JOHANNESBURG, South Africa, August 26, 2026/APO Group/ –Generative AI can be a powerful business knowledge service, saving considerable time for employees. Yet, concerns grow around AI accuracy and how it handles sensitive information. According to a 2025 KPMG survey (https://apo-opa.co/3UgIjBb), only 46% of company workers trust AI, and that proportion is shrinking, especially since 56% say they are making more mistakes because of AI. 



 

Despite such growing doubts, the technology can be an exceptional information source and time-saver, especially for sensitive, technical, and contextual queries. A South African payroll platform developed these advantages, using AI to provide detailed and contextual information for customer employees and payroll teams.

 

Payroll staff spend considerable amounts of time answering routine questions, such as why someone’s net pay decreased, what amount of taxes they paid, or how much overtime they earned last month. Said staff also have queries around formulas and components, regulations, payroll statuses in different business units, and creating new payroll structures.

 

“Payroll staff juggle a lot of queries,” says Warren van Wyk, Director at Deel Local Payroll, home of the PaySpace payroll platform. “Can generative AI handle those queries while adding real context? We believe it can, but that’s not enough. There has to be trust, accuracy, and oversight. How do we create those things? That was the challenge we set for ourselves.”

 

Building an AI assistant worthy of payroll

 

Generative AI excels at conversing in natural language, changing how we communicate with technology. Already, search engines produce AI-compiled answers that users can expand with follow-up questions.

 

Yet, search answers are relatively simple. An AI that handles payroll queries must meet specific parameters. Van Wyk didn’t want another chatbot that simply responded to keywords. Payroll information is very contextual, and the AI must reflect that. Payroll environments are also full of private information, security limits, and regulations that require strict oversight and privileges.

 

These stipulations define the baseline for a useful generative AI payroll assistant. Thus, the engineers at Deel Local Payroll developed AI Assist, a remarkable assistant that can answer payroll questions tailored to the person asking. Employees get specific answers about their payslips, from what they pay in tax to deductions and overtime.

 

Payroll staff additionally use AI Assist to surface and study specific payroll components and formulas, handle detailed queries, and explore the PaySpace platform’s knowledge base. AI Assist provides answers relevant to an organisation’s payroll setup, such as calculation formulas or listing components sharing specific tax codes. Payroll departments also use AI Assist to understand and exploit the PaySpace system’s features.

 

Building trust in AI

 

However, being helpful is not enough. Generative AI can make mistakes. Firms worry about AI’s access to information. Can someone use AI to learn other people’s salaries? Will the AI unearth forgotten and under-supervised data? What about the current data? Payroll information is incredibly sensitive. What if the AI causes a data leak or embeds that information in its model?

 

Developers understand how crucial it is to answer those concerns and use them as their baseline, says Van Wyk.

 

“We are very conservative with AI Assist’s features, focusing on several important things from the start. We host pre-engagements with our customers to identify specific use cases, and we established several rules. The AI will be native to the platform, not an integration. It will conform to ISO and SOC standards, and it will show a user only what they have authority to access. Nobody can ask AI Assist about other people’s salaries if they don’t have the authority to see those details. If a feature can’t meet those criteria, it goes back onto the shelf.”

 

The team designed AI Assist’s infrastructure to ensure sensitive data doesn’t appear to the wrong user or end up inside an AI’s model. Whenever someone interacts with the AI, it creates a temporary instance on secure Microsoft Azure cloud infrastructure. No data leaves that space, and access to data depends on the user’s profile as determined by the PaySpace platform.

 

To reduce the risk of hallucinations and other mistakes, the system doesn’t rely solely on the AI model. It runs subsystems that handle specific queries and data access, passing information to the AI.

 

“We cannot use our customers’ data to train the model,” says Van Wyk. “Instead, we have systems that curate the right data and hand information to the AI, which then responds to the user. This is very important and has two advantages. It stops data from leaking into the AI model, and it improves answer accuracy because we directly control the mechanisms that link the data with AI Assist.”

 

Intuitive and effective

 

Hype and misconceptions cloud generative AI’s potential. Deel Local Payroll avoids these issues by focusing on the technology’s most obvious value and deliberately slowing its development pace to ensure maximum, focused benefits.

 

“We start from a basic premise: how can generative AI make things easier for our customers, whether they are working on payroll or need answers from payroll. How do we create thoughtful communication that gives them answers they can trust? It’s that simple, but it’s still very difficult to figure out because this is a new technology with many unknowns. So, we move carefully, test thoughtfully, and involve our customers through user engagements and beta testing.”

 

The results are incredible: “It’s amazing; there’s nothing like it. This will change payroll and every aspect of how we engage with business information. Generative AI done right is changing business for the better.”

CAPE TOWN, South Africa, August 26, 2026/APO Group/ –Africa’s energy sector is transforming as countries diversify their energy mixes and prioritize regional integration for energy security, increasing demand for digital technologies, infrastructure expansion and skilled local workforces capable of managing increasingly complex energy assets.
 



 

African Energy Week (AEW) 2026, taking place in Cape Town from October 12-16, will convene technology and energy service providers and utilities to examine how infrastructure-led and technology-driven solutions can improve the reliability of energy assets for socioeconomic development.

Technology is redefining Africa’s power sector. As real-time management and digital energy networks become the norm, Google is accelerating this transformation by building out the continent’s digital infrastructure, AI tools, and tech ecosystem. Having already exceeded its $1 billion investment goal in Africa, Google’s cloud and connectivity solutions are actively tackling core grid challenges – from balancing demand against generation to minimizing transmission losses and optimizing renewable assets. During AEW, Alex Okosi, Managing Director for Sub-Saharan Africa, will outline how Google plans to continue driving infrastructure growth and supporting the future of Africa’s energy assets.

Meanwhile, KAESO is demonstrating how African service companies can build the local infrastructure, technical capacity, and supply chains needed for increasingly complex oil and gas projects. At AEW 2026, Jorge de Morais, General Manager of KAESO Services, is expected to detail how the company is driving project development, supporting exploration, and boosting local workforce capacity as Angola and Namibia enter a new offshore investment cycle. The company is exploring a fully operational base in Lüderitz to support growing regional demand across Namibia and Mozambique.

To strengthen grid security and expand capacity, National Transmission Company of South Africa (NTCSA) is executing a R440-billion initiative to build 14,500 km of new transmission lines. At AEW, Chief Engineer Popi Mfapa is expected to outline global investor opportunities tied to this massive rollout. Rapidly scaling transmission infrastructure is essential as South Africa works toward a 40% renewable energy mix by 2030 and strengthens its position in regional power trading.

“Africa’s next generation of energy infrastructure will require more than investment in physical assets. It will require digital intelligence, operational expertise, resilient transmission networks and a skilled African workforce capable of managing these systems,” stated NJ Ayuk, Executive Chairman of the African Energy Chamber. “The participation of Google, KAESO and NTCSA at AEW 2026 reflects the convergence of these capabilities and the opportunities emerging as African markets modernize their energy systems.”

JOHANNESBURG, South Africa, August 27, 2026/APO Group/ —By Sanjay Govender, Head of GBS/BPO Solutions at Qrent (https://Qrent.co.za/).
 



 

The BPO industry has embraced AI as a technology capable of improving operational efficiency, enhancing customer experiences, and supporting business growth. But inside South African call centres, the opposite is quietly happening.

As AI tools become deeply embedded into customer engagement environments, many operators are discovering that the real cost of AI is not the software licence – it’s the infrastructure required to run it.

 

From voice neutralisation software and real time call assistance to AI driven first line support and live agent coaching, the processing demands inside modern BPO environments have increased dramatically over the past 18 months.

 

What many providers underestimated was the backend impact. AI does not run for free. It requires compute power, memory, networking throughput, low latency environments, and increasingly expensive infrastructure to support it at scale.

 

The result is that many BPOs are now facing a difficult and expensive decision. One approach is to run AI workloads directly on endpoint devices. This means moving away from standard workstation deployments toward higher specification machines capable of handling AI assisted applications locally.

 

In practical terms, this is driving a noticeable shift away from traditional Intel i5 deployments toward growing demand for i7 powered devices on the call centre floor. AI enhanced workloads are forcing hardware upgrades far earlier than many refresh cycles originally planned for.

 

The second option is to keep endpoint devices relatively standard while shifting the AI processing burden into the backend environment. In this model, AI applications and workloads are hosted centrally on servers, reducing the processing demand on the user device itself. While this avoids large scale desktop upgrades, it introduces a different problem – significantly increased server infrastructure requirements.

 

This is where many BPOs are starting to feel the financial pressure. Backend server environments capable of supporting AI driven workloads require substantially higher compute density, increased storage performance, more advanced networking, and far greater scalability than traditional call centre infrastructure.

 

The cost of expanding on premises server stacks to accommodate these workloads is rising rapidly, particularly as demand for AI capable hardware continues to grow globally.

 

According to Gartner, worldwide spending on AI optimised servers is accelerating sharply as organisations race to support enterprise AI workloads, contributing to overall global IT spending reaching $6.15 trillion in 2026 (https://apo-opa.co/4gTlf4e).

 

The third route many organisations are exploring is moving AI infrastructure off premises entirely through hyperscale providers such as Amazon Web Services or colocation environments like Teraco. In this model, the infrastructure is rented rather than owned, with AI workloads hosted externally and delivered to the BPO environment through cloud or hosted platforms.

 

While this removes the burden of large upfront infrastructure investment, it introduces ongoing rental and operational expenditure costs that must be managed carefully over time. For some BPOs, this creates far greater flexibility. For others, especially those operating at scale with strict latency and compliance requirements, the long-term cost equation becomes more complex.

 

What is becoming increasingly clear is that AI is fundamentally changing the economics of the BPO industry. For years, cost optimisation in call centres focused largely on labour efficiency. Today, infrastructure efficiency is becoming equally important.

 

The conversation is shifting from simply how many agents a BPO can support, to how much compute power it takes to support them effectively in an AI enabled environment. This is why the traditional procurement model is coming under pressure. Many operators still attempt to purchase server infrastructure outright through large capital expenditure projects.

 

But in a market where AI workloads are evolving rapidly, hardware demands are changing constantly, and infrastructure pricing remains volatile, locking large amounts of capital into fixed infrastructure is becoming increasingly risky.

 

A growing number of BPOs are instead exploring leasing and rental models for backend AI infrastructure. Rather than purchasing expensive server environments upfront, providers can deploy infrastructure through operational expenditure models that spread costs over time while maintaining flexibility as AI requirements evolve.

 

This approach also reduces the risk of overinvesting in hardware that may become insufficient or obsolete far sooner than traditional infrastructure cycles allowed for. In an AI driven environment, scalability and adaptability are becoming more valuable than ownership itself.

 

The uncomfortable reality is that AI is not automatically reducing operational costs inside BPOs. In many cases, it is increasing them. The difference is that the costs are shifting away from people and moving into infrastructure.

 

That changes everything, because the next competitive battle in the BPO industry may not be about who has the cheapest labour model. It may be about who can afford to power AI at scale.