Harnessing Generative AI Responsibly: A Guide for African Boards
Generative AI is reshaping industries, economies, and governance at a pace that demands immediate attention. Boards of directors can no longer afford to treat AI as a distant concern, it is here, it is powerful, and it carries both transformative potential and serious risks. African boards, in particular, must act now to understand the ethical implications, governance requirements, and strategic opportunities of this technology. The decisions made today will determine whether organisations harness AI for growth or fall victim to its misuse.
Artificial Intelligence (AI) refers to machines performing tasks that typically require human intelligence, such as decision-making, language processing, and pattern recognition. Generative AI is a subset of AI that creates new content: text, images, audio, video, and even software code based on learnt data. Tools like ChatGPT, Midjourney, and Claude demonstrate how advanced these systems have become. Unlike traditional AI, which analyses data, generative AI produces original outputs, making it both revolutionary and unpredictable.
Generative AI represents a fundamental shift in capability, amplifying human productivity and creativity at an unprecedented scale. This technology doesn’t simply assist with tasks – it transforms entire workflows, enabling organisations to achieve what was previously impossible or prohibitively expensive.
Read also: Creativity: How generative AI is changing the way we create
McKinsey estimates that generative AI could add $2.6 trillion to $4.4 trillion annually to the global economy. In Africa, where labour productivity lags other regions, AI presents a rare opportunity to leapfrog inefficiencies. Businesses are already using AI to automate customer service, reducing response times by up to 90%, generate financial reports in minutes, and analyse market trends with unprecedented speed. A study by PwC found that 54% of companies using AI reported increased productivity. For boards, the implications are clear: organisations ignoring generative AI risk falling behind competitors who leverage it for cost savings, innovation, and customer engagement.
AI can optimise supply chains, personalise marketing, and even predict regulatory risks, areas where African businesses often struggle. The question is not whether to adopt AI, but how to do so responsibly. The strategic advantages are too significant to overlook. AI-driven analytics can uncover hidden business insights, improving decision-making. Chatbots handle up to 80% of routine customer inquiries, freeing human agents for complex issues. In healthcare, AI assists in diagnosing diseases, with some systems matching or exceeding doctor accuracy in detecting conditions like tuberculosis. Financial institutions use AI to detect fraud, reducing losses by up to 30%. For boards, these benefits translate to higher efficiency, reduced operational costs, and competitive differentiation. Yet, the focus must extend beyond gains to the risks, because unchecked AI adoption can lead to reputational damage, legal consequences, and financial losses.
Generative AI is not without flaws. It can produce biased outputs, violate intellectual property laws, and generate harmful misinformation. Deepfakes: AI-generated fake videos or audio are already being used in scams and political manipulation. A 2023 report from Sumsub found a 10x increase in deepfake fraud across industries in just one year. Boards must demand answers from executives on key issues such as bias and fairness, given that AI models trained on skewed data can reinforce discrimination.
In South Africa, predictive policing algorithms have been found to disproportionately target low-income communities, subjecting innocent individuals to increased surveillance and harassment. Similarly, AI-driven credit scoring systems risk excluding low-income users if trained on data reflecting existing socio-economic disparities. A 2019 MIT study found facial recognition systems had error rates up to 34% higher for darker-skinned women, a critical concern given Zimbabwe’s deployment of facial recognition surveillance systems with reported accuracy gaps.
Intellectual property is another concern, as generative AI often uses copyrighted material without permission, exposing companies to lawsuits – Getty Images sued Stability AI for scraping millions of photos without licensing. Misinformation and fraud are rising threats, requiring boards to assess how their organisations verify AI outputs before deployment. A survey of AI platforms used in five African countries showed that 70% of AI platforms in healthcare did not support local languages, creating a significant barrier to trust and engagement.
Accountability is non-negotiable. Boards should require clear AI governance frameworks, including third-party audits, bias testing, and strict usage policies. Executives must demonstrate how they monitor AI risks, not just benefits. This is especially vital in contexts where Western-developed AI systems fail to account for African cultural nuances. For instance, as cited earlier, 70% of AI healthcare platforms surveyed lacked support for local languages, creating barriers to adoption. Educational AI tools designed around Western models may also alienate African students, undermining their effectiveness. Without localization, AI risks eroding cultural identities while deepening inequalities – a reality boards cannot ignore.
Africa faces distinct challenges in AI adoption. Weak digital infrastructure, underdeveloped regulatory frameworks, and low public awareness increase vulnerability to AI misuse. Beyond deepfake scams, autonomous weapons deployed in Libya and unchecked surveillance technologies in other regions underscore the dangers of unregulated AI. Without safeguards, AI could widen inequality by favouring tech-savvy corporations over smaller enterprises. Boards must push for localised AI models, as global tools often neglect African languages, cultures, and business environments. Investing in homegrown solutions, for example, using alternative data for inclusive credit scoring can reduce bias and improve relevance. Regulatory preparedness is also essential. Workforce transition plans are another priority, as AI disrupts jobs. Reskilling initiatives are critical to mitigating backlash and ensuring equitable benefits.
