Stronger AI capabilities correlate with better competitive intelligence in Zimbabwean firms, and that enhanced CI is linked to higher corporate growth and improved sustainability; interviews suggest AI only delivers strategic value when embedded in CI routines.

Harnessing Competitive Intelligence and AI for Corporate Growth and Sustainability

Alexander Maune · April 21, 2026 · Journal of Intelligence Studies in Business

openalex correlational low evidence 7/10 relevance DOI Source PDF

Using survey and interview data from Zimbabwean firms, the study finds that AI capabilities are strongly associated with improved competitive intelligence, which partially mediates positive associations with corporate growth and sustainability performance.

This study investigates how artificial intelligence (AI) integration enhances competitive intelligence (CI) effectiveness and, in turn, drives corporate growth and sustainability performance in Zimbabwean firms. Employing a mixed methods design, the research combines a quantitative survey of 312 senior managers and strategy professionals from medium and large firms with qualitative data from 28 semi structured interviews across manufacturing, financial services, telecommunications, and retail sectors. Quantitative findings reveal that AI capability significantly predicts CI effectiveness (β = 0.62, p < .001), while CI effectiveness significantly predicts corporate growth (β = 0.51, p < .001) and sustainability performance (β = 0.47, p < .001). Mediation analysis indicates that CI effectiveness partially mediates the relationship between AI capability and both corporate growth and sustainability outcomes. Qualitative analysis using the Gioia methodology further identifies three aggregate dimensions: AI enabled competitive intelligence, strategic decision making and growth, and sustainable value creation, illustrating how AI enhances sensing, analytics, and reporting capabilities, and how these capabilities are embedded into strategic routines. The findings extend the resource based, knowledge based, and dynamic capabilities perspectives by conceptualising CI as a mediating dynamic capability that transforms AI driven data into actionable strategic knowledge. The study contributes to theory and practice by demonstrating that AI delivers strategic value only when integrated into CI processes and organisational routines, enabling firms to achieve sustainable competitive advantage in volatile emerging economy contexts.

Summary

Main Finding

AI capability significantly improves competitive intelligence (CI) effectiveness (β = 0.62, p < .001), and CI effectiveness in turn significantly predicts both corporate growth (β = 0.51, p < .001) and sustainability performance (β = 0.47, p < .001). Competitive intelligence partially mediates the relationship between AI capability and both corporate growth and sustainability, implying AI delivers strategic value mainly when embedded in CI processes and organizational routines.

Key Points

Context: Medium and large Zimbabwean firms across manufacturing, financial services, telecommunications, and retail operating in a volatile emerging-economy environment.
Research design: Sequential mixed-methods combining a quantitative survey (n = 312 senior managers / strategy professionals) and qualitative interviews (28 semi‑structured interviews across 12 organisations).
Quantitative findings:
- AI capability → CI effectiveness: β = 0.62, p < .001.
- CI effectiveness → Corporate growth: β = 0.51, p < .001.
- CI effectiveness → Sustainability performance: β = 0.47, p < .001.
- CI partially mediates AI → (growth, sustainability).
Qualitative findings (Gioia method) produced three aggregate dimensions:
AI‑enabled competitive intelligence (improved sensing, automated data collection, deeper analytics).
Strategic decision‑making and growth (embedding AI/CI outputs into routines and planning).
Sustainable value creation (using intelligence to identify sustainability risks, compliance and responsible innovation opportunities).
Theoretical contribution: Extends resource‑based, knowledge‑based, and dynamic capabilities perspectives by conceptualising CI as a mediating dynamic capability that converts AI‑driven data into actionable strategic knowledge.
Practical takeaway: AI by itself is insufficient; organisational routines and CI processes are required to translate AI analytics into growth and sustainability outcomes.

Data & Methods

Mixed methods: sequential design (qualitative → quantitative → integration).
Qualitative phase:
- Approach: Gioia methodology for inductive coding and theory building.
- Sample: 28 semi‑structured interviews from 12 organisations (strategy, CI, analytics, sustainability roles).
- Sectors covered: manufacturing, financial services, telecommunications, retail.
Quantitative phase:
- Survey sample: 312 senior managers and strategy professionals from medium and large Zimbabwean firms.
- Analyses: Regression and mediation analyses to test paths AI capability → CI effectiveness → (corporate growth, sustainability performance). Reported standardized betas and significance levels.
Theoretical framing: Resource‑based view, knowledge‑based view, dynamic capabilities (sensing, seizing, reconfiguring).

Implications for AI Economics

Value capture from AI depends on complementary organisational capabilities:
- Economic models and firm‑level productivity studies should treat competitive intelligence (and similar organizational routines) as mediators/complements to AI investments rather than counting AI capital alone.
Investment prioritisation:
- Firms and policymakers should allocate resources not only to AI tools but to CI processes, data pipelines, training, and embedding analytics into decision routines to realize growth and sustainability dividends.
Human capital and complementarities:
- Results reinforce the notion of skill complementarities — managerial and analytical capabilities that interpret AI outputs amplify the economic returns to AI.
Policy and infrastructure:
- In volatile/emerging contexts, AI plus strengthened CI can partially substitute for weak institutions by improving forecasting and regulatory anticipation; public policy should support data access, interoperability, and CI capacity‑building.
Measurement and macro estimates:
- Macroeconomic assessments of AI adoption should incorporate indicators of organisational integration (e.g., presence of CI routines, analytics embedded in strategy) to avoid overstating expected economic gains from AI hardware/software adoption alone.
Sustainability and inclusive growth:
- AI investments that are channeled through CI processes can simultaneously advance growth and environmental/social performance; this supports economic policies that incentivise data‑driven sustainability practices (e.g., conditional finance, technical assistance).
Research agenda:
- Future AI economics work should model indirect channels (mediators) through which AI affects firm performance, and consider heterogeneity by institutional context and the maturity of organisational routines.

Assessment

Paper Typecorrelational Evidence Strengthlow — Findings are based on cross-sectional, self-reported survey data and correlational mediation analysis, so estimated associations may reflect reverse causation, omitted variables, or common-method bias; qualitative interviews support mechanisms but do not establish causal direction. Methods Rigormedium — Study combines a reasonably sized survey (n=312) with in-depth qualitative interviews and applies established analytical techniques (regression, mediation, Gioia qualitative coding), but lacks stronger identification (e.g., longitudinal data, instruments, experiments), and the reliance on self-report measures and non-random sampling weakens internal validity. SampleQuantitative: 312 senior managers and strategy professionals from medium and large Zimbabwean firms across manufacturing, financial services, telecommunications, and retail sectors; Qualitative: 28 semi-structured interviews with managers/strategists in the same sectors; cross-sectional, likely non-probability sampling and firm-level self-reported measures. Themesinnovation org_design adoption IdentificationCross-sectional observational design using multivariate regression and mediation analysis on survey data (β coefficients reported); triangulated with qualitative interviews (Gioia method). No experimental or quasi-experimental strategy to establish causality. GeneralizabilitySingle-country study in Zimbabwe — context may not generalize to advanced economies or other emerging markets, Sample limited to medium and large firms and senior/strategy professionals — excludes SMEs and frontline employees, Sector coverage limited to four industries — findings may not hold in other sectors, Cross-sectional design limits generalization about causal dynamics over time, Potential cultural, regulatory, and infrastructural differences constrain external validity

Claims (9)

Claim	Direction	Confidence	Outcome	Details
AI capability significantly predicts competitive intelligence (CI) effectiveness (β = 0.62, p < .001). Decision Quality	positive	high	competitive intelligence (CI) effectiveness	n=312 β = 0.62, p < .001 0.3
CI effectiveness significantly predicts corporate growth (β = 0.51, p < .001). Firm Revenue	positive	high	corporate growth	n=312 β = 0.51, p < .001 0.3
CI effectiveness significantly predicts sustainability performance (β = 0.47, p < .001). Organizational Efficiency	positive	high	sustainability performance	n=312 β = 0.47, p < .001 0.3
CI effectiveness partially mediates the relationship between AI capability and corporate growth. Firm Revenue	positive	high	corporate growth	n=312 0.3
CI effectiveness partially mediates the relationship between AI capability and sustainability outcomes. Organizational Efficiency	positive	high	sustainability performance	n=312 0.3
Qualitative Gioia analysis of 28 semi-structured interviews identifies three aggregate dimensions: AI-enabled competitive intelligence, strategic decision making and growth, and sustainable value creation. Decision Quality	positive	high	identification of thematic dimensions (AI-enabled CI; strategic decision making & growth; sustainable value creation)	n=28 0.3
AI enhances sensing, analytics, and reporting capabilities, and these capabilities are embedded into strategic routines to produce strategic value only when integrated into CI processes and organisational routines. Firm Productivity	positive	high	strategic value / sustainable competitive advantage	n=312 0.3
The study extends resource-based, knowledge-based, and dynamic capabilities perspectives by conceptualising competitive intelligence as a mediating dynamic capability that transforms AI-driven data into actionable strategic knowledge. Innovation Output	positive	high	theoretical/conceptual advancement (conceptualisation of CI as mediating dynamic capability)	n=312 0.3
The study uses a mixed-methods design combining a quantitative survey of 312 senior managers/strategy professionals and 28 semi-structured interviews across four sectors in Zimbabwe. Other	null_result	high	study design / sample composition	n=312 0.5