The opening premise that AI accelerates productivity in a cascading, self-reinforcing ascent, is not a mere slogan but a lived dynamic already rippling through economies, education systems and everyday work.
When the Southern African AI Summit gathered in Victoria Falls last week, it did more than summarise technical breakthroughs. It foregrounded a practical, policy relevant reality: AI deployed properly has the power to lift the productive capacity of societies, provided that innovation is channelled through governance, ethics, skills and inclusion.
The observation that “AI has a productivity escalator effect” invites a nuanced exploration of how technology translates into tangible gains and what it implies for Zimbabwe and the region as they navigate development, inclusion and resilience.
First, the escalator is not a single step but a flight of stairs. AI increases productivity by enhancing cognitive work, automating repetitive tasks, augmenting decision-making with data-driven insights and enabling new business models that were previously impractical.
In practical terms, AI compresses cycles of research and production, shortens time to market for innovations and amplifies the reach of small and medium enterprises (SMEs) through scalable digital platforms. Yet the escalator only ascends when flow is maintained: data quality and governance foundations, human capital capable of designing, deploying and interrogating AI systems and regulatory environments that incentivise innovation while mitigating risk.
The AI summit’s framing: balancing innovation, regulation and ethics — recognised that one rung will not rise without attention to the others. Productivity without trust, safety and inclusivity can misfire, producing gains for a few while leaving many unserved or harmed.
Second, themes emerging from Victoria Falls illuminate the social architecture surrounding AI. The dialogue around deploying AI for food security, expanding financial inclusion through non-bank financial institutions (NBFIs) and reimagining Afrocentric education with integrity, signals a deliberate shift from a purely technical discourse to a human centric one. AI is not merely a tool for efficiency. It is a catalyst for resilience, equity, and systemic capability.
In agriculture, AI-powered advisory systems, predictive analytics for weather and pests and supply chain optimisation stabilises yields, reduces waste and improves access to markets for smallholders. In finance, AI-driven credit scoring, risk assessment and fraud detection — delivered through NBFIs — broadens access to capital for underserved communities.
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In education, Afropolitan pedagogy that foregrounds local knowledge and ethical considerations ensures learners are not passive recipients of technology but active co designers of a future that respects cultural integrity. The escalation of productivity thus depends on building capabilities that translate AI into inclusive growth and social value, rather than narrow technical efficiency.
Third, there is a clear recognition that readiness is a composite metric. Africa’s and Zimbabwe’s preparedness for AI involves not only computing power and data infrastructure but also governance, digital identity, financial ecosystems and data ethics. The readiness agenda includes: robust data governance frameworks that protect privacy while enabling innovation, standards for interoperability across platforms, mechanisms to align incentives for public and private sector actors and educational pipelines steeped in critical thinking, creativity and ethical reasoning.
The AI summit’s emphasis on Afrocentric integrity — a term that implies centring local knowledge, values and developmental priorities — is a reminder that productivity gains must be compatible with social norms, human dignity and political legitimacy. Readiness is thus a balance sheet of capabilities, trust and belonging: do citizens feel safe participating in AI enabled ecosystems? Do organisations, especially SMEs — have access to affordable, reliable AI tools? Do regulators have the capacity to supervise evolving risks without stifling innovation? These are the questions that the AI Strategy to be launched on October 1 by the Ministry of ICT, Postal and Courier Services will answer.
Fourth, the ethical and risk-managed path to an AI-enabled economy in Zimbabwe requires three cascading investments: people, processes and platforms.
People: education and ongoing skills development must align with the demands of AI-enabled productivity. This means revamping curricula to emphasise data literacy, computational thinking and problem-solving, while embedding ethics, bias awareness and societal impact into every level of schooling. It also means continuous re-skilling for the workforce, enabling transitions for workers in sectors susceptible to automation.
Processes: governance and regulatory mechanisms must establish clear guidelines for data stewardship, algorithmic accountability and risk management, including impact assessments, transparency standards and redress channels.
Platforms: the infrastructure and ecosystems that enable AI deployment must be accessible and affordable, particularly for rural and under -served communities. The escalator effect rises when each rung is robust enough to carry the weight of increased productivity.
Fifth, the economics of inclusion demand deliberate design. AI’s productivity advantages can widen existing disparities if access to technology, digital skills and capital remains uneven.
The African context, marked by informal economies, variability in infrastructure, and diverse regulatory landscapes requires tailored approaches. It calls for a developmental philosophy that couples technology with inclusion: prioritising sectors with high social return (agriculture, health, education, finance) and targeting marginalised groups (women, youth, rural populations) with purpose built programmes.
The role of NBFIs becomes crucial here. They can bridge the gap between formal financial systems and informal economic actors by delivering AI-enabled credit, savings, insurance and payments at scale, often with lower barriers to entry than traditional banks. The product design must respect local risk tolerances, ensure transparent pricing and include education on responsible use of credit (financial education) and AI-driven financial tools. In this sense, AI contributes to a productivity escalator that is not merely about higher output but about more inclusive and sustainable growth.
Sixth, governance must be proactive, not reactive. The ambition to balance innovation with regulation and ethics is too important to be deferred to future contingencies or sector-by-sector patchwork. Zimbabwe and the region can adopt several practical governance principles: regulatory sandboxes that test AI innovations in controlled environments; transparent impact assessments of AI deployments in critical sectors; ethics-by-design requirements that embed fairness, accountability and human oversight into AI systems from inception and public-private partnerships that align incentives and share risk while ensuring public benefits.
A forward-leaning approach also demands data governance that respects privacy, sovereignty and consent, along with clear data stewardship roles for public agencies.
It would be prudent to establish an overarching AI strategy that identifies priority use cases, sets measurable targets and links them to national development plans.
Seventh, the education-to-economy pipeline must be reimagined through Afrocentric integrity. This means education systems that honour Africa’s epistemologies, languages, and knowledge traditions while equipping learners with global competencies. It is not enough to teach generic AI literacy. Curricula should empower students to co-create AI solutions that address local realities, such as drought tolerant crop varieties, climate-resilient farming practices and community-centred fintech services. Teacher training, mentorship programmes and partnerships with industry will help translate classroom knowledge into market ready skills.
Higher education and research institutions should be encouraged to produce applied AI research with direct societal relevance, while ensuring ethical frameworks anchored in local values guide innovation. An education-and-skills strategy grounded in Afrocentric integrity will produce a workforce capable of leveraging AI for inclusive growth, while preserving cultural dignity and social cohesion.
Eighth, the path to readiness is incremental, resilient and collaborative. A realistic Zimbabwean plan would prioritise a few high impact pilots that can learn quickly and scale effectively. For example, AI enabled agritech platforms could be piloted with government and farmer cooperatives to optimise inputs, irrigation and yield forecasting; AI-assisted healthcare analytics could be deployed in urban and rural clinics to improve triage, resource allocation and disease surveillance; and fintech pilots leveraging NBFIs could test credit scoring models, fraud detection and micro insurance products for small traders and farmers.
Each pilot should incorporate robust risk assessments, privacy protections and mechanisms for community feedback. Success in pilots would then feed into a broader scale up strategy, underpinned by investment incentives, regulatory clarity and shared infrastructure (such as digital IDs and interoperable payment rails).
Ninth, the regional dimension matters. Africa’s AI readiness is not a siloed national project. It is a continental growth opportunity. Zimbabwe stands to benefit from regional harmonisation of standards, shared data governance norms and cross-border fintech and agritech ecosystems. Regional collaborations can pool scarce AI talent, infrastructure and financing, creating economies of scale that individual countries cannot achieve alone. Initiatives that promote open data, knowledge exchange and joint research programmes can accelerate productivity gains while ensuring ethical conduct and accountability. In this light, the Victoria Falls AI summit synthesis should be read as a catalytic beacon for regional ambition: when southern African nations coordinate policy, invest in people and empower communities to participate in AI-enabled growth, the escalator effect accelerates for the region as a whole.
Conclusion
The productivity escalator that AI promises is not automatic. It requires intentional design, inclusive policies and resilient institutions. The recent AI Victoria Falls summit rightly foregrounded the hard but essential work of balancing speed with safeguards, imagination with ethics and innovation with humanity. For Zimbabwe, the opportunity is clear, by aligning education, governance, infrastructure, finance and sectoral deployment around Afrocentric integrity and social inclusion, the country can elevate its productive capacity while ensuring AI serves the many, not the few. The escalator will rise more smoothly when every rung — policy, people, and platforms, interlocks with purpose. In this inclusive ascent, AI becomes not a driver of displacement, but a catalyst for localised invention, shared prosperity and sustainable development.
Ndoro-Mukombachoto is a former academic and banker. She has consulted widely in strategy, entrepreneurship, and private sector development for organisations in Zimbabwe, the sub-region and overseas. As a writer and entrepreneur with interests in property, hospitality and manufacturing, she continues in strategy consulting, also sharing through her podcast @HeartfeltwithGloria. — +263 772 236 341.
