Data analytics—led by AI/ML and underpinned by ESG metrics and data governance—are reshaping corporate investment decisions by improving forecasting, risk assessment and strategic agility; but benefits depend on leadership, infrastructure and regulatory alignment, while bias, poor data quality and regulatory fragmentation limit impact.

Main Finding

The dissertation’s systematic literature review (25 peer‑reviewed articles, 2015–2025) finds that AI/ML, ESG integration, and robust data governance are the primary enablers that transform corporate investment strategy. Organisational capabilities (leadership commitment, digital infrastructure, cultural readiness, regulatory alignment) determine whether analytics investments translate into better forecasting, risk intelligence, optimized capital allocation, and enhanced investor confidence. Persistent barriers include algorithmic bias and transparency issues, data quality problems, and fragmented global regulation. The study synthesises these insights into an integrated, practical conceptual framework grounded in Dynamic Capabilities Theory and the Strategic Alignment Model.

Key Points

• Scope and evidence
  • Systematic Literature Review (SLR) following PRISMA 2020; reflexive thematic analysis using Braun & Clarke (2019).
  • Corpus: 25 peer‑reviewed studies (2015–2025), cross‑sectoral (energy, logistics, healthcare, telecommunications, wealth management, etc.).
• Core technological drivers
  • AI/ML and predictive analytics: improve forecasting accuracy, speed, and scale of investment decisions.
  • Big data and real‑time data integration: enable holistic risk views by combining financials, macro data, alternative data (e.g., sentiment).
  • FinTech tools: support execution, liquidity management, and operationalization of analytics.
• Strategic outcomes
  • Improved risk intelligence and volatility management.
  • Optimized capital allocation and higher ROI where analytics are well aligned with strategy.
  • Greater transparency and traceability—important for investor trust and ESG reporting.
• Organisational enablers and constraints
  • Success depends on leadership buy‑in, digital infrastructure, talent/skills, and cultural readiness.
  • Data governance, privacy compliance, and regulatory alignment are essential for scalable adoption.
• Ethical and regulatory risks
  • Algorithmic bias, lack of model interpretability, and opaque decision rules are major ethical barriers.
  • Data quality and fragmented international regulations hinder cross‑jurisdictional implementations.
• Conceptual contribution
  • An integrated framework links technological enablers (AI/ML, ESG analytics, governance, FinTech) with organisational capabilities and strategic outcomes, guided by Dynamic Capabilities Theory and the Strategic Alignment Model.

Data & Methods

• Methodology
  • Systematic Literature Review (SLR) using PRISMA 2020 protocols for study selection and transparency.
  • Reflexive thematic analysis per Braun & Clarke’s six‑phase framework to code and synthesize themes.
• Corpus and selection
  • 25 peer‑reviewed articles published 2015–2025; inclusion/exclusion criteria and databases/search strings reported in appendices.
  • Risk‑of‑bias mitigation: explicit inclusion/exclusion criteria, coding matrix, and thematic frequency analysis.
• Analysis artifacts
  • Thematic coding matrix, theme frequency tables, industry‑specific prioritisation tables, and conceptual framework diagrams included.
• Limitations noted
  • Secondary data only (no primary or causal empirical tests), potential selection bias in reviewed corpus, and limited sample size for a cross‑sector synthesis.

Implications for AI Economics

• Firm‑level productivity and capital allocation
  • AI/ML and big data can materially improve firms’ investment decision quality, potentially raising firm value where organisational capabilities enable effective use. Empirical AI economics work should quantify effect sizes across sectors and firm types.
• Market efficiency and information asymmetry
  • Wider analytical adoption may reduce information asymmetries (better forecasting, more transparent ESG metrics) but could also introduce correlated algorithmic strategies that amplify short‑term volatility—an area for theoretical and empirical modelling.
• Risk, systemic concerns, and regulation
  • Algorithmic commonality and opaque models can create systemic tail risks; economists should study how analytics adoption interacts with market liquidity, contagion, and macroprudential policy.
  • Regulatory fragmentation impedes cross‑border capital flows and comparability of ESG signals; harmonised standards for data, model transparency, and ESG accounting would reduce frictions.
• Labour, skills, and factor reallocation
  • Increased demand for analytics capabilities implies factor reallocation (skills premium for data scientists/quant analysts). Research can explore complementarities between human capital and analytics capital in driving returns.
• Measuring ESG value
  • Analytics enables richer ESG measurement; economists should develop validated methods for incorporating ESG signals into asset pricing, cost of capital estimation, and corporate valuation.
• Research agenda (practical next steps)
  • Causal empirical studies: panel regressions, difference‑in‑differences, or instrumented designs linking analytics adoption to investment outcomes and firm performance.
  • Microdata collection: firm‑level adoption metrics, model types used, governance practices, and performance over time.
  • Systemic modelling: agent‑based or equilibrium models to examine marketwide effects of correlated algorithmic strategies and regulatory interventions.
  • Policy evaluation: cost‑benefit analyses of transparency/interpretability mandates, cross‑border data governance harmonisation, and ESG reporting standards.

Summary takeaway for AI economists: the dissertation synthesises strong conceptual evidence that AI and analytics reshape corporate investment choice, but the field needs rigorous causal and market‑level empirical work to measure welfare, distributional, and systemic consequences—and to inform policy choices around governance, transparency, and ESG standardisation.