ARTIFICIAL INTELLIGENCE IN PRODUCT ANALYTICS INFORMATION SYSTEMS

Abstract

The author of the article investigated the features of integrating artificial intelligence (AI) and machine learning (ML) technologies into modern information systems for product analytics. The relevance of the topic is due to the transition of digital business from reactive data processing to proactive modelling and design of user experience in real time. The paper compares the analytical contexts of Business-to-Business (B2B) and Software as a Service (SaaS) models, identifies key product metrics (NRR, LTV, Churn, TTV) and evaluates the econometric efficiency of algorithms (XGBoost, LSTM, NLP) in customer churn forecasting tasks.

The scientific novelty of the study lies in the systematization and comparison of architectural solutions of the leading platforms Amplitude, Mixpanel, Heap, PostHog and Hex. A technological shift from simple natural language interfaces to autonomous hierarchical multi-agent systems capable of independently detecting anomalies and formulating product hypotheses is revealed. The role of the new Model Context Protocol (MCP) standard in building enterprise semantic hubs for cross-system data analysis from Stripe, Shopify, Google Ads, and CRM systems is explored.

The methodological risks of "vibe analytics" and "confidence laundering" that arise when delegating analysis to AI models without checking econometric assumptions are critically analysed. The concept of the "Analytic Contract" as a software control barrier is substantiated, as well as the introduction of the Override Rate metric (the proportion of AI decisions corrected by the user) as a key leading indicator of model degradation.


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