Old Mutual’s Dhesen Ramsamy to Present at ITWeb AI Summit 2026

Why It Matters

The talk underscores that without solid data governance, AI projects risk compliance failures and loss of competitive advantage, a concern for financial services and other regulated sectors. It signals a broader industry push for infrastructure, talent, and policy alignment across Africa.

Key Takeaways

• Data governance critical for AI trust and compliance.
• South Africa's data policy stresses sovereignty and localisation.
• Skills and compute shortages hinder African AI innovation.
• Business AI adoption outpaces regulatory frameworks.
• Sector-specific AI accountability standards needed in finance.

Pulse Analysis

Data governance is emerging as the linchpin for responsible AI deployment, especially in regions where regulatory landscapes are still evolving. Ramsamy’s upcoming presentation at the ITWeb AI Summit 2026 will dissect how South Africa’s National Data and Cloud Policy enforces data sovereignty, ensuring that sensitive information remains under local jurisdiction. By tying data quality to model performance, he illustrates that enterprises can capture AI value only when they embed lineage tracking, bias detection, and compliance checks into their data pipelines. This perspective resonates with global executives seeking to balance innovation with legal and ethical obligations.

The African AI ecosystem faces distinct hurdles that amplify the governance challenge. While South Africa boasts a relatively dense network of data centres, the continent’s share of global AI‑ready compute power remains modest, limiting the scale of experimentation. Coupled with a shortage of skilled data scientists and engineers, organizations often struggle to move beyond pilot projects. Ramsamy emphasizes that addressing these infrastructure and talent gaps is essential for turning AI from a novelty into a core business capability, urging public‑private partnerships to fund training programs and expand high‑performance computing resources.

Regulatory lag compounds the risk, as many firms deploy autonomous decision‑making tools without clear accountability standards. In financial services, where algorithmic transparency and auditability are paramount, the absence of a ratified AI Act creates exposure to operational and reputational threats. Ramsamy calls for sector‑specific frameworks that enforce explainability, human oversight, and robust audit trails. By shifting focus from compliance alone to building internal AI expertise, companies can transition from passive consumers to active builders, securing a competitive edge while safeguarding stakeholder trust.