The Red Queen Effect in Enterprise AI Implementation: Why You Run Desperately But Stay Right Where You Are

The budget for AI technology has doubled, yet the competitiveness remains stagnant. The problem does not lie in the market, but in the misalignment between productivity and production relations inside the enterprise.

Over the past two years, with the emergence of various AI tools, enterprises' AI budgets have generally doubled, while inquiries from bosses and senior management about AI "output" have also doubled simultaneously. A very realistic problem is: while technological supply is upgrading and enterprises are increasing their investment, most enterprises' relative competitiveness, organizational effectiveness and operational efficiency have not risen proportionally. In other words, technology is constantly upgrading, but enterprise management has not changed accordingly and remains stagnant, just like the Red Queen Effect.

I. Redefine the Red Queen

According to Baidu search: the concept of the Red Queen Effect originates from *Through the Looking-Glass* by British writer Lewis Carroll. In the story, the Red Queen pulls Alice to run as fast as they can, but when they stop, they find they are still in the same place. The Red Queen says: "Here, you have to keep running just to stay in the same place." That is to say, the two of them run desperately but end up staying where they started.

If applied to enterprise AI scenarios, we will see the following situation: large models iterate on a quarterly cycle, and various AI tools are upgrading rapidly and continuously; enterprises simultaneously increase procurement, pilot projects and computing power to catch up with the technology curve. However, the organizational mechanism that connects technology and business value, including division of labor, authorization, collaboration and assessment, remains almost unchanged.

The result is: technology investment is increasing year by year, while the organization's real capabilities, process efficiency, decision-making quality and talent density stay in place. Enterprises think they are catching up with the era, but in fact they are using new technologies to replicate old management. Compared with the capabilities that the enterprise should have had, the displacement is zero. This is exactly the form of the Red Queen Effect inside the enterprise.

II. Root Cause Diagnosis

Lao Yang believes that the core cause of this problem is the mismatch between productive forces and production relations. AI represents a clear leap in productive forces: large models, agents and automation have pushed the capabilities of analysis, generation and execution down to individuals. According to the law of organizational evolution, after the productive forces change, the production relations, organizational design, power and responsibility arrangement, collaboration mechanism and assessment system must be adjusted accordingly, otherwise the new productive forces will be hindered by the old structure.

Going back to enterprise AI application scenarios, you will find that the productive forces have achieved a leap, but the production relations still remain in the hierarchical paradigm of the industrial age: pyramid hierarchy, departmental barriers, manual approval, KPI counted by delivery volume, and IT positioned as a cost center rather than a capability center. Embedding large models into such a structure will only lead to two results: distortion, where AI is reduced to a copywriting and Q&A tool; rejection, where the pilot project delivers excellent results, but large-scale production will be stuck in the approval and collaboration links.

From the above, it is not difficult to see that the purpose of enterprises running desperately with the help of technology is no longer to move forward, but to offset the drag of the old system and maintain the appearance of not falling behind.

III. Analysis of Problem Causes

Why do such results arise? Lao Yang's analysis is as follows:

First, technological inflation and diminishing marginal returns.

From small models to GPT-level large models, training and reasoning costs have risen sharply. The computing power deployed by many enterprises in the previous year becomes insufficient in the next year. To keep up with technology, investment must continue to increase, while technology is updated again in the next quarter, and the marginal return on input-output ratio continues to decline. This is the non-stop inertia on the productive force side.

Second, the organizational mechanism is locked, and the production relations lag behind.

The value of AI can only be demonstrated by going deep into business scenarios, but the procurement approval of many traditional enterprises still relies on middle managers' signatures, cross-departmental data still relies on offline reconciliation, and the reporting chain is unstable with multi-level jumps. Technology can complete interface docking and integration within a few days, but the problem is that once organizational issues are involved, it is impossible to promote the change of even one node within several months.

Third, the assessment orientation is mismatched, and the indicator guidance is distorted.

Some enterprises' digital departments are assessed by the number of launched systems, and AI teams are assessed by the number of delivered projects. The core of the assessment indicators is delivery rather than business results, which gives rise to a large number of proof-of-concept and demonstration achievements, while the actual landing applications are close to zero.

Fourth, expert resources are idle and capabilities are not internalized.

Some enterprises spend a lot of money to introduce algorithm engineers and AI experts, but do not grant them data access rights and business decision-making participation rights. This leads to experts being consumed in data acquisition applications and departmental coordination, becoming high-cost idle resources. The tools are in place, but the people who use the tools are bound by the old organizational relations, which results in the investment in productive forces not being transformed into organizational effectiveness.

IV. "Pseudo-Implementation": The Misunderstood Progress

At present, most enterprises are actively embracing AI, and various achievements have appeared in various reports, even on the big screens at industry conferences, but the actual landing applications are very few, and most scenarios are "pseudo-implementation". For example, some enterprises want to maintain their technological advancement in the industry and become followers of advanced technologies, so they launch a large number of proof-of-concept projects. Although the demonstration effect is outstanding, there are countless problems in actual implementation. In other words, enterprises have made real investment in the "running" posture, but their capabilities are "pseudo-advanced".

Many enterprise leaders believe that the adoption of AI will immediately reduce costs and increase efficiency. It is undeniable that a single point project may save partial labor, but the new engineering, labeling and computing power expenses for maintaining the AI system often exceed the saved costs. This is a typical "pseudo cost reduction". In addition, some enterprises have equipped with conversational robots and prediction models, but the cross-departmental collaboration mode, decision-making chain and talent training system remain unchanged, and the new organizational learning capability has not been established. That is to say, the equipment has been upgraded, but the organization is still that "pseudo-organization".

V. How to Break the Deadlock?

Lao Yang believes that the key to breaking the Red Queen Effect in enterprise AI implementation is not to run faster or switch to a new track, but to reconstruct the lagging production relations, so that the productive forces can be truly transformed into organizational effectiveness.

First, internalize capabilities in business units instead of IT silos.

In the old mechanism, digitalization was handled by the IT department, and the business department only put forward requirements. The breakthrough path is to let business units build their own AI application capabilities, and the IT department retreats to provide platform and governance support. Only when capabilities are precipitated within the organization will they not disappear with the acceptance of a single project.

Second, reconstruct processes and powers & responsibilities simultaneously, instead of adding AI to old processes.

If the goal is only to let AI do substitution, this is only local optimization and it is difficult to form a competitive barrier. The real gap comes from redrawing the approval chain and decision-making chain with the help of AI: which links are decided by humans, which are executed by machines, and how to re-divide the powers and responsibilities. Only by reforming processes and powers & responsibilities together can the production relations be truly adjusted.

Third, delegate data access rights and decision-making participation rights.

The introduced AI experts and business seed users must have the real authority to access data and reach the processes. Decentralization is the core of production relations. If the authority is not granted, the investment in productive forces will be left idle.

Fourth, acknowledge the asynchrony, but never stop reforming.

Technology iteration is measured by quarters, while organizational evolution is measured by years. The two are naturally asynchronous. Enterprises can follow at the infrastructure layer and conduct trial operations at the application layer, but the reconstruction of production relations, including decentralization, assessment and processes, cannot be stagnated for a single day. The ruler to measure success is not whether you have the most advanced technology, but whether the organization is better at using technology than it was a year ago.

VII. Final Summary:

To sum up, the Red Queen Effect in enterprise AI implementation is essentially a structural mismatch between the leap of productive forces and the lock of production relations. Simply increasing technology investment can no longer bring about organizational progress; only adding new power to the old system will only make the organization consume in place. Enterprises must answer a more fundamental question: when technology continues to upgrade, why do our management, organization, processes, assessment and decentralization still remain at the level of three years ago?

This article is from the WeChat Official Account "Xiangjiang Digital Review" (ID: benpaoshuzi), author: Lao Yang, published with authorization from 36Kr.