WHEN people hear the phrase AI governance, they often imagine committees, policies, or dashboards. It sounds like paperwork, oversight and endless reviews.
But here is the truth: governance is not a checklist.
It is not a feature you bolt onto a machine once it is already running. Governance is a layer.
And until we understand that, we will keep building systems that look impressive on the surface but collapse under pressure.
Let us talk plainly.
Most efforts at AI governance today fail because they confuse tools with architecture. We add trust scores, human sign-offs, and monitoring dashboards. We write policies and hope they will keep machines in line. Yet when the system misbehaves, we act surprised.
The problem is not that we lack intent. The problem is that we lack a proper layer.
Think of it like building a house. You don’t add the foundation after the roof is already up. You do not hope that a few inspections will stop the walls from falling. The foundation is a layer. It holds everything else in place.
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AI governance works the same way. Without a governance layer, all the reviews and audits become theatre. You can observe failures. You can explain them. You can even punish someone afterwards. But you cannot prevent them.
Zimbabwe, like many countries, is waking up to the promise and risks of artificial intelligence. We are told AI will transform mining, health, law and finance.
And it will.
But only if we build systems that are safe to trust. That means governance must be baked in from the start. Not as a policy document gathering dust. Not as a committee that meets once a quarter. But as a layer that decides whether an AI system is even allowed to act.
Here is the heart of the matter.
AI models generate outputs. Policies describe expectations. Monitoring observes behaviour. Humans assign accountability.
But none of these answers the most basic question: Is this decision allowed to be executed at all? That question belongs to governance.
It is the layer that defines admissible actions, enforces authority boundaries and constrains behaviour under uncertainty.
It decides what happens before execution, not after. Why does this matter for Zimbabwe?
Because we are at a crossroads. Our government is drafting strategies for AI adoption. Our universities are training the next generation of data scientists.
Our businesses are exploring automation. Yet too often, governance is treated as an afterthought.
We talk about ethics, transparency and accountability. These are important. But without a governance layer, they remain words on paper.
We need systems that can say “no” before harm occurs, not apologies after.
Consider healthcare. Imagine an AI system recommending treatments.
Policies may say doctors must review outputs. Dashboards may track accuracy. But what if the system suggests a harmful action in real time? Governance is the layer that blocks that action before it reaches the patient.
Without it, oversight becomes reaction. And in medicine, reaction can mean lives lost. Or take finance. AI systems now approve loans, detect fraud and manage investments. Policies may require audits.
Humans may sign off on large transactions. But governance is the layer that prevents unauthorised transfers before they happen. Without it, we only discover fraud after money has vanished.
Oversight theatre, again.
This is why so many AI systems appear to work fine until they scale.
At small levels, human reviews and dashboards seem enough. But once systems face pressure, speed, or irreversible decisions, oversight collapses.
Governance is not about catching mistakes. It is about preventing them.
So, how do we build this layer? First, we must stop thinking of governance as a bolt-on. It is not ethics scoring.
It is not an explainability tool.
It is not human-in-the-loop approvals. Those are downstream mechanisms. Governance is upstream. It is the gatekeeper.
It is the part of the system that decides whether intelligence is allowed to act at all. Second, governance must be explicit, enforced, and deterministic. That means rules are clear, authority boundaries are firm, and behaviour under uncertainty is constrained. No vague guidelines.
No “we’ll see what happens.”
Governance must be coded into the architecture. If a decision is not admissible, the system cannot execute it.
Full stop!
Third, governance must be contextualised for Zimbabwe. We cannot simply copy frameworks from Silicon Valley or Brussels. Our challenges are different.
We face resource constraints, regulatory gaps, and unique cultural contexts. Our governance layer must reflect our realities.
It must protect citizens, empower businesses, and build trust in public institutions. And it must do so in ways that are affordable and practical.
This is not just a technical issue. It is a political one. Governance is about authority. Who decides what an AI system can do? Who sets boundaries?
Who enforces them?
In Zimbabwe, this means government regulators, industry leaders, and civil society must work together.
Governance cannot be left to engineers alone. It is a national conversation.
And here is the uncomfortable truth. Until governance is treated as a layer, control will always be assumed, never guaranteed. We will keep telling ourselves that policies and dashboards are enough. We will keep believing that human sign-offs can catch every risk. And we will keep being surprised when systems fail at scale. Oversight theatre will continue.
Trust will erode.
And opportunities will be lost.
But there is another path. We can build governance into the architecture of our AI systems. We can make it explicit, enforced and deterministic.
We can design systems that constrain behaviour before execution.
We can ensure that intelligence is only allowed to act within boundaries we define. And in doing so, we can unlock the benefits of AI while protecting our people, our institutions and our future.
Zimbabwe has a chance to lead here. We are not yet locked into legacy systems. We are drafting strategies from scratch.
We can embed governance as a layer from the beginning.
If we do, we will not only avoid the mistakes of others. We will set a standard for Africa and beyond. We will show that governance is not theatre. It is architecture.
So, the next time you hear someone talk about AI governance, ask them this: Is it a feature, or is it a layer? If it is a feature, it will fail. If it is a layer, it can succeed. And if Zimbabwe gets this right, we will not just adopt AI. We will govern it. We will control it.
And we will make it serve our people, not the other way round.
That is the true meaning of AI governance. It is not paperwork. It is not oversighting theatre. It is the foundation, the — the part of the system that decides whether intelligence is allowed to act at all. Until we build that layer, control will remain an illusion. But once we do, control becomes real. And with real control, AI can become a force for good in Zimbabwe.
Sagomba is a chartered marketer, policy researcher, AI governance and policy consultant, ethics of war and peace research consultant. — [email protected]. LinkedIn: @ Dr. Evans Sagomba Dr. Evans Sagomba (MSc Marketing)(FCIM )(MPhil) (PhD) X: @esagomba.
