The concept of recursive self-improvement (RSI) refers to the ability of AI systems to autonomously upgrade their own architecture and algorithms. Research by Nectain shows that this stage of automation requires businesses to focus on meticulous data preparation and proactive risk management. According to Microsoft, 49% of interactions with Copilot already involve cognitive work, confirming that systems are ready for more complex tasks.
What this means for the market
The transition to autonomous AI self-improvement means businesses can no longer rely solely on technical model accuracy. With Gartner predicting that over half of enterprise GenAI models will be domain-specific, companies face heightened security risks, including prompt injection, hallucinations, and data leaks. Success will depend heavily on organizational culture, management support, and robust data governance rather than just technical implementation.
The foundation of security: Data preparation and centralization
The quality of AI performance directly depends on data preparation. To minimize errors, companies must implement data centralization in unified repositories, deduplication, and master data management (MDM). For instance, the UnityBase low-code platform enables efficient integration and administration of corporate datasets in compliance with GDPR and NIS2 requirements.
When evaluating AI projects, it is crucial to focus on business metrics (ROI, processing speed, error reduction) rather than just the technical accuracy of models. Organizational culture and management support have twice the impact on implementation success compared to purely technical factors.
Practical case study: Automation in the public sector
The automation of citizen inquiry processing demonstrates the efficiency of integrating AI into operational workflows. Using the Scriptum BPM platform or the Megapolis.DocNet system, inquiries are automatically collected and classified. AI models from Softengi analyze texts, extract key data, route requests, and suggest response templates. Within the RSI framework, the system can autonomously analyze its performance accuracy and optimize its algorithms.
Risk management and model security
The growing autonomy of AI requires the adoption of security frameworks such as the NIST AI Risk Management Framework and OWASP standards. Key threats include prompt injection (manipulating model behavior through inputs), hallucinations, bias, and sensitive data leaks.
What to do next
To prepare your business for the era of autonomous AI, implement the following practical steps:
- Centralize corporate datasets in unified repositories and ensure compliance using platforms like UnityBase.
- Adopt security frameworks such as the NIST AI Risk Management Framework and OWASP standards to mitigate vulnerabilities.
- Appoint roles responsible for AI governance and data quality.
- Define business-oriented success metrics (ROI, processing speed) instead of purely technical ones.
- Implement human-in-the-loop control mechanisms to monitor autonomous optimization.
Prepared by a Software Ukraine member. Original publication.