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Dataiku, Blue Turtle target SA's growing AI market

Chris Tredger
By Chris Tredger, Technology Portals editor, ITWeb
Johannesburg, 23 Apr 2026
Sid Bhatia, AVP and GM for the Middle East, Turkey and Africa at Dataiku.
Sid Bhatia, AVP and GM for the Middle East, Turkey and Africa at Dataiku.

enablement platform Dataiku and its South African integration partner, Blue Turtle, participated in the ITWeb AI Summit 2026 and said they are determined to support local AI adoption.

The companies believe South African organisations face several challenges that block AI adoption and application. These include fragmentation, with disconnected and ungoverned AI pilots exacerbating the problem of shadow AI, and difficulty scaling generative AI safely to avoid leakage or hallucinations.

Sid Bhatia, AVP and GM for the Middle East, Turkey and Africa (META) region at Dataiku, said there is an opportunity to bring the company's technology to the South African market because of the increasing focus of businesses on securing a foundation for AI adoption and management – and to ensure return on investment.

Speaking to the increased prominence of agentic AI, Bhatia said this technology is not just smarter automation. It entails multi-step reasoning and orchestration and, as such, requires high-quality and a robust platform to support complex workflows.

The companies assert that effective governance, monitoring and clear guardrails are essential to manage risk, as is compliance with regulation such as SA’s draft AI policy framework. Bhatia believes it is imperative that ethical adoption be covered in policy.

“The necessity comes in where AI will drive strong economic growth and innovation. Having an ethical AI policy framework will prioritise local realities including socio-economic disparities, limited infrastructure or connectivity and compute power. Core pillars such as ethical governance, sector-specific strategies and bias mitigation will avoid conflicts going forward,” Bhatia added.

Dataiku and Blue Turtle argue that AI success depends on three factors: institutional expertise in an era of commoditised large language models; cross-platform orchestration that prevents AI from being trapped in silos; and complete visibility and control so organisations know what is running, how it is performing and whether it is doing what it should.

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