A unified Decision Intelligence framework aims to fuse artificial intelligence and managerial judgment to produce more sustainable strategic decisions; the paper synthesizes multiple management theories into DISB and proposes an operational decision cycle plus eight propositions for future empirical testing.

Main Finding

The paper proposes Decision Intelligence for Sustainable Business (DISB), a conceptual framework that treats Decision Intelligence as a dynamic organizational capability. DISB integrates artificial intelligence (AI), human judgment, knowledge integration, strategic management, and sustainability governance into a unified decision cycle. The framework argues that combining AI-driven analytics with managerial expertise and organizational capabilities improves strategic decision quality, adaptability, and sustainable value creation. The study develops an operational decision cycle and eight theoretical propositions to guide empirical validation.

Key Points

• Motivation and gap

  • Rapid AI advances increase analytical capacity but literature is fragmented across AI/IS, strategic management, business analytics, and sustainability.
  • Existing work is often technology-centered and neglects how AI outputs should be integrated with managerial judgment, organizational learning, and sustainability objectives.
  • There is conceptual ambiguity around "Decision Intelligence"; the paper seeks to clarify its boundaries and position it as an organizational capability.

• Core contribution

  • Introduces DISB as an integrated capability (not merely a technology stack) that coordinates AI, human judgment, knowledge processes, strategic leadership, and ESG governance.
  • Emphasizes complementarity: superior strategic decisions arise from integrating algorithmic outputs with managerial interpretation, contextual knowledge, and stakeholder considerations.
  • Claims the framework contains five interrelated capabilities (identified in the paper), an operational decision cycle, and eight testable propositions to direct empirical work.

• Theoretical integration

  • The framework synthesizes multiple theories: Resource-Based View, Dynamic Capabilities, Knowledge-Based View, Organizational Information Processing Theory, Socio-Technical Systems Theory, and Stakeholder Theory.
  • Aims to move beyond isolated disciplinary explanations to show how technological resources become strategic advantages through organizational routines, governance, and human–AI complementarities.

• Practical claims

  • Provides managers a structured approach to embed AI into strategic decision processes while preserving human judgment and embedding sustainability goals.
  • Highlights the need for explainable AI, governance arrangements, and knowledge integration to translate analytics into long-term organizational performance.

Data & Methods

• Research design

  • Conceptual/theory-building study (no primary empirical data).
  • Systematic conceptual methodology: structured literature identification, thematic synthesis, interdisciplinary theory integration, and iterative framework development.

• Literature search & selection

  • Primary search via Google Scholar; cross-checked with established databases.
  • Search terms included: Decision Intelligence, Artificial Intelligence, Strategic Decision-Making, Decision Support Systems, Explainable AI (XAI), Sustainable Business, Dynamic Capabilities, etc.
  • Emphasis on recent decade literature plus seminal works from multiple disciplines.

• Inclusion criteria and synthesis approach

  • Included peer‑reviewed journals, books, and influential conceptual studies relevant to AI, decision-making, strategic management, and sustainability.
  • Thematic (not chronological) synthesis to identify recurring concepts and gaps.
  • Framework developed through iterative consolidation of constructs and theoretical alignment, evaluated for logical consistency, theoretical coherence, and managerial applicability.

• Outputs

  • A conceptual framework (DISB), an operational decision cycle, and eight theoretical propositions to be empirically tested in future research.

Implications for AI Economics

• Conceptual implications

  • Reframes AI adoption as an endogenous organizational capability with returns depending on complementary investments (human capital, knowledge management, governance). This aligns with economic models of complementarities and endogenous productivity.
  • Suggests heterogeneous returns to AI across firms: firms that develop DISB-like complementary capabilities will capture higher and more sustainable economic value from AI.

• Empirical research directions

  • Measurement: need to operationalize DISB (indices of AI assets, human judgment integration, knowledge integration routines, sustainability governance, and decision lifecycle practices).
  • Identification strategies: firm-level panel data, matched employer-employee datasets, and difference-in-differences/quasi-experimental designs to estimate causal effects of DISB investments on productivity, profitability, innovation, and ESG outcomes.
  • Microeconomic questions: how DISB affects factor shares, wage premia for managerial and analytical skills, returns to R&D/AI capital, and firm exit/entry dynamics.
  • Macro/industry-level questions: diffusion of DISB capabilities, market concentration effects (do DISB complementarities create winner-take-most dynamics?), and aggregate productivity implications.

• Policy and managerial implications relevant to economics

  • Labor and skills policy: DISB implies higher premium on complementary managerial and interpretive skills; vocational and executive training policies should emphasize human–AI collaboration and interpretive judgment.
  • Competition and regulation: regulators should consider how governance and explainability features of DISB influence market power and consumer welfare; policies could promote interoperability, transparency, and diffusion of governance best practices.
  • ESG and externalities: integrating sustainability governance into decision intelligence can internalize social/environmental externalities—empirical work can assess whether DISB adoption leads to measurable ESG improvements and whether those improvements affect firm valuation and social welfare.

• Practical evaluation & cost–benefit considerations

  • Economic analyses should account for implementation costs (data infrastructure, talent, governance) and dynamic benefits (ability to adapt, long-run resilience).
  • Comparative case studies and cost-effectiveness analyses across sectors (e.g., manufacturing, finance, healthcare) would clarify where DISB investments yield highest social and private returns.

Overall, the paper provides a theoretical scaffold useful to economists studying AI adoption and impacts: it emphasizes complementarities, organizational heterogeneity, and sustainability as integral to the economic assessment of AI-driven decision-making.