Building trusted foundations for AI success

David Wylie, Commercial Territory Lead, South Africa and SADC, Hitachi Vantara. (Image: AI-enhanced)

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Scaling AI out of the lab and into production is proving to be a significant challenge for organisations around the world. Recent research found that in 2025, as a growing number of enterprises rushed to launch generative AI proofs of concept, nearly half had to pause or abandon them before broader deployment.

The key to success is proper planning and putting the right foundations in place first, says David Wylie, Head of Commercial for South Africa and SADC at Hitachi Vantara.

Wylie says: “Many organisations are battling to scale AI out of the proof of value and proof of concept stages. Their problems usually come down to readiness across data and infrastructure, and the organisation’s expectations of AI.”

Wylie says data is the most critical challenge to overcome for successful AI. “Where AI use cases have failed, it is often due to the quality of the data. Data readiness is the most important foundation for a successful AI strategy. Organisations’ data platforms have to be modernised and able to deliver the required performance. Organisations must also assess whether their data is accurate, fragmented, in silos, and whether it is properly governed,” he says.

“When you start to dig into it, there are fundamental problems with how the data is organised and how it's managed. From the largest enterprise organisations down to small and medium enterprises, they face exactly the same problems because they haven't actually had to deal with the quality of their data before.”

Wylie emphasises that building a solid data foundation need not mean embarking on an enterprise-wide data project. “It is important for them to identify the use cases that will deliver the greatest business value. Once identified, you need accurate, available, secure data to be fed into these models for them to produce measurable and impactful output,” he says.

“When Hitachi Vantara engages with clients, we're looking at all of the facets impacting data. We assess what data platform is currently in place and whether it is ready for AI models. We help assess what use cases would deliver immediate value. And this is where we bring in our substantial global industry expertise to bear to unpack and find those use cases that would make a real impact. And often there's a Hitachi company or a client that's been through a similar problem,” he says.

Scaling AI successfully requires the right underlying infrastructure, Wylie emphasises.

“We can bring that expertise to bear. We carry out an analysis of the infrastructure to determine whether it will deliver the necessary performance in terms of throughput, storage capacity and internal bandwidth to support and scale the AI models,” he says.

He notes that many organisations that have deployed AI models into the cloud still have a lot of the data on-premises. “Recent industry research indicates that more than half of enterprise IT workloads still run on-premises, either in corporate data centres or local IT facilities. Organisations are finding that there's enormous expense associated with keeping data on-premises while using AI models in the cloud,” he says. “Because of growth in demand for sovereign AI, there is a big focus on modernising the organisation's own infrastructure to support these models.”

Wylie says organisational readiness is equally important: without a clear strategy, AI projects are likely to turn into costly white elephants.

“We have found that many organisations spend a huge amount of money on infrastructure to support AI before they know whether the quality of their data supports any kind of AI model, and before they have identified the business case for AI or the value they want to achieve,” he says. “This can result in troubled projects that introduce significant cost and complexity.

“We focus on the key players in the organisation to understand who the decision-makers are, what their KPIs are, and what they are trying to achieve with the business over the next three to five years. Then we help them assess what specific use cases will deliver results that will make a real difference to their business. Often when we engage with clients, there is a lot of uncertainty, because they haven't really thought about this,” he says.

Hitachi Vantara helps organisations create the trusted data foundations they need for successful AI adoption, with solutions such as VSP One and Hitachi iQ to enable organisations to modernise their environments, accelerate AI adoption and manage data seamlessly across environments.

Wylie says: “AI represents one of the most significant opportunities to our clients in decades. But people are nervous about it. Public opinion is mixed. More importantly, business leaders are nervous about doing the wrong thing, making the wrong investments and not realising a good result. And this is where they need expertise to assist them on this journey. They need to reach out to a partner that has experience in these projects.

“We play right through the cycle of bringing successful AI projects into production, from the initial analysis of the data and infrastructure foundations and identifying what AI use cases would bring the greatest benefits, through to assisting organisations to implement AI that delivers real business value,” he concludes.

To learn more about Hitachi Vantara’s services and arrange an AI readiness assessment, go to here.