AfriPulse
Artificial Intelligence (AI) is becoming the engine behind Africa’s digital economy. From mobile lending platforms to health diagnostics and agricultural analytics, AI is reshaping business models across the continent. But behind the promise of efficiency and growth lies a reality that boards cannot afford to ignore: AI’s risks are often hidden, systemic, and capable of undermining trust overnight.
For African boards, the task is to keep pace with innovation while ensuring the organisation has the foresight and guardrails to manage risks before they crystallise into crises.
The nature of hidden risks
AI risks rarely announce themselves. They arise from the way data is collected, the assumptions embedded in algorithms, and the evolving complexity of systems that even their creators sometimes struggle to explain. In Africa, these risks are magnified by uneven regulation, limited local datasets, and governance gaps.
Hidden risks extend across domains:
Healthcare: Diagnostic tools trained on foreign datasets may miss diseases more prevalent in African populations.
Recruitment: Algorithms screening CVs could unintentionally disadvantage women, rural applicants, or candidates from under-represented regions.
Financial Services: Automated credit scoring may perpetuate systemic bias, excluding large segments of informal workers.
Customer Service: Poorly monitored chatbots can spread misinformation or mishandle complaints, eroding customer confidence at scale.
Three categories of AI risks boards must surface
1. Data risks
AI is only as reliable as the data it consumes. In many African contexts, datasets are incomplete, inconsistent, or unrepresentative. This creates blind spots that undermine decision-making and trust.
Questions for boards to ask:
Do we have independent assurance on the integrity, accuracy, and fairness of the data feeding our AI systems?
If challenged publicly tomorrow, could we defend the sources and quality of our data with confidence?
Have we tested our AI systems beyond imported benchmarks to ensure they reflect Africa’s diversity and the cultural complexity within each country?
2. Ethical and social risks
AI can silently amplify bias and exclusion. A bank may see efficiency gains from automated credit scoring, but what if the model systematically excludes women or small businesses in rural areas? The reputational damage could outweigh any short-term savings.
Questions for boards to ask:
What would our stakeholders say if our AI was proven to disadvantage vulnerable groups – would we be seen as inclusive innovators or irresponsible exploiters?
How are we testing for fairness, and who has authority to stop deployment if ethical red flags emerge?
If a regulator audits or a journalist investigates our AI today, what story would the evidence tell about our values?
3. Operational and strategic risks
AI failures are not theoretical; they can stop business in its tracks. A fraud-detection tool that freezes legitimate accounts or a predictive maintenance system that misses critical failures can damage both customer trust and business continuity.
Questions for boards to ask:
Do we have a clear chain of accountability if an AI-driven system fails and harms customers?
How resilient are our systems – could we switch off an AI tool without crippling operations?
Are we deploying AI at a pace our governance structures can realistically oversee, or are we chasing efficiency at the cost of control?
The board’s role in surfacing the hidden
Executives will often frame AI in terms of technical feasibility or cost savings. Boards must shift the discussion to strategic and ethical oversight. The core question is not “Can we build/buy this?” but “Should we build/buy it, and under what guardrails?”
Practical steps for boards include:
Mandate AI Impact Assessments – Ensure every major AI initiative is tested for ethical, legal, and reputational impact.
Embed Oversight – Integrate AI governance into risk or audit committees, or establish a dedicated technology oversight structure.
Demand Transparent Metrics – Move beyond compliance checklists. Require forward-looking dashboards on AI risks, incidents, and strategic alignment.
Invest in Board Learning – Commit directors to ongoing education in AI governance, ensuring fluency to challenge management effectively.
Run Crisis Simulations – Use tabletop exercises to stress-test responses to AI failures, from biased outcomes to data leaks.
Africa’s digital transformation is accelerating, but governance capacity often lags behind innovation. When AI risks surface, they do so suddenly and publicly, damaging reputation, revenues, and stakeholder trust.
Boards that anticipate hidden risks and demand accountability will not only safeguard their organisations but also differentiate themselves as trusted leaders in the digital economy. In an environment where trust is fragile and competition fierce, that foresight is not optional; it is strategic survival.
For African directors, the real question is this: will you wait for AI risks to expose themselves, or will you demand visibility before they become tomorrow’s headlines?
Amaka Ibeji is a Boardroom Certified Qualified Technology Expert and a Digital Trust Visionary. She is the founder of PALS Hub, a digital trust and assurance company, Amaka coaches and consults with individuals and companies navigating careers or practices in privacy and AI governance. Connect with her on linkedin: amakai or email [email protected]
Source: Businessday.ng | Read the Full Story…
