New stability requirements for artificial intelligence systems
In 2026, artificial intelligence has become a critical part of business processes in finance, retail, and manufacturing. The focus of developers is shifting from initial model accuracy to long-term operational stability—the "keepalive" quality. This engineering discipline requires the application of mature development practices: domain-driven design (DDD), CI/CD, automated testing, and monitoring.
Dependency management and architectural principles
A common mistake when implementing AI is ignoring the dependency map. For example, a change in data format in an adjacent CRM system can lead to silent model degradation. To prevent such risks, AI-native architecture must rely on several pillars:
- Domain-specific models: according to Gartner forecasts, by 2028, more than half of enterprise GenAI models will be highly specialized.
- Unified data layer: creating a single point of data access (for example, based on the UnityBase platform by InBase) protects models from changes in heterogeneous sources.
- Integrated security: using specialized AI security platforms for protection at all levels of infrastructure.
Risk management and organizational culture
For critical infrastructure systems, it is recommended to use the NIST AI RMF 1.0 framework, which structures risk management through the Govern, Map, Measure, and Manage functions. It is crucial to consider threats at all stages of the system lifecycle. For example, Prompt Injection attacks (the top threat in the OWASP 2025 ranking) require protection at the entire application level, not just the model itself.
According to the Microsoft Work Trend Index 2026, the success of AI adoption is twice as dependent on organizational culture and team collaboration as on the efforts of individual specialists. Creating cross-functional teams that share responsibility for the solution lifecycle (as implemented by Softengi) is a critical factor for successful AI transformation.
Impact on the industry
Ignoring dependency maps and failing to secure systems at the application level leads to silent model degradation and vulnerability to critical threats like Prompt Injection. Furthermore, companies that rely solely on individual specialists rather than building a collaborative organizational culture face a high risk of failure in their AI transformation efforts.
A practical checklist
- Apply mature engineering practices: Integrate domain-driven design (DDD), CI/CD, automated testing, and continuous monitoring into your AI development lifecycle.
- Unify your data access: Implement a unified data layer (such as the UnityBase platform) to shield AI models from format changes in heterogeneous external sources.
- Adopt risk frameworks: Use the NIST AI RMF 1.0 framework to manage risks through Govern, Map, Measure, and Manage functions, and secure the entire application level against Prompt Injection.
- Foster cross-functional collaboration: Build multidisciplinary teams that share responsibility for the entire solution lifecycle to ensure cultural alignment and project success.
Prepared by a Software Ukraine member. Original publication.