Core Fact: Enterprises are rapidly shifting from general-purpose LLMs to domain-specific AI models (DSLMs) to address critical gaps in data security, regulatory compliance, and accuracy.
Why general LLMs fall short for enterprise needs
General-purpose language models have limitations when used in business. Key challenges include data security (leakage risks under OWASP 2025), "hallucinations" and inaccuracies in critical sectors (finance, healthcare), a lack of understanding of internal company context, and regulatory constraints (GDPR, HIPAA, and NIST AI RMF 1.0 requirements).
What this means for the market
According to Gartner, by 2028, more than half of enterprise GenAI models will be domain-specific (DSLM). For the industry, this shift means a decline in reliance on public cloud LLMs and a surge in demand for localized, compliant, and highly accurate proprietary architectures that respect strict privacy controls.
Practical steps
To successfully transition to domain-specific models, enterprises should adopt an iterative data preparation approach and follow structured implementation steps:
An efficient approach to data preparation
Avoid attempting to clean all corporate data at once. Instead, prepare data for specific high-priority projects. Use specialized data management tools, including low-code platforms, to create a single point of access to information.
AI solution architecture: A banking use case
To automate request processing, deploy specialized solutions according to this framework:
- Collecting, cleaning, and anonymizing data from internal systems.
- Fine-tuning a proprietary model on curated banking terminology.
- Integrating the model with existing systems (CRM, scoring) via APIs.
- Monitoring performance to prevent errors and ensure auditability.
Checklist for choosing a DSLM
Implement a proprietary model if your company requires strict privacy controls, regulatory compliance, high accuracy in specialized terminology, and complete control over algorithm training.
Prepared by a Software Ukraine member. Original publication.