The speed of deriving business value from artificial intelligence systems depends directly on the state of an enterprise's IT infrastructure. According to the Cisco AI Readiness Index 2025, which surveyed over 8,000 AI leaders across 30 countries and 26 industries, only 13% of organizations are classified as "Pacesetters." These companies are the ones capable of consistently delivering results from AI. This indicates that successful integration of AI-native systems is primarily a challenge for corporate architecture, rather than just a matter of choosing the right LLM.
The main obstacle lies in outdated methods of data preparation (Data Readiness). Instead of embedding structuring directly into the infrastructure layer, companies spend significant resources on creating temporary "data lakes" and developing custom ETL pipelines to clean information post-factum. This approach creates technical debt and makes scaling AI projects economically inefficient.
Why AI-readiness is an architectural, not an algorithmic, problem
An analysis of the 13% market leaders shows that they do not simply build isolated sandboxes for generative AI; they systematically rebuild their data architecture. Traditional corporate IT infrastructure was designed for transactional systems, where the priority was record integrity rather than context availability for intelligent agents.
For AI-native systems, it is critical to have an understanding of relationships and semantics in real-time. If the infrastructure does not provide this, engineers are forced to build intermediate integration layers, leading to delays, loss of context relevance, and uncontrolled growth in computing costs. Readiness for artificial intelligence is defined by the infrastructure's ability to supply machine-readable, secure, and semantically linked data by default.
The "data lake" trap: why post-processing costs more than proper design
The popular concept of Data Lakes, where raw data is poured into a general repository for future processing, often leads to the emergence of unmanageable "data swamps." The main infrastructure obstacles with this approach include:
- Legacy ERP systems: Data is stored in proprietary formats. For every new AI model integration, unique ETL pipelines must be created, which fail whenever the database structure changes.
- "Data swamps": Raw logs, scans, and documents accumulate in cloud storage without metadata, making them unsuitable for RAG (Retrieval-Augmented Generation) systems without significant investment in cleaning and vectorization.
- Manual API generation: Creating endpoints for every new entity creates a bottleneck. AI agents lose the ability to dynamically access new corporate information sources.
According to the FinOps Framework and the AWS Well-Architected Cost Optimization methodology, proper architectural modeling at the design stage is significantly more effective than post-launch optimization. If data requires constant rewriting of ETL pipelines, the total cost of ownership (TCO) for the AI system grows exponentially.
The Data-as-Model concept: metadata as a foundation for AI
An effective solution to overcome this chaos is transitioning to the Data-as-Model concept. Under this approach, data structure, business logic, security rules, and domain metadata are described centrally and serve as a single source of truth for the entire system.
When infrastructure relies on a domain model, it can automate routine processes:
- Synchronize physical database structures without the need for manual scripts.
- Generate standardized API contracts and web services based on metadata.
- Provide AI agents with semantically linked entities instead of disparate tables.
This approach turns domain metadata into a "translator" between rigid relational database structures and the semantic needs of large language models.
Security and Machine Trustworthiness: embedding RLS at the model level
In the context of AI-native systems, security takes on a new dimension. The Cisco Cybersecurity Readiness Index 2025 (a survey of 8,000 cybersecurity leaders) identifies Machine Trustworthiness and AI Fortification as critical pillars of protection. The greatest threat is the leakage of confidential corporate data through unauthorized access by an AI agent to restricted context.
Traditional security, which acts on top of existing APIs, is often vulnerable to prompt injection techniques. Security must be integrated directly into the data model.
In the Data-as-Model architecture, access control mechanisms—such as Row-Level Security (RLS) and Access Control Lists (ACL)—operate at the level of the entity infrastructure description. A request from an AI agent is automatically filtered before the data even enters the model's context. This makes it impossible to transmit confidential information, even in the event of malicious prompts.
Practical implementation: AI-native architecture without custom ETL
Building infrastructure based on the Data-as-Model principle requires a platform-based approach. One example of such a toolkit is the full-stack JavaScript low-code platform UnityBase (a joint development of the Intecracy Group alliance, with InBase as the key developer). The platform is built around the concept of domain metadata management.
Instead of manually coding every layer, the architect describes the Domain metadata, which the platform then uses as a single foundation for:
- Creating DBMS-agnostic ORM: Working with various DBMS (PostgreSQL, Oracle, MS SQL Server) without vendor lock-in.
- Automatic REST API generation: The platform independently creates endpoints ready for interaction with AI models and external services.
- Built-in audit and security: Automatic application of role-based models (RBAC) and row-level policies (RLS), which is critical for Machine Trustworthiness.
Enterprise products from the alliance, such as the Megapolis.DocNet electronic document management system or the Scriptum.DMS document management system, utilize UnityBase platform mechanisms to ensure secure interaction with their own AI centers. Thanks to generated APIs and metadata, AI components work directly with a secure domain model without requiring additional ETL pipelines.
| Architectural criterion | Traditional (ETL + Data Lake) | Infrastructural (Data-as-Model) |
|---|---|---|
| Data preparation speed | Low (weeks or months for cleaning pipelines) | Instant (data is already structured and described by metadata) |
| API and interface management | Manual creation and maintenance of integration endpoints | Automatic generation of API contracts based on the model |
| Security (Machine Trustworthiness) | Applied on top of data (high risk of leaks) | Embedded at the model level (RLS/ACL acts automatically) |
| Scaling cost (TCO) | High (grows with the volume of new sources) | Low (design-optimization is cheaper than rework) |
In summary, the maturity of an AI-native infrastructure is measured not by the power of its algorithms, but by the readiness of its data. The Data-as-Model architectural approach provides the structural integrity, embedded security, and semantic context necessary for scaling artificial intelligence solutions at the enterprise level.
FAQ
What is the Data-as-Model concept in the context of preparing infrastructure for AI?
It is an architectural approach where data structure, security rules, and logic are described centrally at the level of a single metadata model (Domain metadata). The system uses this model to generate the database and API contracts, immediately providing AI agents with ready-to-use context without additional ETL pipelines.
Why do traditional Data Lakes not solve the problem of data readiness for LLMs?
Data Lakes typically accumulate raw data without metadata or relationships. Using such information for RAG systems requires continuous and expensive post-factum cleaning and vectorization, which significantly increases the total cost of ownership (TCO).
How does the Data-as-Model concept ensure data protection (Machine Trustworthiness)?
Security is embedded directly into the data model using RLS (Row-Level Security) and ACL mechanisms. During a request from an AI agent, the infrastructure layer automatically filters the data, preventing the leakage of confidential information even in the event of prompt injection attacks.