Beyond the AI experiment

Jonathan Oaker, CloudZA CEO.

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South African enterprises are experimenting with artificial intelligence (AI) at pace, but too many projects remain stuck at the proof-of-concept stage. Pilots are launched, enthusiasm runs high, yet the value often fizzles out before anything reaches customers or operations. The result is an innovation culture that looks active on the surface but does not deliver transformation where it matters most. “You can run pilots endlessly, but until you define what success looks like and push into production, you won’t see real business impact,” says Jonathan Oaker, CEO of CloudZA. “Once you’ve got 95% validation on your success criteria, it’s time to go live.” Defining these success criteria upfront ensures that pilot projects have a clear goal and measurable outcomes, making it easier to decide when to scale and invest further.

AI depends on data, but the quality of outcomes relies heavily on how that data is stored and accessed. Yet many local businesses still work with traditional systems that cannot support the demands of AI workloads, which is why modernising the data layer is the first step. “We see big shifts when clients move from legacy databases into modern data lakes,” explains Oaker. “If you put your data into something like S3 object storage with Parquet files, you can query millions of rows in seconds. That’s the kind of foundation AI needs.” Once the underlying data layer is updated, AI stops being an abstract idea and begins to deliver real value. For example, contact centres now use AI to automate quality assurance and analyse thousands of calls daily; healthcare providers leverage AI for claims validation and patient insights; and banks are modernising their infrastructure to offer personalised financial advice and improve customer experience.

The cloud is not simply a platform for hosting AI. Oaker sees it as the place where organisations can achieve both scale and cost control. That said, many still assume that migrating workloads will be prohibitively expensive, “but with the right architecture and budget, we’ve seen organisations cut their total cost of ownership by as much as 70%,” he says. “The cloud gives you elasticity and the ability to query data on demand, rather than keeping expensive servers running all day.” This shift allows businesses to experiment without fear of runaway costs and to scale quickly once those experiments prove their worth.

As AI adoption gains momentum, Oaker sees organisations continually running into the same obstacles: a shortage of skills, rising costs around workload optimisation and privacy concerns. It’s not a secret that most companies do not have the internal expertise to design AI solutions at scale (and the price of poorly managed deployments can rise quickly). “If you’re not caching prompts or optimising queries, costs can explode,” he cautions. “The deployment model also matters. Managed services like AWS Bedrock give you security and cost control, while unmanaged GPU instances can become expensive quickly.” For many organisations, the challenge is not whether AI works, but whether it can be deployed without risking intellectual property or sensitive information. “Businesses worry about where their data is going and who has access,” Oaker continues. “That’s why we help clients build private, secure AI environments that protect intellectual property and meet compliance standards.”

CloudZA’s approach with AI is to help clients move faster in a way that makes economic sense. “Talk to a consultant before you make big decisions,” Oaker advises. “We can help you architect the right solution, apply for funding and accelerate deployment. We frequently build comprehensive models before customers make any financial commitment, allowing the work to demonstrate its own value.

It’s about getting them to production quicker and with less risk.” Moving from pilot to production also requires a mindset shift. Generic, generative AI (genAI) chatbots may showcase technical capability to a certain degree, but they rarely deliver on business outcomes. “The differentiator is when AI starts becoming part of a person’s daily experience,” he says. “If a bank can use your data to provide personalised advice instead of a generic response, that changes the game.” But there’s a risk in waiting. Oaker predicts that the companies which hesitate now will be overtaken by competitors already scaling AI across their operations. “And by 2027 to 2030, businesses that aren’t leveraging AI in production will lose ground to competitors that are,” he warns. “This is the new revolution, and you can’t afford to sit on the sidelines.”