Enterprise revenue hinges on integrated ERP, pricing algorithms and aligned data—but scholarship is thin on execution and governance; robust, micro‑level and longitudinal research is urgently needed to measure AI-driven revenue effects and ensure accountable algorithmic decisions.

Main Finding

Intelligent-enterprise performance — defined as the ability to make revenue-oriented decisions through integrated transactional systems, analytics and AI — hinges on process visibility (ERP), pricing intelligence, coordinated enterprise architecture, enterprise-wide data alignment, and AI capability. However, the literature from 2015–2023 is thin on treating revenue as an end-to-end enterprise system, lacks longitudinal and execution-level evidence, and does not adequately connect pricing models to operational execution or to governance for accountable algorithmic revenue decisions. The article proposes a framework and research priorities to fill these gaps.

Key Points

• Core constituents of an “intelligent enterprise”: transactional systems/operational backbones, adaptive analytical services, enterprise architecture, data governance, and AI capability.
• Performance drivers identified in the literature: ERP-enabled process visibility, pricing intelligence (algorithmic/dynamic pricing), coordinated enterprise architecture, and aligned data across functions.
• Major gaps: scarce scholarship explicitly unifying revenue and systems integration; limited longitudinal/empirical studies; weak links between pricing models and operational execution (e.g., order fulfillment, billing, customer lifecycle); underdeveloped governance/ accountability models for algorithmic revenue decisions.
• The article recommends a technically grounded, revenue-focused research agenda to advance both theory and practice.

Data & Methods

• Method: peer-reviewed literature review of journal articles published between 2015 and 2023 focused on unified revenue and systems integration topics.
• Scope/limitations: the compound topic is sparsely represented in journals, so the review synthesizes a relatively small and heterogeneous set of studies; evidence is largely cross-sectional/conceptual rather than longitudinal or experimental.
• Output: synthesis of recurring themes, identification of gaps, and proposal of a framework and prioritized research questions (technical and organizational) tied to revenue-oriented intelligent enterprise design.

Implications for AI Economics

• Empirical agenda: need micro‑level, longitudinal studies linking AI-driven pricing and recommendation algorithms to realized revenue outcomes across the transaction pipeline (order capture → fulfillment → billing → retention). Causal identification strategies will be vital to assess net revenue impact and welfare effects.
• Model integration: economic models of pricing and market behavior should incorporate operational constraints and execution frictions (ERP/fulfillment delays, inventory, billing accuracy) to predict realized revenues under algorithmic strategies.
• Measurement & data requirements: researchers will need integrated enterprise datasets (transaction logs, ERP events, pricing rule history, customer interactions) and standards for data governance to enable reproducible economics analyses.
• Governance & accountability: algorithmic revenue decisions raise questions of accountability, fairness, and regulatory compliance. AI economics should study institutional and technical governance mechanisms (audit trails, explainability, incentive alignment) and their effects on firm behavior and market outcomes.
• Policy and competition: integrating AI-driven revenue systems can alter market power dynamics (dynamic pricing, targeted offers). Economists should evaluate social welfare, consumer surplus, and competitive implications of enterprise-level adoption of intelligent revenue systems.