AI boosts both customer analytics and operations in omnichannel retail, but research remains fragmented and siloed; the authors propose an integrated decision-intelligence framework to align predictions, optimization and governance for actionable omnichannel decisions.

Main Finding

AI delivers the greatest omnichannel retail value when consumer-facing prediction models and operational optimization models are integrated into a governed decision-intelligence system. Current literature is fragmented — abundant research treats consumer analytics and operations separately — and few studies explicitly connect prediction to realizable operational actions, governance, and valuation. The paper proposes an Integrated Decision Intelligence Framework (data → prediction → optimization → governance/learning) and argues AI should score and select offers/actions by jointly considering relevance, inventory, fulfillment cost/capacity, return risk, margin, customer lifetime value and fairness.

Key Points

• Evidence base: From 1,348 records (2010–June 2026) the author retained 35 peer‑reviewed studies for qualitative synthesis.
• Literature imbalance: 14 studies consumer-analytics focused, 12 operational-efficiency focused, only 9 integrated decision-intelligence studies.
• Common AI applications reviewed: customer segmentation/CLV, recommendation/personalization, sentiment/service analytics, demand forecasting, inventory & fulfillment optimization, and generative-AI assistants.
• Integration gap: Many consumer-AI systems optimize engagement (clicks/conversions) without accounting for inventory, fulfilment costs, return risk, operational capacity, or governance constraints — producing misaligned or unexecutable decisions.
• Framework: Four-layer decision-intelligence architecture
• Omnichannel data integration (customers, products, channels, inventory, fulfilment, returns, contexts)
• Predictive models (conversion, demand, stockouts, returns, churn)
• Optimization/decision models (translate predictions into actions subject to cost, capacity, service, margin, policy, governance)
• Implementation + governance + learning (explainability, audits, human override, feedback loops)
• Methods and technologies observed: machine learning, deep learning (CV/NLP/recsys), simulation & optimization (digital twins), early-stage generative AI for content/assistants.
• Governance concerns emphasized: privacy, consent, explainability, bias mitigation, human-in-the-loop, auditability; generative AI raises hallucination and leakage risks.
• Empirical gaps: lack of longitudinal, multi-channel datasets linking customer outcomes with financial and operational performance; scarce causal evaluations of integrated systems.
• Added contribution: the paper presents decision-scoring/feedback-learning/valuation equations (positioned as tools to operationalize the framework and to value AI investments in omnichannel settings).

Data & Methods

• Review protocol: PRISMA-aligned systematic literature review with explicit search blocks (AI, omnichannel, decisions).
• Databases searched: Scopus, Web of Science, ScienceDirect, Emerald, SpringerLink, Taylor & Francis, IEEE Xplore; citation chasing via Google Scholar.
• Time window: June 2010 – June 2026.
• Screening: removed duplicates → title/abstract screen → full-text eligibility. Inclusion: peer‑reviewed English studies with clear retail-management relevance. Exclusions: commentaries, vendor reports, inaccessible full texts, purely technical algorithm papers unrelated to retail, superficial mentions of AI.
• Final corpus: 35 studies selected from 1,348 initial records. Studies were coded by year, context, method, AI type, consumer vs operational contribution, decision linkage, governance, and limitations.
• Synthesis: deductive coding by research questions and inductive coding for emerging themes; produced thematic matrices and cross-cutting findings.

Implications for AI Economics

• Valuation of AI investments must internalize operational externalities: personalization and promotion systems that raise demand have downstream costs (fulfilment, pick/pack labor, increased returns, lost trust from stockouts) that traditional marketing metrics (CTR, conversion) don’t capture. Economic appraisal should include fulfilment cost curves, return risk, capacity constraints, and long-run customer lifetime effects.
• Optimal allocation/pricing decisions require joint models: next-best-offer, price, and promised service level should be optimized jointly with local inventory and routing capacity. Treating demand predictions as exogenous (separate from design of offers) misprices trade-offs and leads to suboptimal welfare/profit outcomes.
• Measurement & counterfactuals: economics research should prioritize longitudinal, multi-channel panel data linking marketing interventions to operational costs and financial performance. Causal identification (experiments or quasi‑experiments) is needed to estimate true marginal benefits and costs of integrated AI-driven policies.
• Welfare and distributional concerns: personalization and dynamic promises interact with fairness and privacy; economists should quantify welfare implications (consumer surplus, trust/expectation erosion, distributional impacts across customer segments) and incorporate constraints into optimization.
• Market structure & competition: integrated AI capabilities are likely a source of competitive advantage (better matching of promises to fulfillment capacity). This raises questions about market power, investment barriers (data, compute, organizational integration), and regulatory concerns around opaque decisioning.
• Research agenda (economics-oriented):
  • Develop integrated structural models that nest customer response and operational constraints to price and evaluate policies.
  • Estimate the value of information: marginal benefit of improved local inventory visibility, real‑time capacity signals, or better return-risk models.
  • Evaluate externalities of engagement-optimizing algorithms (e.g., generated demand leading to congestion/stockouts) and optimal regulatory/contractual responses.
  • Design and evaluate explainability/ governance mechanisms that alter firm incentives and consumer trust — assess their economic costs and benefits.
  • Quantify the value of embedding fairness/privacy constraints in optimization (trade-offs with profit and service levels).
• Policy and managerial takeaway: firms should move from siloed investments (separate recommender or forecasting upgrades) to investments in integrated decision systems plus governance. Cost–benefit analyses should explicitly model cross‑channel operational impacts; regulators should consider standards for explainability, audit trails, and consumer protection in omnichannel decision systems.