Salesxplore-En — TechCom

Corporate AI requirements: the necessity of domain-specific language models

Thu, 18 Jun 2026 06:07:17 +0300

The integration of artificial intelligence into corporate processes is no longer a future prospect but a practical reality. However, when it comes to critical data, specific business processes, and stringent security requirements, general large language models (LLMs) often prove insufficient. This year and in the coming ones, a clear trend is observed: enterprises are increasingly turning to domain-specific language models (DSLM) that enable greater accuracy, control, and compliance with regulatory norms.

The 2026 landscape for AI-augmented FinOps and multi-cloud cost control

Fri, 22 May 2026 06:07:28 +0300

By 2026, over 80% of large enterprises utilizing multi-cloud infrastructures will integrate artificial intelligence into their FinOps strategies to optimize costs and enhance efficiency. This shift is driven by the increasing complexity of cloud environments, dynamic provider pricing models, and the necessity for automated analysis of vast amounts of resource consumption data.

Evolution of FinOps in multi-cloud environments

Traditional FinOps approaches, based on manual report analysis and static rules, are becoming ineffective in multi-cloud settings. The complexity lies not only in managing different pricing models across Azure, AWS, and GCP but also in tracking and forecasting costs for dynamic resources such as serverless functions, containers, and specialized AI/ML services. Companies face challenges with insufficient cost visibility, suboptimal resource utilization, and difficulties in allocating budgets across various business units.

Improving system integration quality through AI-based data governance

Thu, 14 May 2026 06:07:16 +0300

By 2026, over 70% of successful digital transformation initiatives will rely on an effective Data Governance strategy integrated with artificial intelligence capabilities. This will enable addressing data quality issues critical for system integration. The increasing complexity of enterprise IT landscapes, encompassing on-premises systems, cloud solutions, and hybrid infrastructures, necessitates a new approach to data management where AI becomes not just a tool, but a central element of the strategy.

Streamlining ISO/IEC 27001 audit processes with AI by 2026

Tue, 12 May 2026 06:08:06 +0300

According to forecasts, by 2026, the volume of data requiring analysis to confirm compliance with cybersecurity standards will increase by 40% compared to the current year. This creates a significant burden on internal teams and external auditors, especially in the context of regular ISO/IEC 27001 compliance checks. The adoption of AI-driven tools is becoming not just an advantage, but a necessity for effective compliance management.

Challenges of Traditional ISO/IEC 27001 Audits

Traditional ISO/IEC 27001 audits are labor-intensive processes that require manual collection, analysis, and verification of vast amounts of information. Key challenges include:

Current developments in multi-cloud and cloud infrastructure

Mon, 11 May 2026 06:06:53 +0300

According to the latest analytical reports, over 85% of enterprises are already using or planning to adopt multi-cloud strategies within the next two years. This reflects a fundamental shift from monolithic on-premises infrastructures to more flexible, scalable, and resilient architectures. The benefits of multi-cloud are evident: avoiding vendor lock-in, optimizing costs by selecting the best offerings for specific workloads, and improving fault tolerance and geographic availability.

Hybrid infrastructure, combining on-premises resources with public clouds, remains a key element of the strategy for many large organizations, especially in sectors with stringent regulatory requirements or significant investments in their own data centers. This allows for maintaining control over critical data and applications while leveraging the scalability and innovation of cloud services.

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.

Integrating RPA and low-code for advanced BPM automation by 2026

Fri, 30 Jan 2026 12:39:06 +0200

By 2026, over 80% of large enterprises will utilize a combination of Robotic Process Automation (RPA) and low-code platforms to optimize business processes. This Gartner forecast highlights the growing need for tools that not only automate routine operations but also allow for rapid adaptation of business logic to changing conditions. Complex Business Process Management (BPM) processes, spanning multiple departments, integrating with diverse systems, and involving dynamic execution conditions, demand a flexible and efficient approach that merges robotic capabilities with rapid application development.

Cybersecurity for AI: The emergence of DLP standards by 2026

Tue, 27 Jan 2026 09:52:55 +0200

Rising Risks of Data Leaks in AI Systems

By 2026, over 70% of new AI systems will experience data breaches due to inadequate Data Loss Prevention (DLP) solution integration. Artificial intelligence, integrated into corporate processes, handles vast amounts of sensitive information—from customer personal data to trade secrets and intellectual property. This data is both the fuel for AI and its most vulnerable point. Traditional DLP systems, designed to protect structured data in conventional enterprise systems, often prove ineffective against complex attack vectors targeting AI models and their training datasets.

Low-code platforms enhanced by AI to boost BPM efficiency by 2026

Fri, 23 Jan 2026 12:21:11 +0200

By 2026, over 75% of new enterprise applications are projected to be developed using low-code or no-code platforms. This trend addresses the growing need for rapid business process digitalization and adaptation to dynamic market conditions. For Business Process Management (BPM) systems, this signifies revolutionary changes, especially with the integration of artificial intelligence capabilities.

Evolution of BPM systems: from manual configuration to AI optimization

Traditional BPM system development often requires significant resources, lengthy development cycles, and deep technical expertise. Low-code platforms have already simplified this process, enabling business analysts and developers to create functional solutions with minimal coding. However, AI integration elevates this paradigm to a new level. AI-driven low-code platforms can automatically generate code snippets, suggest optimal process pathways, identify bottlenecks, and even predict system behavior based on historical data.