As artificial intelligence (AI) continues to revolutionise business operations, another emerging technology is quietly gaining traction: quantum AI. Combining the computational power of quantum computing with advanced AI algorithms, quantum AI promises to unlock solutions to problems too complex for classical computers to handle. But while interest in the technology is surging, practical implementation is still at a nascent stage.
A recent global survey of 500 business leaders found that more than 60% of respondents are actively exploring or investing in quantum AI initiatives. This reflects a growing awareness of quantum’s potential to reshape data analysis, machine learning, and decision-making across industries such as finance, healthcare, and manufacturing.
Despite this interest, the same survey revealed that 38% of executives view high costs as a barrier to adoption, while 35% cite a lack of understanding, and 31% are concerned about the lack of real-world applications. These figures underscore a key trend: curiosity about quantum AI is high, but many organisations remain unsure how to engage with it effectively.
“With the emergence of quantum technologies, companies can analyse more data than ever and achieve amazingly fast answers to very complex questions involving myriad variables,” said Bill Wisotsky, Principal Quantum Architect at SAS. “Our goal is to make quantum research simple and intuitive for our customers and help them apply it to their business.”
While Wisotsky’s comments reflect a vendor perspective, they highlight a broader industry sentiment—that the value of quantum AI will ultimately lie in its ability to solve real-world problems, not just theoretical ones.
A technology still in development
Quantum AI, in essence, leverages the capabilities of quantum computers to improve AI processes such as optimisation, machine learning, and pattern recognition. Unlike traditional computers, which process information in binary bits (0s and 1s), quantum systems use qubits, which can represent multiple states simultaneously through quantum superposition and entanglement. This allows them to perform complex calculations at speeds impossible for conventional machines.
However, quantum hardware is still in the early stages of development. Most systems are limited by noise, error rates, and the need for highly controlled environments. This has led to the rise of hybrid quantum-classical models, where quantum algorithms are integrated with classical computing infrastructure to achieve near-term benefits while full-scale quantum computing matures.
Quantum AI in the real world
Some companies have begun experimenting with proof-of-concept projects in quantum AI, particularly in areas like logistics optimisation, financial risk modelling, and advanced drug discovery. These applications often require evaluating billions of variable combinations simultaneously, something quantum systems are theoretically well-equipped to handle.
For example, a number of enterprises are piloting quantum annealing to solve large-scale optimisation problems. Others are exploring quantum-enhanced machine learning to improve forecasting accuracy or detect anomalies in vast datasets.
South African businesses, especially those in sectors like banking, mining, and healthcare, may find early applications in areas such as portfolio optimisation, supply chain efficiency, and drug molecule simulation, respectively. But for now, these use cases remain exploratory.
The need for skills and strategy
Beyond the technical challenges, another key barrier is the talent gap. There is a lack of professionals who understand both quantum mechanics and AI, making it difficult for organisations to build in-house capabilities. According to a World Economic Forum report, quantum literacy is one of the critical skills gaps in the emerging tech landscape.
To overcome this, industry experts recommend that organisations begin by investing in foundational training, forming academic or vendor partnerships, and experimenting with sandbox environments that allow teams to test quantum approaches without high upfront costs.
Although mainstream adoption of quantum AI may still be several years away, its momentum is undeniable. Governments are investing in national quantum strategies, while private capital continues to flow into quantum startups and infrastructure. A recent McKinsey report estimates that quantum technologies could create up to $1.3 trillion in value by 2035.
For South African business leaders, the current window presents an opportunity to observe, learn, and experiment. Engaging now, even at a small scale, could provide early-mover advantages when quantum computing becomes more accessible and commercially viable.
The promise of quantum AI is not just faster computing, it’s about reshaping how businesses think, learn, and make decisions. As technology, expertise and use cases continue to evolve, organisations willing to explore this frontier may find themselves ahead of the curve when the quantum shift arrives.
