The Impact of AI Automation on Reducing Operating Costs and Improving Decision-Making Accuracy in Enterprise Platforms

Kodirov Shokhrukh

Open Access

The Impact of AI Automation on Reducing Operating Costs and Improving Decision-Making Accuracy in Enterprise Platforms

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Kodirov Shokhrukh ¹ ,

⁴ Senior Full-Stack Developer South Korea

Abstract

The article examines the cumulative impact of artificial intelligence technologies on the operational performance and strategic resilience of corporate systems operating under the conditions of large-scale digital transformation in 2024–2025. The scientific and practical relevance of the topic stems from the fact that, over a remarkably short period, the business environment moved from localized experiments with generative models to their systemic incorporation into the corporate governance perimeter, including the use of autonomous AI agents integrated into microservice architectures and distributed environments for parallel data processing. The methodological foundation of the study is built on a systemic analysis of industry reviews produced by leading consulting organizations, IEEE and ACM scholarly publications, as well as applied case studies related to the development of high-load platforms in the medical and advertising sectors. The findings show that AI-driven automation can reduce the cost of digital content verification by as much as 98%, accelerate analytical procedures many times over-up to three hundredfold in some cases and raise the accuracy of financial forecasting to a range of 91–95%. Particular attention is given to substantiating the concept of “decision-making speed” as one of the central parameters of contemporary competitiveness, emerging within hybrid models of cooperation between humans and algorithmic systems. On this basis, the article argues for the necessity of transitioning toward AI-native architectures capable of ensuring business scalability while simultaneously reducing the dependence of critical processes on the human factor. The propositions presented possess practical value for systems architects, chief digital transformation officers, and heads of technology divisions in large corporate structures.

Keywords

artificial intelligence, corporate platforms, operating costs, decision-making, agentic AI, SaaS architecture, business process automation, digital transformation, parallel data processing, forecasting accuracy

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International Research Journal of Advanced Engineering and Technology

The Impact of AI Automation on Reducing Operating Costs and Improving Decision-Making Accuracy in Enterprise Platforms

Abstract

Keywords

References

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