In this blog, Data2X unpacks key insights from Episode 4 of AI: Alternative Intelligence, a podcast from Data2X, featuring Dr. Shikoh Gitau. Listen to or watch the full conversation on Spotify or YouTube.
Cultural perspectives shape how societies imagine and engage with artificial intelligence in profound ways. In many Western policy circles, AI is often framed as a looming force—something to be managed, constrained, or even feared. In our latest episode, Dr. Shikoh Gitau offers a strikingly different lens through which to view AI. She grounds her view of AI in how it is already being used to solve real challenges across African countries, rather than treating it as a distant or speculative technology. This practical, solutions-driven approach, she argues, positions Africa not as a follower but as an emerging leader in the global AI landscape. The conversation explores why she believes the continent is poised to define the next era of AI, how communal values shape its development, and what it will take to build regulatory environments that enable—rather than inhibit—innovation.
Africa as a Hub of AI Innovation
Across the continent, Shikoh believes that AI isn’t a novelty or convenience—it’s a response to necessity. While many in wealthier regions view AI as a tool for drafting emails or streamlining workflows, African innovators are developing AI solutions that address life-or-death challenges. Rather than waiting for ideal conditions or external validation, African people and institutions are already building, testing, and applying AI in ways that meet local needs. The energy comes from a mix of research institutions, private sector companies, entrepreneurs, and governments are actively building, testing, and deploying AI tools tailored to local needs. This pace is not driven by hype, but by urgency and opportunity. Increasingly, there is a growing sense that Africa can set its own direction instead of following models built elsewhere.
Communal Approaches to AI
A core theme of the conversation is the contrast between individualistic assumptions embedded into many AI systems and the more community-oriented ways of thinking common in many African societies. Concepts of identity, problem-solving, and value are often understood collectively rather than individually. Instead of measuring intelligence or worth in individual achievement, African worldviews often emphasize shared outcomes and mutual responsibility. Concepts like Ubuntu— “I am because we are”—shape everything from how data is interpreted to how solutions are designed and adopted. Yet this communal ethos is largely missing from AI systems built in individualistic cultures. Models often fail to capture linguistic nuance, cultural references, or collective modes of reasoning. Even widely spoken languages like Swahili are poorly represented, missing context, idioms, and tone. The result is not just technical inaccuracy but a deeper disconnect from the lived experience of African users. Dr. Gitau argues that AI must reflect combined intelligence—knowledge distributed across families, communities, and networks—not just individuals.
Balancing Regulation and Innovation
According to Shikoh, conversations about AI governance in Africa are evolving quickly. While Global debates —particularly in Europe and North America— tend to be dominated by risk and fear-based narratives, African policymakers are increasingly embracing approaches that enable innovation. As governments see practical AI use cases in health, education, and public services, the focus is shifting toward guardrails that support experimentation while managing real risk
Rather than imposing sweeping bans or restrictive laws, many African leaders are pursuing progressive policies that encourage experimentation while addressing real risks. Organizations like Dr. Gitau’s Qhala are playing a central role by training policymakers, convening multi-sector dialogues, and developing practical tools to help governments assess their readiness, identify gaps, and benchmark progress against clear ambitions.
There is momentum behind building enabling environments: access to compute power, better datasets, talent development, and shared infrastructure like Qhala’s Qubit Hub, a communal AI research lab for African innovators. Governments are beginning to view themselves not just as regulators but as customers and champions of local AI ecosystems.
Ultimately, this balance between protection and progress is rooted in pragmatism. When AI can be the difference between life and death, the property is not to slow it down, but to shape it wisely.