AI-ENHANCED BUSINESS INTELLIGENCE DASHBOARDS FOR PREDICTIVE MARKET STRATEGY IN U.S. ENTERPRISES

Abstract

This study examined whether AI-enhanced business intelligence (BI) dashboards measurably improve predictive market strategy in U.S. enterprises. Building on a systematic evidence scan of 138 peer-reviewed papers (2012–2025) and industry reports covering AI-assisted analytics, managerial decision support, and dashboard adoption, we designed and executed a multi-site quantitative evaluation combining a cluster randomized controlled trial with complementary observational analyses. The AI dashboard integrated model-generated demand forecasts, prescriptive nudges, and natural-language querying; the control condition used feature-parity descriptive BI without predictive or prescriptive components. Primary outcomes focused on forecast accuracy (MAPE and RMSE), with secondary endpoints including decision cycle time, campaign ROI, and monthly revenue growth. Mediation by user adoption (feature-use rate) and moderation by data maturity, firm size, and market volatility were pre-specified. Across participating business units, AI dashboards produced a statistically significant improvement in forecast accuracy (ATE −2.5 percentage points in MAPE), reduced decision cycle time (−18.2 hours), and increased campaign ROI (+13.6 pp) and revenue growth (+1.8 pp). Approximately one-third of the accuracy gain was mediated by adoption intensity, indicating behavioral uptake as a key pathway from capability to performance. Effects were larger in data-mature environments, while size and volatility showed limited moderating influence after multiplicity control. Rolling-origin back tests and Diebold–Mariano tests confirmed predictive uplift versus baseline models, and calibration diagnostics indicated reliable uncertainty communication. Sensitivity analyses (Did, quantile treatment effects, and per-protocol) supported robustness. Taken together, findings from both the empirical trial and the 138-paper evidence base suggest that AI-enhanced dashboards yield operationally meaningful gains with modest latency/complexity costs, translating into more accurate forecasts, faster decisions, and improved commercial outcomes. The study provides implementation guidance for enterprise rollout, emphasizing standardized onboarding, telemetry-informed adoption support, and governance practices to sustain performance.