Domain-specific AI models for enterprise applications

In 2026, the corporate environment is undergoing a significant transformation, where artificial intelligence is moving from experimental projects to the core of new solution architectures. The 2026-2027 horizon is defining, as large-scale AI integration into business processes is occurring now, demanding a shift from general models to highly specialized ones. According to Gartner, by 2028, over half of the GenAI models enterprises will use will be domain-specific, underscoring the relevance of this direction today. This trend is driven by the need for deeper automation of cognitive work and decision-making, as well as the necessity to manage AI risks beyond standard approaches.

Evolution of AI: from general models to domain-specific efficiency

Artificial intelligence is no longer just a tool for automating routine tasks. Today, it is actively used to support cognitive work: analysis, decision-making, evaluation, and creative thinking. This is confirmed by a Microsoft study, which in its report ‘2026 Work Trend Index Annual Report’ notes that out of over 100,000 Microsoft 365 Copilot chats analyzed, 49% of conversations supported cognitive work. However, general AI models often cannot provide the necessary accuracy and depth of understanding of specific business processes and data, which is critical for enterprise applications.

Therefore, the focus is shifting to development where AI is not just an add-on module but the foundation of architecture and functionality. The cornerstone of this approach is the creation of domain-specific AI models. These models are trained on highly specialized data, understand industry terminology and logic, allowing them to deliver significantly more accurate and relevant results. They can effectively work with unique datasets, regulatory requirements, and operational nuances of a specific business, whether it’s finance, logistics, or manufacturing.

Legacy system challenges: how ERP becomes a bottleneck for business innovation

Many large corporations, especially in the financial sector, continue to rely on legacy ERP systems developed decades ago. These monolithic architectures, while robust, have become a significant bottleneck for business innovation. Changes in such systems often take 3-6 months, which is unacceptably long in today’s market demanding rapid time-to-market for new products and services. This leads to the emergence of ‘shadow IT,’ where departments create their own solutions bypassing centralized systems, posing additional security risks and data fragmentation.

Problems with legacy systems include:

• Low flexibility: Difficulty adapting to new business requirements and technologies.
• High maintenance costs: Outdated technologies and a lack of qualified specialists.
• Data fragmentation: Customer or business process information is often scattered across different modules, making it impossible to create a unified profile.
• Limited integration capabilities: Difficulty integrating with modern AI solutions and other external services due to outdated or non-existent APIs.

Common mistake: complete replacement of legacy systems instead of phased modernization

A common mistake among IT department heads is attempting to completely replace a legacy ERP system with one large project. This approach is risky, expensive, and time-consuming, often taking years and requiring the shutdown of critical business processes. This can lead to operational disruptions, budget overruns, and low user adoption of the new system.

Instead, a phased modernization strategy is more effective. It involves identifying the most critical and valuable components of the legacy system that can be preserved, and gradually replacing or supplementing less efficient parts with modern microservice architectures and domain-specific AI models. This approach allows the business to continue functioning without interruption, achieving quick wins and gradually transforming its IT infrastructure.

For example, UnityBase (an open-source low-code platform developed by InBase) allows for the design of architectural solutions that integrate new functional blocks and AI services while preserving valuable data and logic from existing systems. This enables the creation of hybrid architectures where legacy systems coexist with modern components, ensuring flexibility and scalability.

Architectural example: integrating domain-specific AI models in the banking sector

Consider a typical scenario in the banking sector. Banks often have fragmented customer profiles: loan data in one system, deposits in another, transaction history in a third. This complicates personalization of offers and rapid response to customer needs, as well as compliance with regulatory requirements for AML/KYC.

Integrating domain-specific AI models can solve this problem as follows:

1. Unified customer profile: An AI model trained on banking data aggregates information from various legacy systems (CRM, ERP, lending systems, transactional systems) to create a unified, dynamic customer profile. This model can identify hidden connections and behavioral patterns.

2. Personalized offers: Based on the unified profile, the AI model can generate hyper-personalized banking products and services, proactively offering them to the customer through various channels. This increases loyalty and revenue.

3. Compliance automation: Domain-specific models can automate compliance checks with regulatory requirements (e.g., detecting suspicious transactions for AML). This reduces operational costs and the risk of fines.

4. Risk optimization: AI models for credit risk assessment, trained on the bank’s historical data, can provide more accurate forecasts than traditional scoring systems, accelerating the loan issuance process.

Softengi, with its experience in developing AI solutions for the financial sector, can act as a partner in creating such domain-specific models, integrating them into the bank’s existing architecture. The bidXplore, salesXplore, and solveXplore products (AI у data analysis and process optimization tools) developed by Softengi demonstrate the potential of AI for optimizing business processes across various industries.

AI risk management: security, reliability, and accountability in the corporate environment

The implementation of AI, especially domain-specific models in critical infrastructure, carries significant risks that must be effectively managed. This is not just a matter of data security, but a comprehensive approach covering model reliability, accountability, and resilience to attacks.

Key aspects of AI risk management:

• AI security: Protection against Prompt Injection, the foremost risk in the OWASP LLM Top 10 2025 (LLM01:2025), as well as other types of attacks such as data poisoning or model evasion. AI security is evolving from a focus solely on the model to a comprehensive approach encompassing data, infrastructure, and integrations.

• Reliability and resilience: Ensuring stable and predictable operation of AI models, their ability to withstand unexpected input data, and maintain accuracy in real-world conditions. MITRE ATLAS structures adversarial AI behavior into tactics and techniques, which is useful for threat modeling, AI red teaming, and building detection controls.

• Accountability and transparency: The ability to explain how an AI model arrived at a particular decision, especially in critical scenarios such as loan issuance or medical diagnostics. This is important for regulatory compliance and customer trust.

NIST AI RMF 1.0 structures AI risk management around the functions Govern, Map, Measure, and Manage, providing a framework for creating an effective AI risk management system in the corporate environment.

Implementation strategy: creating domain-specific AI solutions for enterprise applications

Effective implementation of domain-specific AI models requires a clear strategy and a phased approach. This is not a one-time project but a continuous process of integration and optimization.

Readiness assessment checklist for implementing domain-specific AI models

• Key business processes requiring AI optimization have been identified.
• An assessment of the availability and quality of data for training domain-specific models has been conducted.
• The existing IT infrastructure has been analyzed for compatibility with AI solutions.
• A strategy for AI risk management (security, privacy, accountability) has been developed.
• A team with the necessary competencies for developing and supporting AI systems has been formed.
• Success metrics and KPIs for evaluating AI implementation effectiveness have been defined.

This approach allows enterprises to modernize their enterprise applications without business interruption, accelerating the launch of new features and ensuring competitive advantages in 2026-2027.