Organizational Dilemma of In-depth Application of WorkBuddy: Seven Fractures from Individual Productivity to Enterprise Capability

It has become a trend for enterprise business personnel to independently develop software with workbuddy, but the enterprise governance framework generally lags behind, which is an indisputable fact at present and also a problem that enterprise leaders must pay attention to.

Business personnel developing business applications independently with the help of agent tools such as WorkBuddy has become a remarkable trend in the current enterprise AI implementation. This change has positive significance: it breaks the traditional software development's single dependence on the professional IT department, enables front-line personnel who are most familiar with business scenarios to have the ability to directly build tools, thus bringing substantial improvements in demand response speed and scenario fit.

However, when applications enter the stage of "deeply embedded in business" from "shallow trial", most enterprises have not simultaneously established a matching governance framework. As a result, the shortcomings of organizational capabilities originally covered by the tool dividends are intensively exposed. Today, Lao Yang will systematically sort out seven types of structural problems caused by the in-depth use of WorkBuddy by business personnel from the perspective of enterprise architecture and organizational governance.

I. Seven Faults Caused by In-Depth Application

1. Asset Ownership Fault — Development results are deposited on personal terminals and not included in the enterprise asset system.

This is the most common phenomenon at present. The codes, configurations and data generated by business personnel through WorkBuddy are usually stored on personal computers. Due to the limitation of technical capabilities, business personnel have never thought about systematic deployment and maintenance on professional servers. Therefore, the system development work of business personnel has too much arbitrariness and looseness, and is not included in the enterprise's version management, centralized storage and intellectual property management system. When the developer leaves the job or the device fails, the relevant applications cannot be effectively taken over and continuously operated and maintained, and the previous investment will be lost accordingly.

From this point, it is not difficult for us to see that the essence of current business personnel using workbuddy for development is to bind the core productivity of the enterprise to individuals rather than the organization, which violates the basic requirement that IT assets should belong to the organization and be inheritable. This reflects the management capability of an enterprise, and also reflects a problem from the side: Technology cannot save management, or in other words, technology cannot fill the loopholes in management.

2. Data Security Fault — Lack of backup, encryption and permission control mechanisms.

Still limited by professional capabilities, most business personnel use AI tools like workbuddy for software development just for simplicity and ease of use, and never consider data security. It is worth noting that the systems developed by business personnel often directly call production data such as customer, contract and capital data, and they never consider data backup issues, fail to establish backup strategies, and do not implement data classification and access control. Once the terminal is infected, the hardware is damaged or unauthorized access occurs, it will lead to business interruption, even data leakage and compliance risks.

In terms of security and ease of use, business personnel often choose the simplest possible solution, and the cost of this behavior of ignoring data security will be huge.

3. Integration Governance Fault — "System interconnection" without professional review creates new risks.

With the deepening of system development, the interconnection between self-built systems and related third-party business systems is a problem that must be faced. It is difficult to generate value without interconnection with existing business systems, but interconnection faces technical difficulties. At this time, the problem arises: some enterprises simplify professional issues, thinking that AI can handle everything. If business personnel forcibly realize interconnection in a non-standard way, although the connection is realized on the surface, hidden dangers may be buried at the level of data integrity and system stability.

It should be known that integration belongs to the category of enterprise architecture governance in nature, and should be reviewed in scheme and controlled in implementation by teams with professional capabilities. However, the problem of most traditional enterprises at present is the lack of awe for technology, and they always like to use arbitrary behaviors to solve rigorous technical problems.

4. Value Positioning Fault — Comparing amateur self-developed products with professional commercial products, forming irrational expectations.

At present, there is another problem that plagues business personnel, that is, the "high standards and strict requirements" of some leaders. Lao Yang believes that some enterprises have not planned and formulated a scientific path for the application strategy of workbuddy, which belongs to a "chaotic combat" mode. Some managers even do not distinguish the essential difference between self-developed lightweight applications and professional software companies. When they see a little system development achievement of employees, they require the former to surpass the latter in function completeness. This misalignment of expectations stems from the lack of understanding of software engineering complexity and long-term operation and maintenance costs, which cannot achieve the goal and also consumes organizational trust.

So it is not difficult to see that positioning determines direction and results. Just as a CIO friend said in the group: The boss's illusion is not the technical illusion of AI, but "It's very easy for AI to do this thing!".

5. Investment Mechanism Fault — Replacing resource investment with assessment pressure, resulting in mismatched responsibilities and rights.

At present, there is another contradiction that has to be paid attention to but employees dare not mention: some enterprises include AI applications into employees' KPI but do not provide corresponding accounts, computing power and Token budget, making employees undertake organizational goals at personal cost, which is a typical manifestation of serious mismatch between responsibilities and rights.

This practice has squeezed out usage and produced some results in the short term, seemingly saving operating costs for the boss, but at the cost of sacrificing employees' enthusiasm and sustainable output. In the long run, it weakens the health of the entire enterprise's AI application ecosystem.

6. Collaboration Boundary Fault — Single-department tools are difficult to reuse across organizations.

The problem of departmental collaboration is a long-standing problem in the digital era, and it still exists in the AI era. The data caliber and process logic of self-built applications by business departments are mostly defined according to the habits of a single department, lacking unified cross-departmental standards, which limits their value to the inside of the department and also aggravates the islandization. It should be known that enterprise-level efficiency improvement depends on the horizontal interconnection of processes and data between departments, rather than the local optimization of single-point tools.

It is not difficult to see that the lack of collaboration capability makes AI applications stay at "department-level efficiency improvement" rather than "enterprise-level empowerment". Therefore, no matter how powerful workbuddy is, it is difficult to break through the departmental walls.

7. Capability Differentiation Fault — The difference in individual AI literacy widens, and the variance of organizational capability rises.

With the in-depth application of workbuddy, under the same tool conditions, the differences in different employees' demand expression ability, engineering understanding and problem-solving level will expand significantly. If enterprises lack unified empowerment, standards and collaboration mechanisms at this time, structural differentiation of AI application capabilities will appear within the organization, and some key capabilities will be concentrated in a few people, which not only brings single-point dependency risks, but also amplifies the imbalance of overall capabilities.

From the above, it is not difficult to see that the in-depth application of workbuddy accelerates the deepening and manifestation of various contradictions in the enterprise, and the capabilities of employees will be further widened. But what causes all this is not a technical defect, but the enterprise's insufficient preparation in management dimensions such as IT governance, data governance, architecture management, investment mechanism and organizational design, or even no consideration at all.

II. Four Governance Actions

First, establish the capitalization and hosting mechanism of AI outputs.

Enterprises need to clarify that all codes, configurations and data generated by agent tools are enterprise assets, which are forcibly included in the unified code repository, storage and backup system, and supporting basic version management and automatic backup. Eliminating the risk of asset loss that "assets disappear when employees leave" at a low cost is the primary premise of governance construction.

Second, incorporate data security and integration control into mandatory specifications.

Implement data classification, permission control and regular backup for self-built applications; for those involving interconnection with core business systems, the professional technical team must review the interface scheme and security strategy, and unauthorized direct connection to production databases and non-standard interconnection are prohibited. The security boundary is the bottom line for the operation of production systems, which cannot be compromised for the sake of efficiency demands.

Third, replace transfer-type assessment with real resource investment.

Enterprises should provide necessary budgets such as accounts, computing power and Token for AI applications, so that assessment goals match resource guarantees. Only by transforming AI applications from personal burdens to organizational investments can we maintain employees' continuous participation and output quality.

Fourth, correct value expectations and implement hierarchical management.

The management needs to clearly define the respective applicable boundaries of self-developed lightweight applications and commercial software to avoid irrational benchmarking. At the same time, "business-technology translators" should be cultivated to transform the abstract demands of managers into implementable scenarios, and precipitate the effective practices of front-line teams into promotable organizational assets, so as to converge risks in both directions of expectation management and capability construction.

Finally, to sum up:

The in-depth development of self-built systems by business personnel with the help of WorkBuddy is an important progress for enterprise AI applications to move from concept to pragmatism, which is a very positive aspect. But the current problem is that the inertia of organizational management has seriously affected the in-depth application of AI, and the organization has become a "drag" restricting the implementation of AI in enterprises.

Tools lower the threshold of development, but do not lower the threshold of organizational governance — this is exactly the core contradiction faced by most enterprises that deeply apply WorkBuddy at present.

What problems does your enterprise have in the application of workbuddy? Welcome to share in the comment area.

This article is from the WeChat official account "Xiangjiang Digital Review" (ID: benpaoshuzi), Author: Lao Yang, published by 36Kr with authorization.