A recent online discussion about FDEs (Forward Deployed Engineers) brought together AI professionals from R&D, presales, consulting, and other backgrounds. An FDE is someone who understands both technology and business and works directly with enterprises to help implement AI. In Taiwan, this role is more commonly described as an “implementation consultant” or “deployment engineer.”
Over more than two hours of discussion, participants shared candid observations about enterprise AI implementation. Several of their conclusions closely reflect the realities facing Taiwan’s traditional industries. From a decision-maker’s perspective, here are three things enterprises should clarify before adopting AI Agent.
Business Leaders’ AI Anxiety and Valid Demand Are Two Different Things
When speaking with business owners in traditional industries, you may find that their interest in AI is much greater than the technology sector assumes. ChatGPT, AI Agent, digital employees, knowledge bases—they have heard of every trending concept. Anxiety is especially common among leaders in industries under significant operating pressure: Are our competitors already using AI? Can it help us hire fewer people?
The anxiety is real, but anxiety does not equal valid demand. One participant in the discussion proposed distinguishing among three levels:
- The business owner is willing to learn about AI
- The business owner is willing to spend some money experimenting with it
- The enterprise has a quantifiable, recurring problem that can continuously generate value when solved
The first two levels are common today. The third is far less common than many imagine.
Some enterprises have yet to complete even the basics of digitalization, data accumulation, and process standardization, yet they are already asking what AI Agent can do. Once the service provider begins working with them, it often discovers that although the project is labeled as AI, the actual work involves digital transformation or even process consulting.
Decision-makers therefore need to answer one question honestly before adoption: Are you trying to solve a genuine business pain point, or are you reacting to the anxiety that “everyone else in the industry seems to be doing it”? The two require entirely different approaches to investment.
What Kind of Enterprise Is Best Positioned to Adopt AI Agent Now?
Intuitively, the most traditional and least digitalized companies would appear to have the greatest room for transformation. In practice, however, these enterprises are often the hardest to move forward. They have little accumulated data, no unified systems, processes that depend heavily on manual work, and knowledge scattered across LINE groups, Excel spreadsheets, and employees’ heads. For these companies, the first step toward adopting AI Agent is not AI. It is building the information infrastructure they have left unfinished for more than a decade.
At the other end of the spectrum, technology companies with strong R&D capabilities typically do not need outside support.
The enterprises best suited for adoption at this stage are those in the middle—a group described in the discussion as “semi-digital enterprises”:
- Employees already rely heavily on computers for their daily work
- The company has systems such as ERP, CRM, and OA
- It has accumulated documents and data
- Its systems are not interconnected
- A large amount of work involves repetitive transfers, duplicate data entry, and manual decision-making
- It has no internal R&D team
These enterprises have data that is difficult to use, systems that cannot communicate with one another, and processes in which information must be passed manually across numerous steps. In this environment, the role of AI Agent is to reconnect fragmented information flows.
Among EgentHub clients, this is also the group that adopts AI Agent most quickly and sees the clearest results. By using the MCP protocol to connect existing ERP and CRM systems, with implementation consultants supporting process mapping and prompt engineering, most enterprises can bring their first AI Agent online within two to four weeks.
Organizational Problems Are Harder to Solve Than Technical Ones
This was the strongest point of consensus throughout the discussion.
“Improving efficiency with AI” may sound neutral, but within an enterprise, it often means that a particular role becomes less important, a department loses some of its authority, an information barrier built up by one person over many years is dismantled, or work that once required five people can now be completed by one.
When employees resist AI, it is usually because they understand that if the system succeeds, their own bargaining power will be affected.
Enterprise AI projects therefore tend to follow a familiar pattern: business owners are enthusiastic, middle managers are cautious, and frontline employees may not cooperate. Without someone with decision-making authority consistently driving the initiative, even the best technology will fail to gain traction.
For this reason, EgentHub’s implementation process emphasizes ongoing, hands-on support. It helps enterprises establish internal seed teams, access-control rules, and review mechanisms so that AI Agent becomes part of the organization. This differs from the conventional model of delivering a completed system and walking away. Organizational problems do not resolve themselves simply because a system has gone live.
AI’s Long-Term Value: Shifting Organizational Operations from Dependence on Individuals to Dependence on Systems
When most enterprises discuss AI, their first thoughts are saving time and reducing labor requirements. The discussion, however, highlighted a deeper source of value.
Traditional enterprises commonly face one problem: their operations depend heavily on individuals. When a salesperson leaves, customer relationships leave with them. When a veteran employee departs, no one knows how to execute a particular process. When new employees join, there is no comprehensive training system to support them.
Once AI Agent enters an enterprise, it can gradually digitalize these work processes: which problems employees handled, what knowledge they accessed, what decisions they made, and where processes frequently encountered bottlenecks. As this information accumulates over time, it becomes an organizational asset.
From a decision-maker’s perspective, the long-term value of AI Agent lies in helping the company gradually shift from “depending on a handful of individuals” to “depending on systems built from accumulated organizational knowledge.” The organization becomes observable, measurable, replicable, and transferable. That is far more valuable than saving a few dozen minutes of work.
FAQ
My company has not completed its digital transformation. Is it ready to adopt AI Agent?
It depends on how far along the company is. If you already use ERP, CRM, and Excel and have accumulated data, but your systems are not interconnected, this “semi-digital” stage may actually be an ideal time to adopt AI Agent. However, if basic computer-based operations and data accumulation are not yet in place, you should establish that information infrastructure before considering AI.
How should we address employee resistance to AI adoption?
Start by understanding the real cause of the resistance. In most cases, employees are concerned about the impact on their bargaining power rather than lacking awareness or understanding. An effective approach is to begin with highly repetitive, low-contention processes, allowing employees to experience how AI Agent can reduce their workloads before expanding its use gradually. The initiative must also be continuously driven by someone with decision-making authority.
How should we budget for AI Agent adoption?
Avoid treating AI adoption as a one-time project expense. A more practical approach is to proceed in stages: begin with a small-scale validation to confirm feasibility, then establish an ongoing subscription model. Participants in the discussion broadly agreed that a model based solely on one-off project fees is difficult to sustain for both service providers and clients.
What is an FDE, and how is the role relevant to our company?
FDE is a role that has recently attracted significant attention in the AI community. It refers to someone with both technical understanding and business expertise who works directly with enterprises to help implement AI. Most traditional enterprises in Taiwan do not have internal engineering teams, so this role is typically filled by consultants from external service providers. EgentHub’s implementation consultants perform precisely this role.
References
- 范冰, “A Private Group Discussion on How Enterprises Can Implement AI” — Facebook post containing an anonymized summary of an online FDE community discussion (August 2026)
- FDE4.ai — An open-source book by 范冰 about Forward Deployed Engineers and enterprise AI implementation