As the global climate crisis escalates, the urgency for effective, scalable solutions has never been greater. Technologies like artificial intelligence (AI) and machine learning (ML) are rapidly becoming cornerstones of global climate action. They are helping researchers and innovators model climate systems, optimise renewable energy, track deforestation, and forecast environmental hazards with precision. In Africa, where climate impacts are felt acutely and adaptation capacity remains uneven, these technologies hold transformative potential. Yet, despite the accelerating adoption of climate technology, one foundational element is conspicuously absent from the current discourse, which is mentorship.
Mentorship in climate tech is not just a ‘nice to have’, it is an indispensable mechanism for knowledge transfer, ethical grounding, and sustainable innovation. Unlike other fields where software alone can drive disruption, climate technology intersects with complex ecological, social, and political systems. Mentorship bridges the gap between theoretical training and contextual understanding. It guides young innovators through questions that cannot be answered by code alone: How do you ensure that AI-based wildfire prediction respects Indigenous land rights? What are the limitations of using satellite data in communities with weak digital infrastructure? These are not lessons from tutorials, they are insights gained through dialogue with experienced professionals.
In the African context, the challenge is compounded by unequal access to both resources and networks. Many early-career data scientists and climate tech enthusiasts on the continent are eager and talented but operate in ecosystems that lack structured mentorship pathways. While global initiatives like Climate Change AI and Zindi Africa have begun to create collaborative spaces where learning occurs, these efforts remain fragmented and underfunded. What is needed is an institutional shift — an intentional design of mentorship structures embedded within the educational, corporate, and research sectors.
Ugochukwu Akajiaku, a leading voice in African climate technology, aptly noted in a TechCabal feature that “AI and geospatial technology are not luxuries, but necessities for Africa’s climate resilience strategy.” He emphasised that Africa’s unique environmental challenges demand localised models, spatial data democratisation, and the cultivation of a new generation of geospatially aware, tech-driven environmental stewards. But, as Akajiaku also alludes, without mentorship, this next generation cannot thrive. Innovation alone cannot compensate for the absence of guidance. Without mentors, AI risks becoming another imported solution that fails to take root in African soil.
Indeed, the issue is not merely technical. It is systemic. African universities and technical institutions often do not offer practical pathways from the classroom to the field. Students may become proficient in Python or TensorFlow, yet graduate with little understanding of real-world climate dynamics or the operational needs of renewable energy firms and conservation organisations. To bridge this divide, academic-industry partnerships must become standard practice. Internships, field-based projects, and co-supervised theses can expose students to pressing environmental challenges.
Furthermore, mentorship should not be restricted to senior professionals dispensing wisdom. A more expansive model is required for one that includes peer-to-peer mentorship, reverse mentoring, and cross-disciplinary exchange. This multidirectional mentorship model fosters resilience, creativity, and humility qualities essential for tackling complex climate challenges.
A compelling example of such cross-disciplinary mentorship lies in the marine blue economy space. In Nigeria, the integration of AI into marine ecosystem management, fisheries optimisation, and ocean health monitoring is growing. Omabuwa Mene-Ejegi, writing in The Nation, highlighted the transformative potential of AI in harnessing Nigeria’s blue economy. He argued that real-time oceanographic data, AI-enhanced vessel monitoring systems, and marine spatial planning tools could unlock billions in sustainable marine revenue.
But Mene-Ejegi was quick to add that these tools require more than technological know-how; they demand domain expertise, policy awareness, and collaborative design, a mentorship ecosystem where technologists and marine scientists learn from each other. Without such mentorship, he warns, AI applications risk reinforcing inefficiencies or triggering unintended ecological harm.
Open mentorship networks are another underutilised avenue for growth. Digital platforms like GitHub, LinkedIn, and Kaggle already house vibrant technical communities. These can be repurposed into structured mentorship spaces where experienced climate modellers, data scientists, and domain experts from around the world are matched with aspiring innovators, especially in the Global South. More importantly, they create a sense of community and accountability as a psychological safety net for those navigating the uncertainties of early-career innovation.
The value of inclusive communities of practice cannot be overstated either. Africa has seen a surge in climate-focused hackathons, innovation challenges, and open-source mapping projects. While these are often seen as competition-driven, they can be reimagined as mentorship-rich environments. By embedding mentorship into the design of these events through project pairing, group coaching, and feedback sessions, they become not just platforms for recognition but for growth.
To make this shift a reality, climate investors and donors must be willing to fund mentorship as infrastructure. It cannot remain a peripheral activity or an informal side effort. Mentorship should be built into grant frameworks, startup incubators, university curricula, and fellowship programmes. Metrics for mentorship quality, diversity, and impact should be tracked alongside metrics for technical performance and scalability. Mentorship, in this context, is not charity; it is a long-term investment in human capital, without which no climate tech ecosystem can sustain itself.
Moreover, policy frameworks must also reflect this priority. National AI strategies and climate adaptation plans in Africa should include explicit language around capacity building and mentorship. Ministries of Environment, Education, and Science and Technology can collaborate to design climate-tech fellowship programmes, faculty exchange schemes, and national mentorship registries.
As the world races to achieve net-zero goals and adapt to increasingly volatile climate patterns, AI will undoubtedly play a pivotal role. But AI is only as impactful as the people who develop and deploy it. If we are to meet the goals of the Paris Agreement and build resilience across climate-vulnerable regions, we must prioritise the mentorship of the human minds behind the machine models.
In the end, mentorship is the keystone that binds technical capacity to transformative impact. It is what turns talent into leadership, models into movements, and pilots into platforms that scale. The African climate tech revolution will not be driven by algorithms alone. It will be driven by the people who shape them and those who shape the people. That shaping, now more than ever, begins with mentorship.