AI at AFCON: When Artificial Intelligence Works Without the Headlines
- Chris Howell
- Dec 26, 2025
- 6 min read
When people talk about artificial intelligence in sport, the same places come up again and again: Silicon Valley startups, Chinese tech giants, established hyperscalers like Amazon and Microsoft, and Premier League clubs with analytics departments the size of small companies. Headlines gravitate towards billion‑dollar investments, cutting‑edge research labs, and clubs with bespoke analytics and decision‑support systems built in‑house.
Africa rarely features in that conversation — which matters, because it shapes how we think about where AI lessons come from, whose experience is valued, and what kinds of adoption stories get overlooked.
So with the Africa Cup of Nations (AFCON) underway, it’s a fair question to ask: is AI actually being used in African football — or is this one area where the technology narrative simply doesn’t apply? And if it is being used, does it look anything like the AI stories we usually hear about in Europe, the United States, or China?
The answer is more interesting — and more instructive — than a simple yes or no.
AI without the hype
At AFCON level, artificial intelligence doesn’t arrive wrapped in buzzwords, keynote speeches, or big announcements about transformation. It shows up quietly, embedded inside systems designed to solve practical problems on matchday.
Video Assistant Referee (VAR), semi‑automated offside detection, connected match balls, player‑tracking cameras, and performance analysis platforms are now standard parts of modern international tournaments — and AFCON is no exception. These tools are not experimental add‑ons; they are operational systems that referees, coaches, and analysts rely on in real time.
Under the hood, they rely on computer vision and real‑time data processing — systems that help software see what’s happening on the pitch and respond quickly enough to support human decisions. In other words, applied AI. Not moonshot research or speculative models, but technology that helps referees make faster decisions, reduces ambiguity, and gives teams clearer insight into what is actually happening on the pitch.
This is a recurring theme across African football: when AI is used, it is used to support decisions, not to put on a technological show. The value lies in reliability and clarity, not novelty.
Performance analysis and matchday decisions
Behind the scenes, many national teams competing at AFCON now use GPS wearables, video analysis software, and statistical breakdowns to monitor player load, fatigue, and tactical shape across matches and training sessions.
The data collected isn’t especially exotic — distances covered, sprint intensity, acceleration, positional heatmaps, pressing patterns, and recovery metrics — but the value comes from how it’s interpreted and applied. Coaches and analysts use this information to decide when to rotate players, how aggressively to press, whether to adjust defensive lines, and where opponents are most vulnerable.
In practice, this kind of AI‑assisted analysis mirrors what happens in European leagues, just at a different scale. The tools are often licensed rather than built in‑house, and analytics teams tend to be smaller, but the underlying principle is identical: use data to reduce guesswork and improve decision‑making.
That principle matters far more than where the servers are located or how fashionable the technology sounds.
Scouting: where African football is already data‑rich
One area where AI has long intersected with African football is talent identification and scouting — another clear example of this decision‑support approach. African leagues and international tournaments are among the most closely watched talent pools in world football, and with good reason. AFCON regularly features players competing at the highest levels of the game, including figures such as Mohamed Salah, Nicolas Jackson, Achraf Hakimi, and Victor Osimhen. As a result, European clubs routinely use AI‑assisted scouting platforms to analyse match footage, event data, and player development trends across the continent — often long before players attract sustained mainstream media attention.
In many cases, the AI systems are not operated directly by African clubs themselves. Instead, decisions about African football are increasingly data‑driven elsewhere. That distinction is important. AI influence does not require local model training or domestic data centres to be real or impactful. What matters is that data shapes decisions — and in this area, African football is already deeply embedded in global analytics pipelines.
A mobile‑first fan experience
For fans, AI at AFCON looks very different from what you might see at a Champions League final or a major European derby.
Africa is a mobile‑first continent. Smartphones are often the primary screen for watching, replaying, debating, and sharing football. As a result, AI features aimed at fans tend to focus on mobile experiences rather than stadium‑only gimmicks: automated highlights, player recognition, multilingual chatbots for schedules and results, and personalised match summaries that can be shared instantly.
These systems prioritise accessibility over spectacle. They are designed to work on everyday devices, across multiple languages, and in environments where bandwidth, connectivity, and infrastructure vary widely. In that sense, they are optimised for real‑world conditions rather than ideal ones.
Once again, this is AI shaped by context.
Where does the processing happen?
A common assumption is that any AI used at AFCON must be powered entirely by data centres in Europe or elsewhere.
The reality is more nuanced.
Real‑time systems — such as VAR, offside detection, and in‑stadium tracking — require extremely low latency. That means local processing, often within stadium infrastructure itself, where milliseconds matter. Other workloads, such as historical analysis, media distribution, fan engagement platforms, and large‑scale data storage, are typically handled through regional or global cloud platforms.
Africa’s data centre capacity is still uneven, with South Africa dominating overall capacity. However, investment is accelerating across countries such as Nigeria, Kenya, Egypt, and Morocco — which matters because it directly affects where AI can run locally, how quickly systems respond, and how resilient they are in real‑world conditions. For now, AFCON operates in a hybrid model: some processing on the ground, some via global cloud networks. From a practical AI perspective, that hybrid approach is entirely normal and mirrors how many organisations around the world deploy AI today.
Why Africa rarely appears in AI narratives
So why does African football so rarely feature in discussions about AI in sport?
Part of the answer lies in media bias. AI stories tend to follow investment volumes, valuations, and research breakthroughs — areas where the United States and China dominate headlines, and where Europe positions itself as a regulatory counterweight. Africa’s AI story is quieter, more applied, and less tied to venture capital narratives, which makes it easier to overlook.
But that doesn’t make it less real or less instructive.
AFCON shows what AI looks like when the goal isn’t to impress investors or chase headlines, but to run a complex event, support officials, inform coaches, and engage fans under real‑world constraints. It is a reminder that usefulness often matters more than novelty.
This isn’t just sport
It would be a mistake to view AFCON as a special case — a one‑off example of AI usage confined to elite sport. Instead, it works best as a lens: a visible, concrete way to understand how AI is being adopted more broadly under real‑world constraints. In reality, what’s happening around the tournament reflects a broader pattern in how artificial intelligence is being adopted across Africa.
In many African contexts, AI adoption is use‑case led rather than research‑led. The starting point is not “What model can we build?” but “What problem needs solving?” That mindset shapes everything from mobile banking and fraud detection to logistics, agriculture, healthcare — and, as AFCON shows, sport.
Constraints play a defining role. Infrastructure is uneven, budgets are finite, and systems must work reliably in environments where connectivity, power, and hardware can’t be taken for granted. As a result, African AI deployments tend to prioritise robustness, simplicity, and measurable impact over novelty or architectural elegance.
Mobile‑first design is another recurring theme. Just as AFCON’s fan‑facing AI tools are optimised for smartphones rather than stadium screens, many AI applications across the continent are built with the assumption that the phone is the primary interface. That reality encourages lightweight models, hybrid processing, and on‑device intelligence where possible — approaches that are increasingly relevant worldwide.
Perhaps most importantly, visibility does not equal maturity. Much of Africa’s AI usage happens quietly, embedded inside systems that do not advertise themselves as “AI‑powered” at all. Like the technology at AFCON, it works in the background, shaping decisions without demanding attention.
Seen through that lens, AFCON is not an outlier. It is a microcosm — a clear, observable example of how AI is already being used across the continent: pragmatically, selectively, and in service of real‑world outcomes.
Beyond football
There’s a lesson here that extends well beyond sport.
Most organisations don’t need cutting‑edge models, bespoke architectures, or massive compute budgets. They need tools that help people make better decisions with the data they already have — reliably, consistently, and in context.
That is exactly how AI is being used at AFCON.
Not loudly. Not perfectly. But effectively.
Beyond the pitch - If this perspective on AI resonates — grounded, context-aware, and focused on decision support rather than hype — the AI Readiness Consultation is a practical next step.
It’s designed to help teams and organisations assess where AI can genuinely add value, where it might not, and what readiness really looks like in their specific environment.