Artificial intelligence (AI) is increasingly shaping how businesses operate, from automating routine tasks to providing sophisticated insights for investment and lending decisions. It promises efficiency, speed, and data-driven fairness in theory. Yet, as much as AI offers opportunities, it can also unintentionally reinforce existing inequalities. One area where this risk is particularly concerning is the funding of small and medium enterprises (SMEs). These businesses are vital engines of economic growth, innovation, and employment across the Middle East and Africa, yet AI-driven investment and credit systems may inadvertently marginalize them if biases are embedded in the technology.

by Kasun Illankoon, Editor-in-Chief at Tech Revolt

This issue was brought into sharp focus during the recently concluded International Digital Cooperation Forum (IDCF) 2026 in Kuwait, where global leaders discussed the theme “Fueling Growth: Innovation and Investment as Catalysts for Digital and Social Prosperity.” Speakers including Frederico Menna, CEO of 28Digital Europe Union, Jascha Stein, Executive Chair at People Centered Internet and CEO of Omnibot.ai, and Anna Ekeledo, Executive Director of AfriLabs explored how innovation and strategic investment can drive digital and social development. Their conversation underscored a critical point: while AI and investment innovation can accelerate growth, they can also unintentionally exclude the very enterprises they aim to empower.

Understanding AI Bias in Investment Decisions

AI bias occurs when algorithms consistently produce outcomes that favour certain groups over others. Biases can emerge from training datasets that overrepresent specific markets or types of businesses, from assumptions built into model design, or from the limited availability of accurate data for SMEs in emerging markets. According to research reported by BIPSS, these biases can disproportionately reject SMEs that are technically creditworthy but underrepresented in datasets, effectively blocking them from funding opportunities.

For instance, investment models trained primarily on large, structured businesses from developed markets may classify smaller or informal enterprises in the Middle East or Africa as “high risk,” regardless of their performance. This results not only in missed investment opportunities for SMEs but also in a concentration of capital among a few familiar players, perpetuating inequality in access to funding.

Why SME Funding is Critical in the Middle East and Africa

SMEs contribute significantly to economic growth across the region. In the MENA region, they generate a large share of employment and account for roughly 25% of GDP, yet many struggle to access finance. Reports indicate that Middle Eastern tech startups, for example, attract less than 5% of global AI funding, reflecting both investor caution and systemic barriers. (Agbi.com)

In Africa, the situation is similar. Less than 20% of SMEs access formal bank credit, and only a small fraction secure equity or venture capital. Yet SMEs drive up to 70% of GDP in countries such as Ghana. As reported by The B&FT Online, AI-driven lending tools that rely on incomplete or biased data risk rejecting these businesses, even when their repayment history or growth potential is strong. (thebftonline.com)

The Paradox of Innovation Without Inclusion

At IDCF 2026, speakers highlighted a paradox: AI is celebrated for its ability to democratize access to finance and accelerate growth, yet it can deepen inequities if ethical safeguards are ignored. In regions with uneven digital infrastructure and fragmented financial reporting, algorithmic tools may penalize businesses for factors beyond their control — such as incomplete financial data, informal business structures, or lack of digital footprints.

According to a report by McKinsey, the GCC countries are exploring AI’s potential to drive economic growth, yet gaps in adoption and AI readiness mean that SMEs may not benefit equally from these advancements. (mckinsey.com) Similarly, researchers note that in sub-Saharan Africa, uneven access to digital infrastructure and data further compounds algorithmic bias. (arxiv.org)

Pathways to Fairer AI-Driven Investment

The challenge is not simply to critique AI but to design systems that are inclusive by default. Experts at IDCF 2026 emphasized several key strategies for achieving fairness:

• Contextualized Data and Models: Investment algorithms should incorporate data reflective of local economic realities, capturing informal businesses, regional market conditions, and non-traditional credit histories.

• Ethical Governance Frameworks: Developing clear standards for AI transparency, accountability, and fairness ensures automated decisions are auditable and equitable.

• Capacity Building for SMEs: Education, infrastructure development, and mentorship programs can help SMEs meet investor expectations while preparing them to interact effectively with AI-based systems.

• Combining AI with Human Judgment: While AI can speed decision-making, human oversight remains essential to avoid blind spots that could disadvantage underrepresented businesses.

Conclusion

AI has the potential to transform SME funding and fuel economic growth across the Middle East and Africa. However, unchecked biases in AI-driven investment and credit systems can inadvertently reinforce existing inequalities. The IDCF 2026 discussions in Kuwait served as a timely reminder that innovation and investment must be paired with inclusive design, ethical oversight, and human judgment to ensure equitable growth. By addressing AI biases and building supportive ecosystems, stakeholders can ensure that SMEs — the backbone of regional economies — truly benefit from the promise of digital prosperity.