AI-powered strategic information systems improve firms' decision speed, customer engagement and supply-chain performance, but gains rely on leadership, infrastructure and governance; ethical, skill and cost barriers limit straightforward productivity wins.

Main Finding

AI-aided Strategic Information System (SIS) tools (BI, DSS, AI-powered CRM, predictive analytics, ML, Generative AI/LLMs) materially strengthen firm competitiveness by enabling faster, more accurate data-driven decisions, improving customer intelligence and engagement, optimizing supply chains, and increasing strategic agility. Successful performance gains depend on organizational readiness, leadership commitment, technological infrastructure, and governance; adoption faces persistent ethical, privacy, bias, skill, and cost barriers. (Systematic review of 22 studies, 2017–2026).

Key Points

• Core benefits

  • Data-driven decision-making: BI and DSS improve managerial decision speed and accuracy.
  • Customer outcomes: AI-powered CRM and predictive analytics raise marketing effectiveness, personalization, and customer engagement.
  • Operations and supply chains: ML and predictive tools enhance forecasting and operational efficiency.
  • Strategic intelligence: Generative AI and LLMs are emerging as scalable tools for market intelligence and insight generation.
  • Strategic agility: AI-enabled SIS support adaptive responses to environmental turbulence and innovation capability.

• Determinants of successful adoption

  • Organizational readiness, top-management commitment, strategic alignment, and digital skills.
  • Technological infrastructure and governance frameworks (ethics, transparency, accountability).

• Risks and challenges

  • Data privacy, algorithmic bias, ethical risks, lack of transparency, workforce skill gaps, organizational resistance, and high implementation/maintenance costs.

• Literature scope and gaps

  • Existing studies are fragmented across industries and technologies; this review synthesizes cross-cutting patterns but relies on heterogeneous studies (n = 22).
  • Recent rise of Generative AI/LLMs is not yet fully explored in empirical, causal terms.

Data & Methods

• Review framework: PRISMA-guided systematic review.
• Timeframe: studies published 2017–2026, English language.
• Databases searched: Scopus, ScienceDirect, Google Scholar (additional searches in SpringerLink, Emerald, IEEE Xplore, ResearchGate, institutional reports).
• Search strategy: Broad AI + SIS + competitive position search strings (examples provided for Scopus), targeted queries in ScienceDirect and Google Scholar.
• Screening and selection: Initial yield ~1,425 records (58 from Scopus, 1,279 ScienceDirect, 88 Google Scholar); after deduplication and eligibility screening, 22 studies included.
• Study types: Mix of empirical and conceptual papers; thematic analysis used to extract patterns, opportunities, and challenges.
• Limitations of method: Small final sample, English-only, selection limited to specific databases and article types, qualitative/thematic synthesis (no meta-analysis or causal identification).

Implications for AI Economics

• Productivity and firm performance

  • AI-enabled SIS are channels for productivity improvement at the firm level (shorter decision cycles, better targeting, reduced forecasting errors). These gains likely raise value-added and profits for adopters.
  • Complementarities: Returns to AI investment depend on complementary investments in human capital, organizational processes, and IT infrastructure—implying heterogeneous returns across firms.

• Market structure and competition

  • First-mover advantages: Firms that integrate AI-SIS effectively can obtain persistent competitive advantages via superior customer intelligence, faster iteration, and optimized operations, potentially increasing market concentration in some sectors.
  • Entry barriers: High implementation and governance costs plus data access advantages can raise barriers to entry, amplifying incumbent advantages.

• Labor markets and skills

  • Demand shift: AI-SIS raise demand for digitally skilled workers (data scientists, analysts) while automating routine decision tasks—implying skill-biased labor demand and potential wage polarization.
  • Complementarity vs. substitution: Productivity effects depend on whether AI augments or substitutes human labor; policy should focus on re-skilling and transitions.

• Investment, adoption dynamics, and diffusion

  • Determinants identified (leadership, readiness, governance) map to adoption models in economics; policy and firm strategy can accelerate diffusion by lowering coordination failures (training, standards, shared infra).
  • Private returns may exceed social returns if externalities (data network effects, privacy harms, market power) are unpriced—raising a role for policy.

• Data and governance externalities

  • Data access asymmetries create strategic advantages; data privacy, algorithmic bias, and transparency failures pose negative externalities that can distort markets and welfare.
  • Regulatory interventions (privacy rules, algorithmic audits, interoperability standards) will shape equilibrium outcomes and investment incentives.

• Research and policy priorities

  • Need for causal microeconometric evidence on AI-SIS impacts: firm-level panel studies, difference-in-differences, randomized rollouts.
  • Quantify effects on markups, entry/exit, employment composition, productivity dispersion, and consumer welfare.
  • Assess whether AI-SIS increase firm-level rents or diffuse gains across markets.
  • Policy levers: targeted training subsidies, data governance frameworks, competition policy updates, standards for transparency and fairness.

Recommendations for researchers and policymakers - Empirical work: build firm-panel datasets linking AI-SIS adoption to outcomes (productivity, profits, employment); use credible identification strategies. - Measure heterogeneity: by firm size, industry, data access, and complementarities with human capital. - Evaluate welfare trade-offs: competition vs efficiency, innovation vs distributional impacts. - Design policy experiments: evaluate training programs, data-sharing platforms, and governance interventions to understand effects on adoption and market structure.

Reference - Uchenna Nzenwata et al., “Artificial Intelligence-Aided Strategic Information System Tools for Competitive Position in the Market: A Systematic Review,” Advanced Journal of Science, Technology and Engineering, 6(1):109–132 (2026). DOI: 10.52589/AJSTE-R3OC7KWI.