Online Reporter
Dr Evans Sagomba
Everything AI
MY Two Cents: To the Ministry of Information Communication Technology, Postal and Courier Services (MICTPCS), led by Honourable Tatenda Mavetera and Permanent Secretary, Dr Beaullah Chirume: Zimbabwe’s recently approved National AI Strategy (2026-2030) is an essential national compass; it identifies opportunities, risks and the structural priorities required to harness Artificial Intelligence for public good.
What must follow is a clear, operational AI policy that translates strategic intent into enforceable standards, budget lines and institutional responsibilities.
That policy should be concise enough to be implemented, flexible enough to respond to rapid technological change, and deeply rooted in Zimbabwe’s social, economic, and ethical realities, so that the benefits of AI are widely shared and its harms are effectively managed.
The difference between a strategy and a policy is practical: the strategy maps the terrain; the policy builds the roads.
Without policy, strategic goals remain aspirational and scattered across ministries, universities and the private sector.
A robust policy provides legal and regulatory certainty for investors and researchers, concentrates scarce fiscal and human resources on national priorities, assigns institutional responsibility so oversight is not diffused, and creates transparent accountability and redress mechanisms that build public trust.
To convert the strategy into policy, the ministry should begin with an inclusive drafting process.
A cross-sectoral working group must bring together the Ministry of Finance, Health, Education, Agriculture, Justice, the Reserve Bank, universities, telecom operators, civil society and labour representatives, together with provincial voices, so rural realities shape design.
Public consultations and targeted town halls in Harare, Bulawayo and major provincial centres will surface practical use-cases and citizen concerns, while short technical subcommittees can draft risk-appropriate rules for regulated sectors.
The draft policy should embed a rolling review mechanism: mandate a full review every 18–24 months, and allow an expedited technical annexe for urgent updates when model capabilities or legal precedents change.
This creates a stable centre while preserving agility. Equally important is aligning resources to policy tasks: the document must specify budget lines, capacity-building targets for public servants, and a timetable for staffing any new oversight units. A policy without resourcing is a blueprint that gathers dust.
Regulation should be proportionate and risk-tiered.
Not all AI systems present the same dangers or demand the same oversight. Define risk categories, low, medium and high, tied to likely harm: for example, agricultural advisory tools may be low or medium risk; automated credit scoring or employment screening medium to high; health diagnostics and judicial decision-support high.
Regulatory requirements, certification and monitoring should scale with that risk profile so innovation is not throttled where risk is modest, while safeguards are rigorous where harms are significant.
Data governance and protection must sit at the core of the policy. Build on existing data-protection frameworks and extend standards to AI datasets: clear rules on consent, anonymisation, provenance, retention and purpose limitation; mandatory data-impact assessments for large-scale public deployments; and standardised templates and APIs that enable research while protecting privacy and national security. These measures enable both trustworthy public-sector AI and responsible research ecosystems.
A national commitment to research, development and local capability is essential. The policy should commit sustained public funding for basic AI research in universities and research institutions, prioritise sectors with high social payoff (health, agriculture, energy, disaster management), and encourage public–private partnerships with explicit technology-transfer and skills-development clauses. Open research infrastructure, shared compute grants, public-interest datasets and model repositories under clear licensing will lower barriers for local researchers and startups.
Industrial policy and procurement can drive domestic value creation. Use public procurement to favour locally developed, explainable and auditable systems, and insert transfer-of-skills requirements into supplier contracts. Offer targeted incentives, such as tax relief or matched funding, for firms that create jobs and deliver measurable skills transfer, while guarding against subsidies that merely extract value for foreign owners. Complement this with standards-based certification to make Zimbabwean AI products export-ready.
Standards, safety and certification should be pragmatic and enforceable. Define technical criteria for robustness, explainability, fairness and security for high-risk applications, and create a certification pathway that requires pre-deployment audits and post-deployment monitoring. Pair technical oversight with an independent AI Ethics Council to interpret values and a Standards Board to define technical norms, ensuring both moral and engineering lenses are represented in governance.
Labour, education and social transition must not be an afterthought. Anticipate displacement risks with reskilling programmes, training vouchers and employer incentives to upskill existing staff. Integrate AI literacy into secondary and tertiary curricula and fund short professional courses for public servants and small-business owners so adoption is informed and responsible. Gender-disaggregated and vulnerable-group impact assessments should be routine where decisions affect livelihoods.
Procurement, interoperability and open government practices will reduce vendor lock-in and promote local adaptation. Require open standards for government systems, publish procurement decisions with appropriate redactions for security, and mandate interoperability so modules can be reused across ministries. These steps both reduce costs and foster a domestic market of adaptable solutions.
International cooperation must be deliberate and strategic. Address cross-border data flows, harmonise where possible with AU frameworks and other regional instruments, and define Zimbabwe’s positions in international fora so the country protects its economic and human-rights interests while remaining open to collaborative research and trade. Clear rules around cross-border collaboration will reduce friction for academics and firms while preserving sovereignty.
Implementation, monitoring and evaluation are the engines of policy. Set concrete indicators, certified systems deployed, proportion of procurement from local suppliers, researchers funded, service delivery improvements, and require post-deployment audits for high-risk systems with public summaries of findings. create a public grievance and redress mechanism with clear response timelines so harms can be remedied promptly and learning is institutionalised.
Politically, sequence reforms to build momentum: pursue quick, visible wins, public-interest pilots in health or agriculture, a national AI competency fund and certificatory pilots, while developing regulatory architecture in parallel. Use procurement strategically to create demand for local capability, and maintain steady, clear communication with the public: accessible policy briefs, media engagement, and demystification campaigns will grow trust and informed debate.
Turning the National AI Strategy into an implementable, principled AI policy is urgent but achievable. Suppose the Ministry of ICT, Postal and Courier Services (MICTPCS), leads with inclusive design, proportionate regulation, strategic investment in R&D and human capital, and transparent oversight. In that case, Zimbabwe can foster an AI ecosystem that expands opportunity, preserves dignity and keeps Zimbabweans, not foreign algorithms, in control of their national future. The policy you craft today will shape who benefits from AI tomorrow; make it bold, accountable and unmistakably Zimbabwean.
About the Author: Dr Evans Sagomba is a Doctor of Philosophy and Chartered Marketer (CMktr, FCIM) with an MPhil and PhD in Philosophy. He specialises in AI, Ethics, and Policy Research, and is an AI Governance and Policy Consultant. His expertise extends to Ethics of War and Peace, Philosophy of Development, and Political Philosophy. [email protected]. ORCID: 0009-0007-0681-0329; Social media handles; LinkedIn; @ Dr. Evans Sagomba (MSc Marketing)(FCIM )(MPhil) (PhD) X: @esagomba
