How to work in artificial intelligence? You need to master mathematical and statistical fundamentals, learn Python programming, and develop solid business acumen. In Morocco and French-speaking Africa, opportunities are growing with the emergence of players like ABA Technology and the Google-AfCFTA program. AI literacy now forms the essential foundation.
You have been searching for AI profiles for six months. Your recruiters find no one. Or worse: you hire impeccable CVs that deliver nothing in production. This is the crisis of AI experts reported by the Moroccan press. Companies want to integrate AI into their decision-making processes. They do not know where to start or how to train their teams. Between the announcement of ABA Technology and its sovereign AI made in Morocco, and the observation of uneven adoption according to local experts, the gap is widening between promises and operational reality.
Stop hunting unicorns
Do not post a job offer requiring a PhD in mathematics, ten years of experience, and mastery of fourteen languages. Today’s AI requires specialists, but above all hybrid professionals. As I explained in my analysis of AI jobs, value lies at the intersection of technical skills and business domain.
The market now distinguishes the AI researcher, who pushes theoretical boundaries, from the MLops engineer who industrializes, and from the AI product manager who translates needs. Each path requires a different profile.
The three paths into AI
First path: pure AI engineer, capable of building models from scratch. Second path: data architect, who prepares and structures information. Third path, currently the most promising: the business professional trained in AI literacy. This profile does not necessarily code. They identify use cases, translate business needs into technical specifications, and drive compliance.
As I analyzed in my article on jobs that will survive AI, professional resilience comes from this hybridization.
Invest in internal upskilling
Sending three employees to follow a generic MOOC is not enough. AI training requires immersion. Serious platforms now offer certified programs, often co-built with companies. In Morocco, the emergence of ABA Technology with its sovereign AI made in Morocco creates a specific training ecosystem. The Google-AfCFTA program targeting 7,500 African SMEs shows the scale of the movement.
But beware: certification only matters if accompanied by real-world application. I always favor programs that combine theory with process redesign using company data.
I have built a 6-dimensional diagnostic framework to assess exactly your teams’ maturity. Download the AI Board Pack 2026.
Practice on real cases immediately
AI is not learned in the abstract. Take a concrete problem from your company. A candidate matching process that is too slow. An opaque logistics chain. Have your teams work on this real data. The classic mistake is waiting for perfect mastery before acting. You learn by deploying.
Install AI governance from the start
Uncontrolled AI represents your greatest risk. Before even hiring, define who makes decisions, who assumes responsibility and accountability, and what guardrails apply. This AI governance involves defining who validates models, who monitors compliance, and how algorithmic decisions are documented.
As I have observed among my clients between Casablanca and Brussels, AI projects rarely fail for technical reasons. They fail due to the absence of a clear operating model.
The pitfalls that block AI recruitment
First pitfall: believing AI concerns only IT professionals. Your salespeople, your management controllers, your HR managers must develop AI literacy. Second pitfall: neglecting compliance. Given the risks associated with AI highlighted by experts, the absence of a methodological framework exposes you to serious consequences. Third pitfall: recruiting technical profiles without testing business understanding. An excellent data scientist who does not understand your sector remains inoperable.
Fourth pitfall: ignoring change management. You can have the best technical roadmap; if your teams do not adopt the tool, the project dies.
The expected result
A team capable of deploying use cases in production within reasonable timeframes. A drastic reduction in recruitment time for these rare profiles. And above all, measurable value capture from your processes. Companies structuring this approach today are building tomorrow’s competitive advantage.
If you are an HR Director or CEO looking to structure your AI approach, request a free diagnostic.
FAQ
Do you need to know how to code to work in artificial intelligence?
Not systematically. AI solution architects and AI project managers must understand technical issues without necessarily writing code. Business understanding and the ability to translate a business problem into technical specifications are often worth more than mastering Python.
What training should you follow in Morocco for AI?
Online certified platforms offer accessible and recognized programs. The essential remains practical application on concrete projects, ideally within local players like ABA Technology or through regional programs such as the Google-AfCFTA initiative.
How do I assess the AI maturity of my existing teams?
Start with a technical and business skills audit. Identify who already manipulates complex data. Test understanding of ethical issues and AI-related risks. Targeted upskilling on identified gaps delivers more than generic training.