Artificial intelligence deployment across Ghanaian finance, healthcare, agriculture, digital services, and public administration is expanding more rapidly than the institutional machinery required to supervise its risks, according to a qualitative investigation of the country’s National Innovation System. Researchers Benedict Afful Jr. and Kassim Alabani evaluated Ghana’s institutional environment to determine whether the country possesses the operational capacity, cross-agency coordination, and statutory authority necessary to enforce responsible artificial intelligence standards. Rather than documenting a total absence of governance mechanisms, the empirical findings demonstrate an uneven, system-wide governance readiness gap. While Ghana launched its National Artificial Intelligence Strategy 2025–2035 on April 24, 2026, and participated in UNESCO’s AI Readiness Assessment Methodology, formal policy ambition has developed ahead of the technical competence, routine advisory channels, and enforcement capabilities of implementing state bodies.
To diagnose why research and policy mechanisms break down, the researchers combined National Innovation Systems theory with Technological Innovation Systems functional analysis and normative governance benchmarks established by UNESCO and the African Union. The investigative framework conceptualized system performance as inherently relational, examining whether universities, commercial enterprises, government ministries, financial institutions, intermediaries, and boundary-spanning professionals effectively exchange knowledge. The authors extended functional analysis beyond industrial output, treating knowledge diffusion, search guidance, resource mobilization, and legitimation as essential governance functions. This framework grounded the study’s central theoretical contribution, termed the Governance Readiness Paradox, which defines a structural condition wherein commercial deployment and high-level strategy accelerate at a pace that continuously outstrips the institutional capacity required to convert technical evidence into enforceable, accountable, and legally sound public oversight.
The empirical research design was rooted in an interpretivist approach that collected qualitative evidence from 32 purposively selected institutional leaders across Ghana’s innovation landscape. The final sample included eight participants from universities and research institutes, six from commercial technology firms and industry, six from government regulatory and policy agencies, four from financial institutions, four from intermediary bodies such as technology hubs and incubators, and four boundary-spanning actors with sustained professional careers bridging multiple sectors. Data collection utilized a three-part qualitative protocol comprising vignette-based scenarios, triadic elicitation, and the Critical Incident Technique. Each semi-structured session lasted 60 to 90 minutes. Vignettes assessed hypothetical responses to simulated real-world dilemmas, including clinical diagnostic failures and discriminatory algorithms, while triadic elicitation prompted participants to compare institutional roles systematically. The Critical Incident Technique gathered detailed first-hand accounts of specific, bounded events where knowledge translation had succeeded, stalled, or failed in practice.
Data were evaluated through reflexive thematic analysis using NVivo software, identifying patterns across participant categories without relying on rigid statistical coder agreement or formal grounded-theory stopping criteria. Coding categories were deductively integrated with Technological Innovation Systems functions and UNESCO and African Union governance pillars: safety and security, transparency, accountability, and inclusion. This thematic structuring established a systemic failure typology consisting of four mutually reinforcing mechanisms: capability failures, connectivity failures, legitimacy failures, and resource failures. Documentary triangulation against publicly available policy frameworks, such as the Data Protection Act 2012 and the National AI Strategy 2025–2035, contextualized the interview narratives. The authors maintained analytic reflexivity and conducted member checking with selected informants to verify that findings accurately reflected the operational landscape while protecting participants from deductive identification.
Universities and research institutes demonstrated substantial epistemic capacity, yet academic respondents reported severe barriers when attempting to transmit technical insights into public policymaking. Participants observed that when research on algorithmic bias was conducted—such as a completed study evaluating automated credit-screening discrimination in domestic financial technology—no formal institutional pipeline existed to translate those empirical findings into regulatory briefs or actionable policy interventions. Triadic comparisons among academic respondents highlighted an institutional disconnect characterized as knowledge without mandate among researchers versus mandate without knowledge among statutory authorities. Furthermore, academic research agendas were found to be predominantly driven by international donor funding. These external grants frequently rewarded technical performance metrics, such as processing velocity and model sophistication, while providing negligible institutional incentives to pursue governance-oriented inquiry, ethical evaluation, or local policy relevance.
Interviews with commercial firms revealed an operational environment where experimental artificial intelligence systems in fintech, health technology, and agritech are adopted far faster than domestic standards for independent audit, public disclosure, and remediation. While enterprises with foreign investors or international accelerator partnerships exhibited greater governance awareness, local firms operated under profound regulatory ambiguity. When presented with a vignette detailing an algorithmic fraud-detection system exhibiting discriminatory bias across a massive user base, industry participants noted that corporate legal counsel routinely advises silence to minimize corporate liability in the absence of explicit statutory disclosure duties. A documented critical incident from the financial sector corroborated this dynamic: an internal corporate audit revealed an automated credit-scoring model that assigned systematically depressed scores to applicants from three northern regions despite repayment records comparable to other applicants. The firm recalibrated the model entirely internally, omitting regulatory notification, affected customer disclosures, or independent remediation.
Government regulatory agencies and ministries exhibited an acute capability-connectivity bottleneck, leaving state entities struggling to operationalize broad policy signals into technical oversight. In response to a vignette requiring a 48-hour parliamentary briefing on an emerging artificial intelligence incident, regulatory officials acknowledged that internal personnel lacked the specialized technical expertise to draft credible assessments independently, while possessing no formal procedures to commission academic assistance on short notice. Although statutory bodies such as the Data Protection Commission and the National Information Technology Agency maintain defined mandates, cross-agency coordination remains fragmented. Critical incident accounts demonstrated that successful knowledge transfer from researchers to state bodies occurred almost exclusively through informal personal connections, temporary academic secondments, or prior individual relationships rather than durable administrative routines. Consequently, when key personnel resigned, retired, or concluded donor-funded contracts, institutional memory and advisory channels collapsed.
Financial institutions and intermediary organizations formed the least developed components of the governance architecture. Commercial banks and investment actors routinely evaluated technology through conventional operational and reputational risk categories, completely lacking dedicated criteria to assess artificial intelligence safety, data provenance, or algorithmic fairness. Intermediary bodies, including technology hubs, accelerators, and university technology transfer offices, operated effectively as commercial bridges connecting startups to private capital and markets, but their potential role as regulatory bridges remained entirely unactivated. Entrepreneurship training curricula omitted instruction on regulatory compliance, legal liability, and ethical deployment, producing founders who were commercially capable yet governance-naive. Across the entire innovation system, chronic resource shortages acted as a systemic multiplier: domestic funding lines dedicated to public sector artificial intelligence research, regulatory monitoring, and expert technical staffing were virtually nonexistent, leaving institutions reliant on fragmented, temporary external assistance.
To synthesize these institutional realities, the authors developed the Ghana AI Governance Readiness Matrix, which cross-tabulates innovation system components against the four international governance pillars. Component ratings ranged from very low to moderate across dimensions, illustrating that the system suffers from fragmented integration rather than absolute institutional absence. Systemic failures propagated through self-reinforcing cycles: missing diffusion pipelines left regulators without technical evidence; low regulatory capability created ambiguous market oversight; ambiguous expectations discouraged private firms and financiers from investing in auditing routines; and an absence of domestic governance demand incentivized universities to prioritize externally funded technical projects over public policy translation. The matrix identified relative institutional assets that can serve as foundations for reform, including existing academic expertise, emerging policy frameworks, boundary-spanning professionals, and international market linkages.
The authors formulated eight sequenced policy recommendations distributed across three distinct implementation phases spanning five years. Phase I focuses on low-cost structural alignment, establishing a national cross-agency coordination mechanism, creating an interim expert roster to channel university research into regulatory processes, and integrating civil society into National AI Strategy implementation. Phase II establishes dedicated artificial intelligence units within sectoral regulatory bodies, launches structured institutional secondments between universities and public agencies, pilots proportionate impact assessments, and institutes a domestic research funding window to mitigate external donor dependency. Phase III consolidates professional certifications, recurrent public financing lines, and independent statutory accountability mechanisms. The authors caution that premature regulatory compliance mandates, instituted before regulators possess technical capacity, risk generating purely symbolic compliance rather than enforceable governance.
The study notes several methodological boundaries that contextualize its findings. Purposive sampling was intentionally designed to capture information-rich elite institutional perspectives rather than achieve statistical representativeness across the Ghanaian population. Dedicated sampling strata did not include judicial officers, parliamentary committees, civil society organizations, or marginalized demographic groups directly impacted by automated decision-making. Consequently, empirical findings regarding inclusion and societal remedy were primarily derived from institutional accounts, such as the northern regional credit-scoring incident, rather than direct public testimony. Additionally, because corporate audit records remained proprietary, private organizational incidents relied on participant recall rather than independent forensic audits. The authors emphasize that their qualitative diagnosis reflects a dynamic transitional period following the launch of the National AI Strategy, underscoring the need for future longitudinal and quantitative studies to track whether planned institutional coordination successfully closes the governance readiness gap.
Subject of Research: Institutional readiness and governance bottlenecks for artificial intelligence in Ghana's national innovation system
Article Title: Ghana’s national innovation system and the governance readiness gap for artificial intelligence
Article References: Afful, B., Jr., & Alabani, K. (2026). Ghana’s national innovation system and the governance readiness gap for artificial intelligence. Discover Artificial Intelligence, 6(1), Article 1864. https://doi.org/10.1007/s44163-026-02687-x
Image Credits: AI Generated
DOI: 10.1007/s44163-026-02687-x
Keywords: Artificial Intelligence, AI Governance, Ghana, National Innovation System, Technological Innovation Systems, Technology Policy, Governance Readiness Paradox, Research-to-Policy Translation, Institutional Capacity, UNESCO AI Ethics, African Union AI Strategy, Algorithmic Accountability, Public Administration, Data Protection Commission, Digital Development, Qualitative Research
News Source: Blake Davidson. (November 13, 2026). Ghana Faces Governance Readiness Gap as Artificial Intelligence Deployment Outpaces Oversight. Scienmag.
