AI is already highly popular, so why haven't enterprises truly adopted it? Corporate focus on AI has shifted from "whether to do it" to "how to implement it." Top management expects AI to integrate into operations and production, while frontline employees are already extensively using models, Agents, and various AI applications. However, projects that truly enter enterprise production workflows and continuously generate business outcomes remain scarce.
A well-known industry vendor CEO summarized this state as "hot at both ends, difficult in the middle": top management is excited and employees are willing to use it, but the middle part responsible for integrating AI into data, workflows, management, and production systems faces numerous difficulties. Based on observations from a sample of over 1,000 enterprises, only 5% of Agents have entered workflows, with about 95% still stuck in the Demo, pilot, or POC (Proof of Concept) stage.
Although this ratio is not an industry-wide statistic, it sufficiently illustrates the current real watershed for enterprise AI: everyone can now build AI applications, but AI has not yet been universally transformed into a part of organizational workflows or production systems. Therefore, it can be seen that the problem is not whether enterprises have purchased models, knowledge bases, or Agent tools, but whether these capabilities can be integrated into the enterprise's business and operational systems.
This is also the most easily overlooked contradiction in today's enterprise AI market: vendors are selling by components, while what enterprises truly need is a system that can run in the long term.
01. Demo Proves It Can Be Built, Production Requires Long-Term Accountability
AI Coding and Agents have significantly lowered the threshold for prototype development. Applications that previously required weeks of collaboration among product managers, designers, and development teams can now be turned into seemingly complete Demos by business personnel after just a few rounds of dialogue. This in itself is progress, bringing business personnel closer to software production and allowing enterprises to validate ideas at a lower cost. However, a runnable Demo only proves that "functions can be built," not that "the enterprise can be accountable for it in the long term."
Moving from Demo to production requires answering at least several additional categories of questions:
- Is the data continuous, accurate, and stably callable? Is the definition of the same business object consistent across different departments? Has the Agent obtained the context required to complete the task? What data can it access, what tools can it call, and what is the basis for permissions? Do the suggestions it generates require approval? Can erroneous actions be rolled back? Can issues be audited and held accountable? After project delivery, who will update the rules, knowledge, Skills, interfaces, and evaluation criteria?
These questions do not fall into the category of "developing the functions a bit more completely," but are responsibilities that production systems must bear. After deeply using AI, many enterprises have realized that the real difficulty is not whether the model can answer, but that the internal data calibers, permission systems, and responsibility boundaries within the enterprise are not ready.
An application can call all data in a demo environment, but upon entering the production environment, it must know who can view, who can modify, and who can approve; an Agent may perform well on test data, but after entering real business, it may fail due to missing historical data, business rules hidden in expert experience, or inconsistent states in upstream and downstream systems.
Recent numerous surveys by iFenxi have also repeatedly shown similar judgments: application deployment is not necessarily difficult; the difficulty lies in meeting business requirements and continuously generating results. Many Demos look very attractive, but upon entering production, they expose issues such as hallucinations, unstable interfaces, unclosed-loop workflows, and security compliance problems. In other words, AI is reducing the cost of "building an application," but has not solved the problem of "making this application an enterprise system." Enterprise AI projects generally go through four stages:
- Personal use: Employees use AI to complete writing, analysis, retrieval, or coding; Departmental pilot: A team builds an Agent or application around a specific task; Organizational takeover: The enterprise incorporates data, permissions, workflows, and responsibilities into management; Production operation: The system is continuously used, evaluated, maintained, and iterated.
Figure 1: Four Stages of Enterprise AI Projects
Most products in the current market can help enterprises complete the first two stages; what enterprises truly lack is the bridging mechanism to move from the second stage to the third and fourth stages.
02. Why There Is a GAP Between Market Supply and Enterprise Demand
Vendors are selling components, while enterprises want systems. This statement is not simply blaming vendors for insufficient capabilities, but because the problem boundaries of both parties are inherently different.
1. Vendor capabilities are fragmented by technical boundaries, while enterprise problems occur by business workflows
Model vendors excel in models and reasoning, data platform vendors excel in data ingestion, governance, and computation, Agent platform vendors excel in workflow orchestration and tool invocation, vertical software vendors excel in business workflows, and integration service providers excel in deployment and system connectivity.
All these capabilities are important, but the problems raised by enterprises are usually not "I want a model" or "I want a knowledge graph," but rather how to make procurement reconciliation faster, how to detect equipment anomalies earlier, how to directly connect operational analysis to actions, and how to reduce waiting times in cross-departmental approvals.
Enterprise problems naturally span multiple technical boundaries, while vendor products usually stay within a single capability boundary. As a result, a business closed-loop requires the splicing of multiple products; all products can be used, but no single system is truly accountable for the final business outcomes.
2. Components are easy to demo and procure, while systems require long-term operation
For an Agent or Skill, it is relatively easy to define functions, pricing, and delivery cycles. An enterprise system, however, requires continuous maintenance of data, context, permissions, rules, interfaces, and business metrics. From a sales perspective, components are easier to price and accept; from an enterprise operation perspective, what truly matters is whether the system can work stably in the long term. This natural difference makes market supply more easily organized around "what components to sell" rather than "what business closed-loop the enterprise ultimately needs to complete."
3. Project-based delivery cannot cover the continuous changes of AI systems
Traditional projects usually end with project initiation, development, go-live, acceptance, and closure. However, after an AI system goes live, models will change, business rules will change, data and permissions will change, and effects also need continuous evaluation. If contracts, budgets, and responsibility mechanisms only cover one-time delivery, it is difficult for vendors to truly assume system operation responsibilities, and difficult for enterprises to obtain continuous capabilities. The delivery endpoint of AI projects should not just be "go-live," but rather that the system can run stably in real business and has clear maintenance and iteration mechanisms.
4. Enterprises lack a unified system Owner internally
Business departments best understand the problems, but may not be able to complete data governance and production deployment; IT departments are familiar with systems, but may not understand processes, production scheduling, supply chains, and operational processes; security departments care about risks, but may not have decision-making power in business workflows. Without the joint participation of business Owners, platform Owners, data Owners, and security Owners, it is difficult for enterprises to form a system no matter how many components they purchase. A more realistic organizational approach is for business to be responsible for value and workflows, IT to be responsible for architecture, integration, and production, and data and security teams to be responsible for boundaries and quality, forming a joint responsibility mechanism. Therefore, the supply-demand contradiction in enterprise AI is not that there are no products in the market, but that product boundaries are inconsistent with enterprise problem boundaries.
03. How Enterprises Can Build a System from a Single Business Closed-Loop
Enterprises do not need to build a "big platform" covering all departments, all data, all models, and all businesses from the very beginning. A more feasible path is to start from a real business closed-loop, running, governing, and accumulating along the way, and then gradually expanding.
Figure 2: Six Steps for Enterprise AI Implementation
Step 1: Inventory ongoing AI usage and assets
Enterprises first need to know: which models and AI tools employees are using, which Agents, Skills, and applications have already generated value, which data is being called, which capabilities have redundant construction, which applications have entered business workflows, and who is responsible for the results. This step is not to take back employee innovation, but to make decentralized AI usage visible, evaluable, and manageable. Enterprises can first establish a minimal AI asset registration and risk classification mechanism, gradually distinguishing between personal use, departmental pilots, and formal applications.
Step 2: Select a high-value, cross-link, controllable scenario
The first scenario should not only look at "whether a Demo can be built," but should meet five conditions:
- Clear business value, capable of measuring changes in efficiency, cost, quality, or risk; spans multiple business links, capable of verifying the connectivity of data, systems, and workflows; relatively clear data boundaries, without needing to access group-wide data from the start; controllable action risks, allowing for initial queries, analysis, suggestions, and manual approvals; clear business Owner, capable of continuously providing rules, knowledge, and feedback.
Production scheduling, equipment O&M (Operations and Maintenance), procurement reconciliation, supplier analysis, and operational anomaly identification can all be candidate directions. The focus is not on the scenario name, but on whether a complete closed-loop of "data—judgment—action—feedback" can be formed.
Step 3: Build a minimum viable system around the scenario
The first project cannot just deliver an Agent, but must simultaneously form: clear business workflows, confirmed business objects, data, and rules, a reusable Agent or Skill, at least one real business system connection, and permission, approval, audit, and rollback mechanisms. In addition, business outcome metrics must be defined in advance. Metrics can be processing time, manual effort, error rate, inventory turnover, anomaly detection rate, or approval cycle, but must not just count the number of calls and Agents. High call volume does not equal high business value; a large number of Agents does not mean enterprise capabilities are growing.
Step 4: Incorporate local results into a unified asset system
After a successful pilot, the registration of Agents, Skills, and applications must be completed, clarifying responsible persons, versions, and lifecycles, unifying identities, permissions, and data boundaries, and continuously monitoring calls, costs, security, and effects. More importantly, the enterprise must take over data, rules, knowledge, and application assets, rather than leaving all capabilities in the vendor's project environment. Only by completing the takeover can local projects become organizational assets; only by being reusable will the enterprise avoid buying and building a new set in every department.
Step 5: Unified group foundation, business units maintain professional autonomy
At the group level, identities, permissions, security, model access, data specifications, asset catalogs, and operation mechanisms can be uniformly built; each business unit builds professional scenarios and Skills based on its own business. A unified foundation does not mean all applications must look the same, nor does business autonomy mean each department repeatedly builds a set of platforms. What truly needs to be unified are rules, boundaries, and asset management methods; what truly needs to retain differences are business objects, workflow knowledge, and professional experience.
Step 6: Expand to adjacent workflows based on operational feedback
Only when the first scenario can run continuously should the enterprise expand to more departments and workflows. The basis for expansion should come from real operational data: usage rate, adoption rate, error rate, business outcomes, call costs, and reuse times, rather than the number of platforms launched or Agents. The expansion of enterprise AI should not be "connecting a few more models and developing a few more Agents," but gradually connecting from one closed-loop to adjacent closed-loops, ultimately forming a set of sustainably operable organizational capabilities.
04. How Enterprises Should Choose Vendors and Cooperation Models
When selecting vendors, enterprises must first judge whether the vendor is selling components, projects, or system capabilities.
1. Validate vendor capabilities with production problems
Enterprises should ask at least:
- Can it connect to real business data and legacy systems, rather than just running in a demo environment? Can it manage Agents, Skills, applications, and versions? Can it handle identities, permissions, data masking, approvals, audits, and rollbacks? Can it accumulate scenario experience into reusable assets? Does it have production delivery and long-term operation capabilities? After the project ends, can the enterprise take over, migrate, and switch models or vendors?
If a vendor can only show a Demo with very good effects, but cannot explain how data enters, how actions are implemented, how errors are held accountable, and how assets are handed over, then what it provides is more likely component or pilot capabilities, rather than system capabilities.
2. Do not require one vendor to handle all capabilities, but there must be a system-level responsible person
Enterprises can adopt a multi-vendor combination: model vendors provide models, data vendors provide data capabilities, vertical software vendors provide workflow capabilities, and integration vendors complete system connections. This combination is not a problem; the problem is that no one is responsible for the overall results after the combination. Therefore, even when adopting multiple vendors, the enterprise must clarify a system-level partner capable of standing at the global level to be responsible for architecture coordination, asset accumulation, operational metrics, and long-term operation. Otherwise, the interfaces, permissions, data, and responsibility boundaries among multiple components will ultimately become extra work for the enterprise itself.
3. Contracts must cover long-term operation, not just one-time delivery
At least the following should be clarified:
- Ownership of data, context, Skills, Agents, and business rules; maintenance responsibilities for semantics, permissions, knowledge, and interfaces; migration methods during model switching and vendor exit; responsibility boundaries for security incidents and business errors in the production environment; operational services, metrics, and response mechanisms after go-live; reuse methods when expanding from one scenario to others.
This is the procurement and governance approach that must be changed synchronously when enterprises shift from "buying a project" to "building a long-term capability."
05. Conclusion: The Next Round of Enterprise AI Competition Occurs at the System Level
In the first half of enterprise AI, the competition is about who can build a Demo faster; in the second half, the competition is about who can turn the AI capabilities in employees' hands into organizational assets that the enterprise can uniformly manage, continuously reuse, and constantly operate. For enterprises, the most important thing is not to own the most models, Agents, knowledge bases, or Skills, but to build a system that can:
- Allow employees to freely use and create; enable the enterprise to see and manage these capabilities; allow excellent Agents and Skills to be reused; enable continuous accumulation of data, knowledge, and context; connect AI to real business workflows; enable results to be tracked, evaluated, and improved
The true watershed for enterprise AI is not whether AI is deployed, but whether AI can be integrated into the organization's operational methods.