Artificial intelligence has arrived in Nigerian boardrooms not as a distant prospect but as an operational force already shaping decisions, outputs and risk profiles. Yet much of its adoption remains informal, embedded in workflows without corresponding governance. That gap should concern any serious board. AI is not simply another tool. It is a multiplier of both capability and consequence.
Across sectors, executives are deploying AI to accelerate customer service, automate analysis, and enhance decision-making. Marketing teams generate campaigns in minutes. Risk teams experiment with predictive models. HR functions screen candidates using algorithmic tools. The gains are obvious: speed, scale and cost efficiency. What is less visible is the accumulation of unmanaged risk beneath these efficiencies.
Boards must begin by reframing AI from a technology issue to a governance issue. The question is not whether the organisation uses AI. It already does, whether formally sanctioned or not. The real question is whether its use is understood, controlled and aligned with the organisation’s risk appetite. Without that clarity, boards are effectively delegating critical judgement to systems they do not oversee.
The first actionable step is visibility. Directors should require a comprehensive inventory of AI use across the organisation. Not a high-level summary, but a mapped register that identifies where AI is deployed, for what purpose, and with what data inputs. This exercise often reveals shadow usage, where employees adopt public AI tools without oversight, exposing sensitive data in the process. Visibility is the foundation upon which all other controls depend.
Once mapped, the board must insist on classification. Not all AI carries the same level of risk. A model that drafts internal emails is materially different from one that influences credit decisions or customer eligibility. Organisations should categorise AI use cases based on impact, sensitivity of data, and potential for harm. High-risk applications require enhanced controls, including human oversight, explainability standards and rigorous testing before deployment.
Data governance sits at the centre of this conversation. AI systems are only as reliable as the data they consume. Poor data quality leads to flawed outputs, and in regulated sectors, that can translate into legal exposure. Boards should ensure that data lineage is understood, that data used in AI systems is lawfully obtained, and that retention and deletion practices are enforced. This is not theoretical. Regulators are beginning to scrutinise how organisations use data in automated decision-making.
There is also a question of bias and fairness that cannot be ignored. AI models can replicate and amplify existing inequalities if not properly managed. In a diverse and complex market like Nigeria, this risk is particularly acute. Boards should require evidence that models have been tested for bias and that mitigation strategies are in place. This is not simply an ethical concern. It is a reputational and regulatory risk that can erode trust quickly.
Third-party risk is another critical dimension. Many organisations rely on external vendors for AI capabilities, from cloud providers to specialised platforms. Boards must ensure that vendor due diligence extends beyond commercial terms to include security, data handling practices and model governance. Contracts should clearly define responsibilities, particularly in the event of failure or breach.
Accountability must be explicit. AI governance cannot sit in a vacuum or be absorbed vaguely into existing roles. Boards should mandate clear ownership at executive level, often through a designated AI or data governance lead, supported by cross-functional oversight. This ensures that decisions about AI deployment are made deliberately, with input from legal, risk, technology and business leaders.
To operationalise this, boards should embed AI oversight into existing governance structures rather than treating it as an isolated initiative. Audit and risk committees should include AI within their remit. Regular reporting should cover not just performance metrics but risk indicators, incidents and control effectiveness. Internal audit functions should be equipped to review AI systems, not just traditional processes.
Finally, boards must invest in their own literacy. Effective oversight requires a working understanding of how AI systems function, where they fail, and what questions to ask. This does not mean becoming technical specialists. It means being sufficiently informed to challenge management and interpret the answers.
The opportunity is significant. Organisations that govern AI well will move faster with confidence, innovate responsibly and build trust with regulators and customers alike. Those that do not will find themselves reacting to incidents, regulatory intervention and reputational damage.
AI is already shaping the trajectory of Nigerian enterprise. Boards have a narrow window to ensure that its adoption is deliberate rather than accidental. Governance, in this context, is not a constraint. It is the mechanism that allows ambition to scale without losing control.
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Michael Irene, CIPM, CIPP(E) certification, is a data and information governance practitioner based in London, United Kingdom. He is also a Fellow of Higher Education Academy, UK, and can be reached via moshoke@yahoo.com; twitter: @moshoke
