Erp-Vuzke-Mistse-En — TechCom

From code development to AI system administration for enterprise engineers

Mon, 22 Jun 2026 06:11:06 +0300

The role of the enterprise developer is undergoing fundamental changes this year and in the coming ones. There is a shift from a paradigm where the developer primarily writes code to managing complex AI systems. This change requires new skills, tools, and approaches to security, especially in critical infrastructure such as banking and financial institutions.

AI in development: a new landscape for enterprise development

AI in development is not about integrating AI models into existing applications, but a fundamental change in the approach to software creation. In this architecture, AI is not an auxiliary tool but a central component involved in cognitive work that was previously the prerogative of humans: analysis, decision-making, and evaluation.

Maintaining continuity during phased legacy ERP system migrations

Fri, 29 May 2026 23:18:39 +0300

Legacy ERP systems in the public sector have become bottlenecks for development and adaptation to modern requirements. Any change, integration with new government services, or functional update takes 3–6 months and often requires downtime. This leads to departments creating their own "shadow" IT solutions to bypass limitations, and the launch of new electronic services or adaptation to legislative changes occurs significantly slower than among competitors.

As an integration solutions architect with 15 years of experience, I believe that instead of trying to revive a monolith or replace it with a single "big bang," the path to successful digital transformation lies in phased decomposition of legacy ERP. This means extracting individual functions into microservices with API access, allowing for system modernization without business interruption and with minimal risks.

Deploying domain-specific AI models in enterprise environments

Mon, 23 Mar 2026 16:52:43 +0200

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.

Banking efficiency and cost optimization strategies in multi-cloud environments

Thu, 26 Feb 2026 17:46:17 +0200

IT cloud spending is constantly increasing, posing one of the most pressing challenges for financial institutions today. After migrating to cloud environments, particularly in multi-cloud models, many banks face unpredictable spikes in their monthly bills. This occurs due to several factors: from orphaned resources (forgotten virtual machines or databases that continue to incur costs) to overprovisioning of Kubernetes clusters, which are reserved with excess capacity and not fully utilized. Dormant testing and development environments, a lack of strategy for using reserved instances (discounted capacity) and spot instances (temporary, cheaper capacity) only exacerbate the problem. Consequently, cloud bills grow, but transparency regarding exactly where the money is going is lacking, complicating budget planning and control.