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Earlier in my media and creative agency days in Kenya, software proficiency was everything. Every aspiring designer or communication officer enrolled in college courses to master Photoshop, Illustrator, or the full Microsoft Office suite. Competency in tools like Excel, QuickBooks, or CorelDRAW was often a non-negotiable requirement for employment. Even today, many job adverts still list these as core qualifications.
But something profound has shifted.
AI strategies developed just a year ago, most of them focused on reskilling and upskilling, are already becoming outdated before they can be fully implemented. The reason is simple. The world we were preparing people for is evolving faster than our institutions can adapt. We were training people to work with AI tools. But now, AI doesn’t just assist. It performs. We are entering a world where briefing someone to complete a task may itself become inefficient or even unnecessary. This isn’t about eliminating collaboration; it’s about the emergence of agents—autonomous digital systems capable of executing full workflows with minimal human input. And at incredibly low cost, sometimes even for free. From a business perspective, the logic is compelling.
Kenya, like many developing economies, is not just experiencing a digital disruption. It is facing a philosophical one. We are not merely adopting new AI tools. We are re-evaluating the role of the human in the entire value chain. The many jobs promised from classrooms to political platforms may soon become a difficult conversation. If technology can step in and do what we thought only people could, where does that leave our social and economic contracts? All is not lost, but we must become strategic—and quickly.
This global urgency was echoed at Dubai AI Week 2024. Compared to last year’s excitement about generative tools like ChatGPT, Midjourney, and DALL·E, the conversation this year had shifted toward execution and integration. The spotlight was firmly on AI agents and how they are not just supporting businesses, but increasingly running them. Institutional structures, as we know them, will inevitably become leaner.
The key insight from the event was unmistakable. Artificial intelligence is no longer just about helping us think more effectively. It is now about doing the work. The rise of AI agents represents a clear shift from tools that rely on constant prompting to systems that act autonomously. Businesses now seek solutions that can deliver outcomes without human oversight. This raises a critical question. What happens to those who believed prompt engineering was the next big thing? Ironically, just as training programs on prompt mastery were beginning to emerge, the conversation had already moved forward.
This shift is evident in the language used by industry leaders. Terms like “agentic systems,” “co-pilots,” and “AI-native organisations” are increasingly common. These reflect a fundamental change in how organisations are integrating AI. No longer seen as a plug-in or add-on, AI is becoming a core operational layer, much like a fully embedded team member. Success today is defined less by prompt engineering and more by systems design, process orchestration, and intelligent delegation.
Another major takeaway from this year’s discussions was the redefinition of AI investment priorities. While online platforms continue to celebrate automation through scripts, filters, or chatbots, business owners are focusing on return-on-investment-driven applications. They want AI that simplifies billing, accelerates recruitment, secures digital assets, and enhances client onboarding. These are now mission-critical, and expectations are also rising.
Clients do not want tools they need to learn. Instead they want systems that can learn and adapt to their workflows.
This shift is even more urgent in highly regulated industries. In aviation, for example, the focus is on reducing turnaround times, improving passenger experiences, and using biometric technology for security. In public administration, the attention is on AI-powered citizen services, automated case management, and transparent, accountable processes. The fascination with novelty is fading. What matters now is measurable, strategic impact.
While AI adoption continues to accelerate, regulatory frameworks are struggling to keep pace. Countries like the UAE are leading with national AI strategies and ambitious preparedness plans. But globally, consensus is still lacking. Issues such as content ownership, intellectual property, and ethical use remain unresolved. The controversy over AI-generated artwork that mimics the Studio Ghibli style is just one example of the complexities emerging around algorithmic creativity. These legal and moral questions are beginning to shape the boundaries of what AI will be permitted to do across industries.
To understand how AI is being adopted, it helps to examine the different user groups engaging with it. Solopreneurs are focused on simplicity and affordability. They want tools that help save time and reduce costs—writing assistants, task schedulers, and templated automations. However, many still struggle to fully grasp AI’s long-term value. Small businesses, on the other hand, are motivated by fear of being outpaced. They are embracing tools like CRM agents, automated onboarding systems, and customer support bots to remain competitive. Corporates are in an exploratory phase, trying to understand how to integrate AI without disrupting existing systems. In many cases, they confuse AI with traditional IT functions, which highlights a critical knowledge gap that must be addressed. Governments are taking a long-term view. For them, AI represents an opportunity to scale services, improve citizen engagement, and redefine digital governance. They are thinking in terms of strategies, KPIs, and national priorities.
Each of these segments operates at a different level of digital maturity. They also have different budgets and expectations. Crafting meaningful AI solutions requires understanding their distinct needs and meeting them where they are—not where tech culture assumes they should be.
Among the most powerful frameworks presented at the event was the “Worlds and Machines” model. The idea is simple. Every business is a world, shaped by its values, culture, and core outcomes. Surrounding that world is a system of machines—AI agents and automated workflows that help it function, grow, and adapt. This model transforms how we think about scaling.
The process follows a clear cycle. Strangers become leads, leads become customers, customers become clients, and clients become case studies. Those case studies then feed back into the system to attract and convert the next cycle. In this ecosystem, AI is not a tool. It is a worker. A responsive, scalable, and continuously improving member of the team that enhances every part of the business operation.
The deeper shift, however, is not about machines. It is about mindset. The most successful innovators in the emerging AI economy will not simply be coders or designers. They will be systems thinkers—people who understand how to connect the right technologies with the right processes to solve the right problems. These individuals and organisations will redefine value around outcomes instead of effort. They will make branding personal and authentic. And they will treat AI not as a trend but as the engine room of modern work.
Edward Mwasi is a Media Industry Strategy and Innovation Consultant at CBiT. He writes on AI, development, and systems innovation in Africa’s business and public sectors. Contact: edwardmwasi@cbit.co.ke
