AI
PwC’ s 2025 Global AI Jobs Barometer, based on analysis of close to a billion job ads across six continents, found that industries more exposed to AI recorded three times higher growth in revenue per employee than less exposed industries. Workers with AI skills commanded a 56 % wage premium, while skills in AI-exposed roles are changing much faster than in comparable positions.
AI only becomes useful when the use case is clear, the business value is understood, and there is a realistic view of what it will take to implement.”
The value is visible. The appetite is not.
Feeding the machine The global AI boom is already reshaping the energy conversation. The International Energy Agency reported in 2026 that electricity demand from data centres rose by 17 % in 2025, with AI-focused facilities growing even faster. The agency also warned that data-centre growth is running into physical bottlenecks, such as grid connections, transformers, chips, supply chains, and planning approvals.
In South Africa, that warning has a familiar shape. Energy, water, and infrastructure constraints already determine what businesses can plan for. In Cape Town, a proposed Equinix data-centre development has drawn objections from community and civil-society groups over water use, power demand, and environmental impact. The tension between AI’ s ambitions and local infrastructure limits is no longer theoretical.
AI may arrive as software. It lands as load.
Dawood Patel, MD of Helm, which works in AI-powered customer experience and automation, says South African companies are interested but many are still searching for the right entry point.
“ There is a lot of interest in AI, but many businesses are still trying to work out where to start,” says Patel.“ The risk is that companies begin with the tool rather than the problem. AI only becomes useful when the use case is clear, the business value is understood, and there is a realistic view of what it will take to implement.”
AI is easy to access and difficult to use well. A company can buy a tool, run a pilot, announce an innovation programme, and still fail to solve a meaningful business problem. The machine has been fed, but nobody knows what it is meant to produce.
Some companies, Patel says, are adopting AI because they feel pressure from competitors, clients, or boards to show they are keeping up. Activity can dress itself as strategy. Motion can be mistaken for progress.
A poorly chosen project absorbs time, budget and attention while producing little more than a demonstration of intent. It is possible to be very busy feeding the wrong machine.
The digestion problem AI implementation has to fit the business it enters, taking into account its data, systems, processes, customers, staff habits, and risk appetite.
“ AI should not be treated as a product that a company simply buys and plugs in,” says Patel.“ It has to fit the business process, the data, the people, and the context. If those things are not aligned, the technology may be impressive, but it simply will not deliver the value the business expects.”
A chatbot is connected to customer data, service processes, escalation rules, brand risk, compliance requirements and staff workflows. A document-summary tool raises questions about accuracy, confidentiality, review, and accountability. An automated decision process has to be governed.
The cost of implementation, Patel notes, extends well beyond the tool itself, including integration, training, data quality, governance, and human oversight. These are often the areas that determine whether AI becomes genuinely useful or remains a permanent pilot.
Data quality is one of the hardest parts. If the data is fragmented, biased, outdated, or poorly governed, the model reproduces the weakness faster. AI has no moral objection to bad inputs. It will chew what it is given.
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