The real reason African AI projects fail and how to fix it
By Fadoyin Taiwo
In 2021, a Nigerian-founded health-tech startup called Ubenwa made headlines for building an AI system that detects birth asphyxia from the crying of a baby. The prototype impressed global investors and even made it to the semifinals of the IBM Watson AI XPRIZE. Three years later, there has been no public evidence of continent-wide hospital deployment. The problem here was not lack of vision but we could say it was the difficulty of integrating AI into real-world health systems that lack the digital infrastructure to support it.
This story is not unique. Across Africa, from Lagos to Kigali, promising AI pilots light up pitch decks, win grants and trend on social media but months later, they quietly fade out. A recent State of AI in Africa Report (2024) found Africa is home to over 2,400 AI companies with 40% of them founded between 2017 and now. The number of AI startups in Africa has grown by over 70% since 2019 but fewer than 12% made it beyond the pilot stage. From a global view, RAND Corporation (2024) estimates that close to 80% of AI projects fail before reaching production but in Africa, the figure is higher as a result of a perfect storm of infrastructural, financial and operational challenges.
So why does Africa’s AI boom keep getting trapped in pilot purgatory? Why do brilliant prototypes especially ones solving real problems like crop failure, disease detection or traffic management hardly survive long enough to serve the people that they are meant to help?
The Promise That Stops at Pilot
In 2022, Zindi, Africa’s largest data science competition platform hosted crop-yield competitions using satellite data whose winners posted strong leaderboard performance but few have translated into farmer-facing production tools. Why? This is because deploying them required constant internet access, updated soil data and integration with existing agricultural advisory systems which are luxuries that many rural regions do not have.
The story repeats itself across sectors. Several promising AI pilots across Africa have shown technical success but they still struggle to reach real-world use. For instance, Ghana’s miLab digital microscope achieved strong malaria detection results.while Nigeria’s GiveDirectly flood-forecast programme effectively triggered early cash assistance. But both point out a strong challenge which is scaling from pilot to production that needs sustained data infrastructure, integration with public systems and long-term operational investment. They look good in annual reports and at tech conferences but production-grade engineering which is the unglamorous process of making AI systems run reliably and seamlessly for thousands of users every day, under messy real-world conditions, is where most African projects collapse.
The Missing Link: Engineering for Production
Let’s be clear here. Africa does not lack ideas or talent. Nigerian engineers build autonomous drones; Kenyan developers write code for global AI startups; South African researchers contribute to Google’s DeepMind papers. This tells us that the problem is not creativity but continuity.
In Lagos, an AI startup founder once said to me, “We can train models, but we can’t afford to keep them alive. Hosting costs for GPU servers can run into multiple thousands of dollars per month for continuous usage (depending on provider and instance type) and frequent internet outages can disrupt pipelines in both are major real operational constraints. Data storage is usually outsourced to the cloud which raises governance and compliance issues that stall partnerships with banks or hospitals. This is what separates proof of concept (POC) from production system. Building an AI prototype means training a model. Building an AI product means maintaining it, monitoring it, retraining it when the data changes, handling latency, user feedback and scaling. It is what the industry calls MLOps (machine learning operations).
Consultancies and research groups report that lack of engineering readiness, governance and data quality are primary reasons for AI failure. Recent research from IDC, undertaken in partnership with Lenovo in found that 88% of observed POCs do not make the cut to widescale deployment. For every 33 AI POCs a company launched, only four graduated to production. Africa magnifies this problem because startups operate in fragmented data ecosystems, governments lack open data policies and there is limited investment in operational infrastructure. So when an African AI pilot fails, it is not always because the algorithm does not work but because Africa is not engineering for production.
The Three Hidden Barriers
One of the hidden barriers is the data dilemma. Most African AI systems start with small, clean and labelled datasets. But once in the wild, data becomes messy. A chatbot trained on English-language customer service queries will fail when users switch to Pidgin or Yoruba. A traffic prediction model in Nairobi breaks when a new road opens that was not in the training data. According to Open Data Inventory (ODIN) (2024/2025), fewer than 30% of African countries have open government data platforms and even fewer update them regularly. Without consistent and interoperable data, AI projects quickly rot.
Also, another problem is the infrastructure and MLOps gap. Even when data is available, maintaining an AI system is expensive. Most startups depend on AWS or Google Cloud by paying dollar-denominated fees in economies where currencies are volatile. Local data centres are growing but slowly. Few startups have dedicated DevOps or MLOps engineers who are the professionals that make sure models stay live, retrained and performant. A 2023 Artificial Intelligence for Development in Africa (AI4D) Africa study found that many hubs focus on training and incubation but far fewer provide shared GPU clusters, deployment platforms or long-term hosting. Surveys of hubs show infrastructure and deployment support is limited relative to mentorship and training. Pilots are designed to impress investors not sustain uptime.
Furthermore, donor-funded AI projects often measure success by “launch” rather than longevity and this is where the incentive trap comes into play. Once a pilot meets its short-term target, funding ends and the system collapses. Governments, too, love to announce innovation partnerships but hardly budget for maintenance. As one Rwandan technologist said during the 2024 Transform Africa Summit, “We keep building demos for press conferences, not systems for citizens.”
The Rival Interpretation: Maybe It is not About Engineering Alone
Of course, not everyone agrees that the problem is purely technical. Some critics argue that Africa’s AI bottleneck is political not engineering-based. They point to issues like foreign data dependency, limited local ownership of models and the “digital colonialism” of Western firms extracting African data to train global systems. Others say the real problem is social context, building tech solutions without sound understanding of user behaviour or regulatory readiness. For instance, deploying AI in healthcare raises ethical and privacy concerns that many countries have not legislated for. Both critiques are valid and correct. Engineering alone will not fix power cuts, data sovereignty or policy vacuum but even with the best governance, without production-ready systems, AI still remains a concept not a service. Africa needs both structural reform and engineering depth.
How to Break the Pilot Curse
So how do we move from prototypes to production? The fixes are not abstract but they are painfully specific. Firstly, investors and donors should demand production-readiness plans before funding. A good rule of thumb of 20% of project budgets should go to operations and infrastructure. Instead of five new pilots, fund one that can scale for 12 months. Kenya’s AI Centre of Excellence took this route in 2024 by focusing on a few production-grade AI solutions in logistics and agriculture backed by the national ICT Authority. The result is early initiatives moving toward operational deployment.
Also, African tech hubs could pool resources to create shared AI deployment platforms offering hosting, monitoring and compliance as a service. Similar models exist in India where AI4Bharat provides open datasets and ready-to-deploy APIs in local languages. Imagine if Lagos’ Co-Creation Hub or Nairobi’s iHub offered shared GPU clusters and deployment support for AI startups, that could cut time-to-market and reduce the operational burden.
Furthermore, universities and tech training programmes should go beyond data science bootcamps to teach AI operations, system integration and data governance. There is a global shortage of MLOps engineers but Africa can turn that into an opportunity. In addition, public agencies should procure AI services like infrastructure not research experiments. Governments could fund “AI scale-up grants” that it tied to deployment milestones not just demo days. Imagine if the Nigerian Ministry of Health funded not just a diagnostic AI pilot but its integration into 50 hospitals’ IT systems. That’s how you create real impact.
The Bigger Picture for Africa
Africa is bursting with AI promise. From Ghana’s speech recognition startups to South Africa’s mining automation and Nigeria’s fintech risk scoring, the creative energy is undeniable. But until we build the plumbing which are the data pipelines, monitoring systems, local hosting and engineering culture, we will keep replaying the same story: prototypes that dazzle but do not deliver.
Think of it this way. What if Africa treated every AI pilot as a promise to its citizens. I mean like a promise to sustain, improve and deliver not just to experiment? Prototypes prove possibility and production proves usefulness. And until we engineer for production, Africa’s AI boom will remain more of a dream than a daily reality. So the next time you hear of a “revolutionary AI project,” ask: Will it still be working a year from now? Because that is the question that will decide whether Africa’s AI future is built to last or just built to impress.
