Artificial intelligence is evolving into an autonomous agent capable of learning and improving with minimal human intervention. While companies are actively integrating AI solutions into their operations today, the next stage of transformation is already on the horizon. The concept of Recursive Self-Improvement (RSI), being researched by Nectain (an R&D company within the alliance specializing in AI/ML), posits that AI systems will be able to enhance their own algorithms and architecture. This ushers in a new era of automation but simultaneously introduces new risks. Let’s examine how to prepare businesses for this transformation, focusing on data readiness and proactive risk management.
The challenge of AI self-improvement: what is RSI and why it matters for business
Recursive Self-Improvement (RSI) in artificial intelligence is a hypothetical, yet increasingly realistic, scenario where an AI system achieves the ability to autonomously enhance its own intellectual capabilities. This means an AI can not only perform tasks but also modify its internal logic, architecture, and even objectives to become more effective. For businesses, this presents both opportunities for automation and significant risks related to unpredictability and control.
In practice, we are already observing AI agents taking on more cognitive work. According to Microsoft’s 2026 Work Trend Index Annual Report, 49% of conversations with Microsoft 365 Copilot supported cognitive work, including analysis, decision-making, evaluation, and creative thinking. This indicates that AI is already involved in processes previously considered exclusively human prerogatives. As these systems begin to improve themselves, their impact on business processes will become exponential. This requires companies not just to implement AI, but to deeply rethink their strategies for management, security, and accountability.
Data readiness: the foundation for AI analytics and security
The success of any AI initiative, especially in the context of RSI, hinges on data quality and readiness. Data fragmentation, inconsistency, and low quality are primary obstacles to the effective deployment of AI solutions. If AI is trained on poor-quality data, its self-improvement could lead to the amplification of biases, errors, or even the creation of new vulnerabilities.
To prepare data, it is necessary to:
- Centralization and unification: Consolidate data from various sources into a single repository (e.g., a Data Lake or Data Warehouse) and standardize it.
- Cleansing and validation: Remove duplicates, correct errors, fill in missing values, and ensure data integrity.
- Master Data Management (MDM): Establish a single, authoritative source for key business entities (e.g., customer profiles, products, suppliers). Platforms like UnityBase (an open-source low-code platform developed by InBase) serve as a foundation for building such systems, facilitating data integration and management.
- Security and privacy: Ensure data compliance with regulatory requirements (GDPR, NIS2) and protect it from unauthorized access.
Without these steps, AI self-improvement will only exacerbate existing problems rather than solve them.
A common pitfall: evaluating AI solely on technical accuracy
A prevalent mistake in implementing AI solutions is focusing exclusively on technical metrics like model accuracy. While these indicators are important, they do not reflect true business value. An AI project is considered successful when it delivers tangible business benefits—reducing costs, increasing revenue, improving service quality, or accelerating processes.
Organizational factors, such as culture and management support, explain twice the impact of AI compared to individual efforts, according to Microsoft’s 2026 Work Trend Index Annual Report. This underscores that success depends not only on technology but also on a company’s ability to integrate AI into its operational model and measure its impact on strategic business objectives.
Instead of accuracy, business-oriented metrics should be used:
- ROI: Return on investment from the AI solution.
- Reduced processing time: For example, the time taken to process requests or documents.
- Fewer errors: Particularly crucial in the financial or public sector.
- Improved customer satisfaction: Through faster and higher-quality service.
These are the metrics that allow for an objective assessment of AI effectiveness and justify investments.
An operational scenario: automating citizen request processing in the public sector
Let’s consider a typical scenario in the public sector where automation and AI can significantly enhance efficiency. A government agency receives thousands of citizen requests daily through various channels: email, web forms, postal mail. Manual processing of these requests is slow, error-prone, and resource-intensive.
In practice, this looks like:
- Collection and classification: An electronic document management system, such as Scriptum (a low-code BPM platform on UnityBase by InBase) or Megapolis.DocNet (an ECM system by InBase), automatically collects requests from all channels. An AI model (e.g., developed by Softengi) analyzes the request text, classifies it by topic (social benefits, utilities, permits), and determines its priority.
- Data extraction: The AI extracts key information from the request: applicant’s full name, contact details, the essence of the problem, and required documents.
- Routing and processing: Based on the classification and extracted data, the request is automatically routed to the appropriate department or specialist. The AI can suggest a template response or even generate a draft using previous decisions and regulatory acts.
- Monitoring and analytics: The system monitors the status of request processing, identifies bottlenecks, and provides analytics for process improvement. In the context of RSI, the AI can independently analyze the effectiveness of its classification and routing, suggesting algorithmic changes for further acceleration and accuracy enhancement.
This approach not only speeds up processing but also ensures consistency, reduces the human factor, and increases transparency.
AI risk management: from NIST to OWASP
With the growing autonomy of AI, risk management becomes a priority. Frameworks like the NIST AI Risk Management Framework (AI RMF 1.0) provide a structure for identifying, analyzing, and mitigating AI-related risks. NIST AI RMF 1.0 organizes AI risk management around the functions of Govern, Map, Measure, and Manage. This allows companies to take a systematic approach to the security, reliability, and accountability of AI systems.
Particular attention should be paid to the specific vulnerabilities of large language models (LLMs) and generative AI. Prompt Injection, identified as LLM01:2025 in the OWASP Top 10 for LLM/GenAI Applications, is one of the most pressing risks. This attack allows an attacker to manipulate AI behavior by inserting malicious instructions into the input data. In the context of self-improvement, such an attack could lead to unpredictable changes in the AI’s logic with significant consequences.
Other risks include:
- AI hallucinations: Generation of false or fabricated information.
- Bias: Replication and amplification of biases present in the training data.
- Data leakage: Unintentional disclosure of sensitive information.
- Lack of transparency (explainability): Difficulty in understanding why an AI made a particular decision.
Managing these risks requires not only technical solutions but also clear policies, monitoring procedures, and human-in-the-loop mechanisms for critical decisions.
This year and in the coming ones, according to Gartner’s forecast, over half of the GenAI models used by enterprises will be domain-specific. This means companies will develop or adapt AI for very specific tasks, requiring even more rigorous risk management and data security assurance within narrow domains.
Company readiness checklist for the era of AI self-improvement
- Has an internal data owner and an AI Governance responsible person been identified?
- Is there a data quality management policy in place that covers cleansing, standardization, and master data management?
- Has a risk assessment been conducted for AI implementation (including prompt injection, hallucinations, bias)?
- Have business-oriented success metrics for AI solutions been defined (ROI, time reduction, error reduction), rather than just technical ones (accuracy)?
- Have procedures for monitoring and responding to AI model drift been developed?
- Are human-in-the-loop mechanisms in place for critical decisions made by AI?
- Has staff training been conducted on working with AI systems and managing associated risks?