risk warning: The evaluation and acceptance system for AI Agents is still immature, so platforms need to explore relevant methodologies in advance to mitigate uncertainty risks in industry development.

This article presents the latest industrial trends and new business models in software delivery in the era of large AI models, offering high reference value for research in related fields. Key insights are as follows:

1. New industrial trends: Large models have upended the original technology stack and working methods of software delivery. FDE, the delivery product manager role that emerged in the previous software generation, is transitioning from a human role to a productized capability. The long-running debate over whether FDE is a role or a capability is now obsolete, and FDE has evolved into a complete product-and-ecosystem system.

2. Summary of the new business model: A new productized delivery model suitable for the Chinese market has emerged in the AI Agent sector. Different from Palantir’s model of relying on high-paid talent for delivery, this model internalizes the full FDE workflow into an automated product that completes most standard delivery work, then builds a four-layer division-of-labor system: AI handles standard building, in-house FDE handles complex scenarios, ecosystem partners handle non-standard integration, and merchants adjust independently. This model reduces delivery costs, improves delivery certainty, and allows ecosystem partners to profit, and has already been validated to deliver sustained double-digit monthly growth.

3. New open research question: Due to the inherent probabilistic nature of Agents, traditional software delivery acceptance methodologies are no longer applicable, and the industry lacks a unified, mature performance evaluation and acceptance methodology. This is the core challenge the industry needs to overcome currently.