Through conversations with leaders, policymakers, clients and partners in Nairobi and Kigali, Artefact explores a powerful conviction: Africa is not behind on AI, it is on the verge of a new technological leap.
We are entering an era where AI agents have officially moved from acting as passive assistants to autonomously owning decisions. From incident response to credit approvals, agents now make recommendations and coordinate work across complex enterprise systems. However, this profound shift exposes a critical new bottleneck: context.
Automation. Speed. Scale. Everything luxury has always refused. And yet, AI agents may be the first technology capable of delivering what the industry has promised for decades but never quite managed to completely achieve: knowing every client as if they were your only one.
In just a few years, generative AI has moved from novelty to default front door. Large language models (LLMs) already attract close to 45 billion visits per month, the equivalent of 56% of traditional search volume, which has plateaued for years.
Every year, Pride Month drives campaigns, content, and brand initiatives around the LGBTQIAPN+ cause. It is an important moment for visibility, but the market itself has increasingly been asking: what comes after that? Recent research points to a clear signal: audiences want to see less one-off communication and more concrete structure.
Most CRM and CDP projects I work on in Germany don't fail because of the technology. They fail before the first data pipeline is built. CMOs still largely lead marketing strategy within silos, brand A, channel B, campaign C. But the customer doesn't see silos. They are a business traveller booking an upgrade on Monday. A family buyer hunting a weekend sale on Friday. An insurance policyholder renewing in October. The same person. Three distinct micropersonas. Three different touchpoints. And in the database: a flat profile with a date of birth and a list of all transactions.
While AI promises faster service and nuanced decision-making, much of that potential remains trapped in the experimental sandbox. Discover how leading organizations are moving past simple AI pilots, overcoming workforce adoption hurdles, and embedding agentic workflows into core business processes to drive real, measurable ROI.
I have now met with countless property executives who are genuinely surprised at the low pace of AI adoption inside their own firms, despite having switched on Copilot for all their teams. The pattern is remarkably consistent. A licence is bought, often the cheapest available, little or no real training is provided, and leadership then waits for a transformation that never arrives. Adoption stays low, frustration grows, and the promised step-change is nowhere to be seen. The assumption underneath it all is that putting a powerful tool in people's hands is the same as changing how they work.
The automotive industry is in the grip of a paradox. OEMs are spending billions to develop vehicles that can sense their environment, reason about risk, and act without human intervention — yet the organisations building those vehicles still rely on annual planning cycles, manual decision chains, and systems that were not designed to communicate with each other. Vehicles are becoming autonomous faster than the enterprises that produce them. This mismatch is no longer a strategic curiosity. In an industry facing simultaneous pressure from electrification, software-defined vehicle architectures, compressed margins, and intensifying competition from Chinese manufacturers who are outpacing Western OEMs on both cost and AI integration, decision latency has become a structural liability.
