We do not have a tech talent problem, we have a talent identification problem

An opinion piece by Ene Ojaide, April 15, 2026

Across Africa’s technology ecosystem, the narrative of a persistent “talent shortage” has become widely accepted. Founders, hiring managers, and policymakers frequently cite the lack of qualified professionals as a primary constraint to growth. However, this framing is increasingly flawed. The issue is not the absence of talent, but the systemic failure to accurately identify and deploy it.

At the core of this problem is an over-reliance on traditional credentials as signals of competence. Degrees, certifications, and elite institutional affiliations continue to dominate hiring decisions, serving as proxies for capability. While these markers may offer some level of standardization, they are inherently exclusionary and often fail to reflect actual skill, adaptability, or problem-solving capacity.

This approach is particularly limiting in emerging markets, where access to formal education pathways is uneven. A significant portion of capable individuals develop skills through alternative means self-learning, informal networks, hands-on experience, and digital communities. Yet, because these pathways do not conform to conventional hiring filters, such individuals remain systematically overlooked.

The consequence is a paradox: organizations report talent shortages while a large pool of capable individuals remains underutilized. This mismatch is not incidental; it is the direct result of outdated identification systems that prioritize pedigree over performance potential.

The economic implications of this inefficiency are substantial. Misidentifying talent leads to suboptimal hiring, increased turnover, reduced productivity, and slower innovation cycles. At scale, it constrains the overall growth of the digital economy by limiting the effective deployment of human capital.

A structural shift is now emerging, driven by advancements in data and artificial intelligence. New models of talent identification are moving away from static credentials toward dynamic, capability-based assessment. These systems analyze a broader range of variables, including cognitive strengths, behavioral patterns, interests, and contextual factors, to determine alignment between individuals and roles.

An example of this shift is Luma, a product developed by Thinkdata Nexus. Luma functions as an AI-powered career decision system designed to replace guesswork with structured, data-informed alignment. Rather than relying on CVs or job titles, it evaluates an individual’s capabilities and context to map them to suitable career pathways. Importantly, it does not only generate recommendations but also provides reasoning, fit analysis, and trade-offs, enabling more transparent and informed decision-making.

This model reflects a broader transition from assumption-based career progression to systems that are adaptive, evidence-driven, and continuously refined. By focusing on what individuals can do rather than where they have been it creates a more accurate and inclusive framework for talent identification.

For organizations, this represents a shift from filtering candidates to understanding them. For individuals, it expands access to opportunities that would otherwise remain inaccessible under traditional screening mechanisms. For the broader economy, it enables a more efficient allocation of skills, improving productivity and accelerating innovation.

The persistence of the “talent shortage” narrative obscures the real issue. Africa does not lack talent; it lacks the systems required to see it clearly. Until identification mechanisms evolve beyond rigid credentials and toward capability-driven models, the gap between available talent and deployed talent will remain.

The next phase of growth in Africa’s technology sector will not be defined by how much talent is produced, but by how effectively it is recognized.

See also: 3MTT secures an additional €11 million funding for tech talent building in Nigeria