86% of Large Russian Companies Use or Pilot LLMs While Autonomous AI Agents Remain Rare in Production
Infosystems Jet and Smart Ranking have published findings on AI maturity among Russia’s largest enterprises. The study shows that 86% of major Russian companies already use or pilot large language models, yet fully autonomous AI agents have reached industrial deployment in only 8% of organizations.
The research is based on a survey of 52 large Russian companies collectively employing about 450,000 people, supplemented by seven in-depth interviews and desk analysis. Respondents included CIOs, CTOs, and digital transformation leaders.
While 53% of organizations running generative AI have already placed solutions into production, uptake declines with increasing autonomy. Semi-autonomous agents exist in 59% of companies, fully autonomous agents in 25%, and multi-agent systems in 23%. In production environments these figures fall to 15%, 8%, and 8% respectively.
More than 60% of firms are actively exploring autonomous and multi-agent technologies. However, 44% cite insufficient process maturity, data infrastructure, and integration challenges as reasons for halting agent projects. Maxim Andrianov, head of software development and implementation at Infosystems Jet, noted that agent value depends directly on data quality, process digitization, and corporate system integrations.
Cost remains a major constraint: 58% of respondents named server expenses as a barrier. Legal risks were cited by 56%, limited investment budgets by 42%, and specialist shortages by 40%. In 17% of companies the absence of dedicated AI leadership hinders progress.
Even after pilots launch, economic results are elusive. 46% see no sustainable financial impact, while 40% struggle to scale due to lack of product or process ownership. Another 40% report unstable output quality, including hallucinations and other failures.
Almost half the companies plan to increase AI spending, yet near-term priorities will favor projects with clear economics and controlled risks. Success will require not only stronger models but also high-quality data, digitized processes, robust integrations, and governance mechanisms.
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