Online Reporter
Dr Evans Sagomba
My TWO cents: Honourable Minister, Permanent Secretary, the adoption of an AI strategy is a decisive moment for Zimbabwe.
The 2026–2030 Zimbabwe AI Strategy rightly signals ambition; the policy that follows must convert that ambition into a governance architecture that is coherent, implementable and durable.
The questions you already ask, where AI governance begins, who owns it, what counts as AI, how to language it, how to classify and manage risk, and how to make values operative, are not technical quibbles. They are the scaffolding upon which public trust, economic opportunity and constitutional accountability will rest.
I offer the following principles as my thinking on the practical, academically grounded blueprint to translate strategy into policy.
Define boundaries between AI Governance, IT Governance and Organisational Governance
AI governance should be explicitly defined as a cross-cutting governance domain that sits at the intersection of IT governance, regulatory governance and organisational governance. IT governance remains necessary for infrastructure, cybersecurity, data management and service reliability.
Organisational governance, boards, audit committees, human resources, legal, and compliance retain accountability for corporate conduct and fiduciary duties.
AI governance is neither a subset of IT nor a separate ministry; it is a governance lens that overlays and augments existing structures when the systems in question exhibit autonomy, adaptivity, scale effects or opacity.
Operationally, the policy should prescribe a governance matrix: IT governance owns availability, integrity and resilience; organisational governance owns procurement, contractual risk and human resources; AI governance owns lifecycle oversight of algorithms and models, risk classification for autonomous decision-making, explainability requirements, and redress mechanisms for harms.
This matrix must be obligatory across public agencies and recommended for regulated private entities, so responsibilities are clear and handoffs are formalised.
Clarify ownership and accountability
Who “owns” AI cannot be an abstraction. Ownership lives with the decision-makers who commission, deploy and rely upon AI outputs.
The policy should require named accountable officers at three levels: strategic (board or permanent secretary level), programme (head of department or agency), and technical (chief data officer or model owner).
For high-impact systems, there must be joint accountability: the policy decision-maker cannot absolve themselves by claiming technical ignorance, and technologists cannot claim policy neutrality.
Accountability mechanisms should include ex ante impact assessments, a mandatory registry of high-risk systems, audit trails for model updates, and post-deployment monitoring obligations.
Where harm occurs, the policy should enable administrative sanctions, contractual remedies and referral to consumer protection bodies.
Criminal liability remains a last resort reserved for recklessness or wilful malfeasance; the primary emphasis should be on administrative clarity, remediation and learning.
Articulate a pragmatic definition of AI and differentiate GenAI where necessary
Policy must adopt a principled, practical definition of AI that focuses on capability and effect rather than a catch-all list of techniques.
Define AI as systems that perform tasks that would otherwise require human cognitive skills, which learn or adapt from data, and which make or materially influence decisions affecting people, services or markets.
Within this umbrella, treat generative AI (GenAI) as a subclass deserving of special attention when it is used to create synthetic content, impersonate individuals, or produce unverifiable outputs at scale.
The policy should not fall into the trap of over-inclusion that stifles innovation, nor under-inclusion that leaves risks unmanaged.
Use risk-based thresholds to determine applicability: models that materially affect rights, safety, finances, or public discourse should be regulated; purely experimental prototypes with no external effect may be guided rather than mandated.
GenAI requires additional rules on provenance labelling, watermarking, and liability for fabricated content when deployed for public-facing services.
Standardise language to promote clarity and interoperability
Vocabulary shapes governance. The policy should mandate a small, consistent lexicon to be used across ministries and regulated entities.
Reserve “AI system” for the overall deployed capability; “model” for the trained algorithmic artefact; “platform” for the technical environment that hosts models and data pipelines; “tool” or “application” for user-facing instantiations.
These distinctions help allocate responsibilities: platforms are responsible for operational safety and patching; models for training data provenance and testing; applications for user-facing disclaimers and human-in-the-loop controls.
Require public-sector documentation templates that adopt this lexicon for procurement, impact assessment and audit reporting. A shared language reduces ambiguity in contracts and enables inter-agency comparability.
Adopt a layered, risk-based classification and governance approach
Risk classification is the policy fulcrum. Adopt a three-tiered approach: low, medium and high impact.
Criteria for classification must be concrete and context-sensitive: the scale of deployment, the sensitivity of affected datasets, the degree of autonomy in decision-making, and the reversibility of harmful outcomes.
Low-impact systems require transparency and basic documentation. Medium impact systems require formal risk assessments, testing protocols, and escalation pathways.
High impact systems demand mandatory external audits, independent certification, public transparency statements, and, in some cases, a prohibition on public sector use until controls are in place.
Note that approval of risk classification should be multi-stakeholder: technical reviewers, legal counsel and a designated ethics committee must sign off.
For high-risk systems, the policy should require ministerial-level notification and, where decisions affect human rights or welfare, explicit parliamentary oversight mechanisms.
In addition, management of high-risk systems must include: robust data governance, pre-deployment bias and fairness testing, continuous monitoring with predefined stop-conditions, and clear remediation plans.
Independent audit rights should be guaranteed to a designated national regulator or inspectorate.
Translate values and ethics into enforceable obligations
Values without operationalisation are ceremonial.
Translate constitutional values and ethical principles into obligations that bind agencies and vendors.
For example, the principle of non-discrimination becomes a requirement for disaggregated impact assessments and thresholds for disparate impact; the principle of transparency becomes mandatory model documentation and public registries for systems above a risk threshold; the principle of accountability becomes named responsible officers and enforceable audit trails.
Embed ethical checklists into procurement, budgeting and performance evaluations.
Make compliance with these obligations a condition for public procurement and for licensing of critical digital infrastructure. Incentivise compliance through fast-track procurement for certified operators and technical assistance for smaller agencies.
Ensure participatory governance and public contestability
AI governance cannot be purely technocratic.
Establish participatory processes: regular stakeholder consultations, community impact hearings, and mechanisms for affected individuals to seek explanation and redress. create a public registry of significant AI systems that includes summaries of purpose, data sources, risk classification and contact details for accountability.
Design contestability mechanisms that allow citizens and civil society to challenge outputs that affect them.
These mechanisms should be affordable, timely and backed by the power to require corrective actions.
Build institutional capability and phased regulatory implementation
Policy must be realistic about capacity. Prioritise capability-building: training for policy-makers, technical staffing for regulatory bodies, and shared tooling for impact assessments.
Adopt a phased implementation timeline with pilot windows for complex requirements such as external certification, allowing time for ecosystems to adapt. More so, create a central AI Coordination Office within the Ministry as a convening hub, with a mandate for technical guidance, standards development and cross-sector harmonisation. This office should not centralise all approvals; rather, it should set standards and support decentralised compliance.
Align incentives with innovation and protection
Regulation and innovation need not be antagonistic. Use a mix of rules and incentives: regulatory sandboxes for experimentation under monitored conditions; procurement preferences for compliant vendors; public funding for open datasets and model evaluation infrastructure; and penalties for non-compliance calibrated to deter harm without crushing nascent local enterprises.
Also, promote open, reusable standards for data interchange, model cards and risk assessment templates so Zimbabwean startups can compete regionally on both quality and compliance.
In the end, successful translation of the Zimbabwe AI Strategy into a standing policy will be judged not by rhetorical fidelity to principles but by the clarity of duties, the rigour of risk controls, the fidelity of ethical operationalisation and the institutional capacity to enforce them.
The Ministry is well placed to make Zimbabwe a model for responsible, African-led AI governance that balances opportunity and protection.
Treat AI governance as a governance problem first and a technical problem second.
Define boundaries, name accountable officers, adopt a pragmatic definition of AI with special rules for GenAI, standardise language, classify risk consciously, make values enforceable, ensure participation and build capability incrementally.
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)
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I am a scientific research specialist in Machine Learning, Deep Learning, AI and Quantum Computing. I work with a number of reputable research institutions around the world. Each time I read articles about analytic assessment of this “animal” called Artificial Intelligence or AI, I continue to wonder if this animal is fully understood by these same people who write about it. While, as a country, we are scared of being left behind in the adaptation of AI as a development tool in the technical realm, it is naive however to try and stoop like a Falcon when you are a mere chicken. This country is a long way from understanding let alone using AI tools as a means or way of crafting development strategies. We must understand the basics first, run risk assessments of AI and then slowly ease into using it. It is pointless to rush into the unknown, akin to jumping into an empty swimming pool headlong. The consequences can only be disastrous.