MODERN ARTIFICIAL INTELLIGENCE PLATFORMS IN BUSINESS
TL;DR
The proposed classification and the identified success factors serve as a practical guide for selecting AI platforms that align with the specific needs and capabilities of an organization, helping reduce investment risks, avoid costly mistakes, and enhance the likelihood of successful scaling.
Abstract
The article is devoted to the study of modern artificial intelligence platforms for business, with the aim of developing a structured classification of available tools and identifying the key factors that determine the success or failure of their implementation in corporate environments. The relevance of this topic stems from the growing gap between the widespread strategic commitment to AI and the limited practical results achieved by most organizations. Despite significant investments and high expectations, a large proportion of AI projects remain confined to pilot stages, failing to deliver tangible business value at scale. This disconnect highlights the absence of systematic approaches to navigating the fragmented landscape of AI solutions, which complicates decision-making and increases the risk of suboptimal technology choices. To address these challenges, the research employs a comprehensive methodological framework that integrates systemic analysis with comparative and classification methods. The study is based on a broad synthesis of empirical data, including global surveys of business leaders and IT professionals, as well as analytical assessments of platform capabilities. The classification criteria are defined around functional purpose, architectural flexibility, integration potential, and alignment with organizational maturity levels. This multidimensional approach enables a holistic view of the market and provides a foundation for identifying recurring patterns in both successful and failed implementations. The results of the study include a structured typology that distinguishes four major categories of AI platforms. The research also identifies critical success factors, including data integration quality, workforce competencies, governance frameworks, and measurable ROI systems, while simultaneously highlighting the phenomenon of AI regression, where organizations abandon previously adopted AI solutions due to unmet expectations, underscoring the importance of realistic planning and organizational readiness. The practical value of the research lies in its direct applicability to strategic and operational decision-making in technology management. The proposed classification and the identified success factors serve as a practical guide for selecting AI platforms that align with the specific needs and capabilities of an organization, helping reduce investment risks, avoid costly mistakes, and enhance the likelihood of successful scaling.