Executive Summary
Why this matters: The risks you are watching for may not be the risks that hurt you. In Nigeria, AI risks are often quieter than global headlines suggest — but no less serious.
What to know: AI systems reshape power, responsibility, and trust in ways organisations do not explicitly choose. Once embedded, they are difficult to undo.
What to do: Treat AI adoption as a governance issue. Document decisions. Assign accountability. Create review mechanisms before deployment.
Watch out for: Assuming you can fix problems later. AI systems do not wait for policy.
The Hidden Risks of AI Adoption in Nigerian Organisations
The Situation
In global discussions, AI risk is usually framed around dramatic scenarios. Regulatory fines running into millions. Class action lawsuits. Public backlash that destroys reputations overnight.
These risks are real. But in Nigeria, the risks that actually hurt organisations are often quieter. They do not make headlines. They do not trigger immediate consequences. They accumulate gradually until they become crises that are expensive to fix.
Understanding these hidden risks is essential for any leader considering AI adoption in the Nigerian context.
What this means for leaders
The risks specific to Nigerian organisations tend to cluster in three areas:
Undocumented decision-making. AI tools are being used to inform decisions about hiring, lending, customer service, and operations. But in many organisations, there is no record of how these tools were configured, what data they were trained on, or how their recommendations were weighted against human judgment. When something goes wrong — a qualified candidate rejected, a creditworthy customer denied, a complaint mishandled — there is no audit trail. The organisation cannot explain its own decisions.
Overreliance on tools no one fully understands. A team adopts an AI tool because it works. They do not know why it works. They do not know what assumptions are embedded in its design. They do not know how it will behave when conditions change. This is not negligence; it is normal. AI tools are designed to be user-friendly, which often means hiding complexity. But hidden complexity is still complexity. When the tool fails or produces unexpected results, the organisation has no internal capability to diagnose or fix the problem.
Data practices that were never designed for machine use. Nigerian organisations have data. Lots of it. Customer records. Transaction histories. Employee files. But this data was collected for human consumption — to be read, interpreted, and contextualised by people who understand its limitations. AI systems consume this data differently. They find patterns humans miss, including patterns that reflect historical biases, data entry errors, and structural inequities. When AI is trained on data that was never meant for machine learning, the outputs can be systematically wrong in ways that are difficult to detect.
Why this matters more in Nigeria
Formal guardrails are still emerging. The Nigeria Data Protection Act 2023 provides a framework, but enforcement is developing and many organisations are still learning what compliance requires.
In this environment, organisations often move ahead without clear internal policies, assuming problems can be fixed later. This assumption is dangerous.
AI systems do not wait for policy. Once embedded into workflows, they begin shaping outcomes immediately:
They influence how people are evaluated. Performance metrics start reflecting AI-assisted outputs. Employees who use AI well are rewarded. Those who do not — or cannot — fall behind. The organisation has made a choice about what it values, even if no one explicitly decided.
They shape how decisions are justified. “The system recommended it” becomes an acceptable explanation. Human judgment defers to algorithmic output. Accountability becomes diffused across a process no one person controls.
They redistribute trust. Customers and employees learn that their interactions are mediated by AI. Some adapt. Others disengage. The relationship between the organisation and its stakeholders shifts in ways that may not surface until trust is tested.
Practical takeaway
The core insight is simple but often ignored: AI adoption is a governance issue, not just an efficiency play.
- Document decisions before you make them. Before deploying any AI tool, record: What problem does this solve? What data does it use? Who is accountable for its outputs? What are the review mechanisms? This documentation is not bureaucracy. It is insurance against future crises.
- Assign accountability explicitly. Someone needs to own AI decisions. Not the vendor. Not the algorithm. A person with authority to intervene when things go wrong. If this person does not exist, you are not ready to deploy.
- Create review mechanisms with teeth. Periodic reviews that actually have the power to pause or reverse AI deployments. Not annual check-boxes, but genuine oversight with the authority to act.
- Understand NDPA 2023 implications. The Nigeria Data Protection Act has specific requirements for how personal data is processed. Many AI tools involve cross-border data transfers, automated decision-making, and data processing that requires explicit consent. Non-compliance is not theoretical; it is a legal and reputational risk.
Risks or limitations
There is a risk that this framing creates paralysis. If AI adoption is so fraught with hidden risks, perhaps the safest path is to do nothing.
That is not the message. Inaction has its own risks, which we will explore in a later post. The goal is not to avoid AI but to adopt it with eyes open.
The organisations that navigate this well are not necessarily the most cautious. They are the ones that treat AI as a governance challenge requiring explicit choices, not a technology upgrade that can be delegated to IT.
The risk is not that AI will replace people. The risk is that organisations adopt systems that subtly reshape power, responsibility, and trust — without ever explicitly choosing to do so.
