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NewsMaterial5 min readDisconfirmAI Editorial

Aotter Vice President Su Peng-han: AI Can Read Enterprise Data Without Replacing ERP—Standardizing Data and Introducing MCP Logic

In an interview, Aotter co-founders Su Peng-han and Xie Lun explained the core strategy of their enterprise AI product, AotterClam AI: rather than replacing AI agents, they standardize messy data scattered across deeply customized traditional ERPs and expose it to clients' existing Claude or GPT models via the Model Context Protocol (MCP). Using manufacturer East Asia Lighting as a case study, they illustrate this operational logic while noting that the true bottleneck in adoption is often not technology but the tacit operational knowledge held by "that mysterious person" within the organization.

2026-11-11 13:09 GMT+8

When East Asia Lighting first approached Aotter, the problem they wanted to solve was not "which model to use," but who could help them replace their old ERP. Replacing an ERP is a massive undertaking for traditional industries—many companies' ERPs were highly customized years ago by system integrators who cannot provide APIs or cooperate with development. Even if a new vendor creates an AI chatbot, integrating it would hit roadblocks. Actually replacing the system could paralyze the company for half a year, and the people and processes bound to the system would be difficult to change alongside it.

Su Peng-han, Aotter's co-founder and vice president, offered a solution: do not ask clients to replace their ERP. His positioning judgment is clear: build tools for AI to use, rather than trying to replace or compete with AI agents, because agents are too powerful. The specific approach is that after the client explains their workflow, Aotter's Forward Deployment Engineer (FDE) team, accompanied by product managers and business development personnel, moves in to pull scattered data from various systems into AotterClam AI's data middleware. This middleware sits on top of the client's original ERP, standardizes the data so AI can understand it, and leaves subsequent analysis and applications to the AI.

Founded in 2012, Aotter began as a software R&D company and owns the YouTube channel Aotter Girls, which has millions of subscribers. Launched in April this year, the enterprise AI data brain AotterClam AI focuses its efforts on organizing enterprise data scattered across different locations. Once organized, the data is handed over to the AI already in use by clients via the MCP protocol. Executives do not need to understand what MCP is; they can simply ask questions in the Claude or GPT interface to receive analysis.

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How effective is it in practice? Xie Lun, Aotter's co-founder and CEO, said that mid-to-senior level executives at East Asia Lighting now directly open Claude or GPT. To prepare board reports, they just tell the AI to pull up the gross margin for this quarter, how much it grew, the reasons why, which business metrics are lagging, and which sales figures are high but profits are low—the answers come out instantly.

Xie Lun added a deeper observation: the real pain point in manufacturing often lies in transportation. Choosing between sea freight and air freight involves significant cost differences, yet there is no API for sea freight, though inventory data shows the status. In the past, with specialized division of labor, everyone only looked at their own systems, leaving many gaps in company operations; once data is connected, one can determine that a batch of goods does not need air freight and is still on time, allowing schedules to be optimized immediately.

However, the most difficult step in data standardization often gets stuck on people. Su Peng-han mentioned a phenomenon: every company has a very mysterious person who knows how to fill out the form. This person might be an auntie or an uncle who knows what to write in which field under what circumstances. Having operated the system tens of thousands of times, things that look difficult to others are natural to him; he even knows what to do during the 30 seconds it takes for a submission to go through. Many ERPs are not deeply customized for specific industries, so each industry develops its own special usage methods. 'We don't have orders, so why must the title on that entry form say Order?' Everyone calls it an order, but in reality, it is not an order.

This also explains why the best counterpart for promoting AI adoption is mid-to-senior management rather than the boss. Su Peng-han used the analogy of switching from kerosene lamps to electric lights: 'The masters and mistresses feel nothing because it is the servants who are making the switch.' The servants find it very convenient, but the boss just sees that the lights turn on and doesn't understand why a switch is needed. What truly gives the boss a sense of crisis is population aging—some manufacturing firms originally had 8 to 10 finance and accounting staff, and those familiar with traditional ERP workflows will retire one by one within five to ten years.

Regarding information security strategy, Su Peng-han offers a concise criterion: 'If you are willing to upload this data to Google Sheets, then it should be fine.' At least then you can use cloud models; without overly vertical applications, eighty percent of needs can be handled. For enterprises still wary of the cloud, Aotter can also provide on-premises solutions, but will warn that on-premises models have limited capabilities. The recommended approach is to categorize data by sensitivity: truly sensitive data should be processed slowly on-premises; data that has been aggregated into quarterly reports with names and vendor contact information removed can use the cloud to gain broad insights.

It should be noted that all the above effectiveness descriptions—including Xie Lun's claim that 'a fully customized ERP can be online in 15 minutes'—are self-reported by the vendor and have not been independently verified. Aotter serves mostly older enterprises; these companies are not short of money and have stable niche markets. Once they switch to new systems and processes, they can extend their lifespan, but this condition may not apply to all industrial stages. The data middleware itself becomes a new dependency: if the standardization process misses key business semantics, the 'clean data' read by AI may actually be incorrect data.

Su Peng-han believes that current concerns about AI are similar to concerns about the cloud ten years ago. Back then, enterprises worried that putting hosts on the cloud meant Google would know everything; even earlier, people dared not even swipe cards online. He described this as a paradigm shift; in ten years, the next generation will have been chatting with AI since birth. This analogy itself is also an assumption—it presupposes that trust building for AI will follow the same trajectory as the cloud, but AI handles reasoning rather than storage, so the risk structure may not be the same.

Sources

  • inside.com.twINSIDE↗
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