According to Gartner, more than 40% of agentic AI projects will be cancelled before the end of next year. One of the main reasons, says the research house, is unclear business value. Organisations are deploying autonomous systems because of FOMO and without defining measurable outcomes. If anything, agents get pointed at whatever is loudest in the organisation, which is often an executive with a preference. 鈥淭he agent will follow its instructions to the letter,鈥� says Jan van Niekerk, senior vice president: Data and AI strategy at Netsurit.

鈥淚f the instructions were never tied to strategy, that obedience is the problem, not the safeguard.鈥� What gets automated in those conditions is what Van Niekerk calls 鈥渧anity work鈥�, the visible tasks that show the organisation is 鈥渄oing鈥� AI without changing what it costs to run.

But is any of this a technology problem? South African enterprises license the same frontier models as everyone else and pre-built agents now ship for processes that have been documented for decades. Van Niekerk says enterprise AI maturity runs through four stages, from personal AI, to workflow AI, and process AI, to being AI-native. 鈥淢ost South African enterprises are stuck at personal AI, where a tool gets handed to everyone and the world is expected to change, with no P&L impact to show for it,鈥� he says. He adds that markets like the US, the UK and parts of the Gulf have more enterprises operating at process level, mapping and redesigning end-to-end work before deploying agents and assigning ownership of the value.

Agentic AI promised to free staff up for other tasks, which is easy to report, but essentially meaningless on its own. An agent that removes six hours of work a week from a finance team has changed nothing until somebody decides what those hours can be used for. 鈥淗ours saved is a vanity metric unless the capacity is redeployed,鈥� says Van Niekerk.

His advice is to baseline the process first, measuring the capacity that鈥檚 freed up, and then tracking what it was redirected to, such as more customers served, a cost line reduced or increased revenue. 鈥淚f all you can report after 90 days is 鈥榯ime saved鈥�, you haven鈥檛 proven value, you鈥檝e proven activity,鈥� he says.

Nazia Pillay, managing director, 麻豆原创 Southern Africa, says measurable improvement should be evident within months when an enterprise picks a well-defined process. The quickest payback comes from work that is repetitive, time-consuming and governed by clear rules. 鈥淪tarting with the outcome, rather than the technology, makes it easier to demonstrate value and decide where to expand next,鈥� she says.

Van Niekerk says there are a few signs to look out for if a process is not ready for an agent. One would be if staff members can’t describe it end-to-end, without contradicting one another. Another is if the exceptions outnumber the rule, or the handoffs between people and systems are undocumented and the real knowledge is in somebody’s head or inbox. Lastly, if there is no single owner or baseline metric. 鈥淎gent readiness isn’t a property of the process, it’s the output of having mapped it, where needed, and redesigned it first,鈥� he says. A great example of this is credit decisioning and fraud monitoring, where the rules are already written down, which is why banks have deployed agents here first. 鈥淲here there is a decision that needs to be made, and it’s an explainable decision, that’s where agents really work well,鈥� says David Pretorius, customer advisor manager at SAS.

Pretorius believes one problem enterprises are least prepared for is agent containment, or defining the limits on what an agent can reach, change or break once it starts acting on systems rather than answering questions. An agent that reasons, retries and chains calls can act on systems. 鈥淭he model is the engine of the vehicle and the harness around it is the steering, the brakes, the dashboard, the seatbelts, everything around it, keeping it contained,鈥� says Pretorius. 鈥淚t should have a bounded blast radius.鈥� If a deployment causes problems, it should be cheap to fix and quickly reversible, which implies project management rather than model selection. This is also true of policy, because a document written for chatbots will have little defence against agents acting autonomously. 鈥淭he objective is not to require human approval for every routine action, but to establish clear thresholds so that agents can handle predictable work efficiently while people retain control over sensitive decisions, exceptions and consequences,鈥� says Pillay.

Agent density is already a capacity metric in infrastructure planning, but Pretorius says that the enterprise version will rather count agents against people. 鈥淏y 2030, the question will be how dense is your agent landscape,鈥� he says. 鈥淎nd how does that ratio sit with regards to the amount of people you have, and what is the payoff rate?鈥� Getting to this kind of number means knowing what each agent is for, and who owns it, which most enterprises haven鈥檛 yet considered. This is why Van Niekerk鈥檚 recommendation is to treat agents as organisational assets, with ownership tied to a role.

He says agents need to be registered, and their purpose, permissions and prompts documented. 鈥淚n a business of 10 000 people running 10 agents each, agent lifecycle management becomes as fundamental as identity management is today.鈥�

BETTER TOOLS, SAME RULES

FNB parent FirstRand went from dabbling with agentic AI to scaling it inside six months, says Kevin Mitchell, chief digital officer. Project scoping took 30 minutes, compared to two weeks.

No exceptions

Every control built up over decades of regulated delivery still applies once agents enter the pipeline, he says. Segregation of duties, risk review and release management all need to be included in the production workflow. All code, whether written by a staff member or generated, must still be reviewed before it鈥檚 committed.

Keep the estate closed

Pay attention to data flows; a tool that sends customer information to an unintended location will be flagged by risk and compliance. Running agentic tooling in a closed environment isolates the prompts, code and data. Mitchell says the data must be kept under the bank鈥檚 jurisdiction. Usage telemetry will also show who is actually working with the tools, and for what purpose.

Build it into the SDLC

An AI program that runs alongside a development process should be included in the development lifecycle at the outset. Mitchell says AI agents should span ideation, product development, risk, legal and operations, rather than only being used by a single team. Bring the risk and legal people in early, he advises, because teams that meet an agent for the first time at go-live may become a bottleneck.

Tie tokens to outcomes

Consumption dashboards will tell you an agent is busy with a task, but not whether it鈥檚 providing value for money. Mitchell suggests linking token spend to specific backlog items and features. He says the bank is already tracking these metrics, including time-to-production.

Plan for the sceptics

The appetite for agentic usage inside a large engineering team will not be uniform, and a rollout should assume that enthusiasm will wane. One group at FNB queued for access immediately, while another team said they didn鈥檛 trust a coding agent to fulfil tasks as well as they could. The latter group needs evidence rather than encouragement, says Mitchell. The argument he makes is that the tooling has changed, not the responsibility. 鈥淗umans are ultimately accountable. You just have a better tool,鈥� he says.

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Emmanuel Raptopoulos, Global President of Customer Success for Europe, Asia-Pacific, the Middle East and Africa at 麻豆原创, spoke to ITWeb following the recent 麻豆原创 Connect 2026 event in Las Vegas.

鈥淵ou need enterprise context to make meaningful decisions from information held in your systems. That is the foundation for moving from reactive towards predictive operations,鈥� said Raptopoulos.

He added that organisations need an AI layer anchored in their operational context to respond faster and improve predictions. This involves more than data; it also encompasses how a company operates, makes decisions and uses its transactional systems.

He believes Africa could use emerging technologies to bypass some stages of technological development.

“We have seen technologies emerge in the past that allow markets to leapfrog previous stages of development. You do not necessarily have to go through every preceding step. I think Africa has another opportunity to leapfrog from a technology perspective. With an agentic approach, there is potential to jump a stage and apply the technology to areas such as transparency and efficiency in the public sector, citizen trust, access to public services and education,” said Raptopoulos.

He said there is a lot of opportunity, but guardrails are extremely important.

“We should continue to apply judgment. Human judgment is here to stay. Governance, security and clear accountability are essential to managing autonomous systems safely.”

Risks of AI agents

Raptopoulos acknowledged that AI agents could behave unpredictably even when rules and controls are in place, including measures to ensure they are task-specific and appropriately prompted.

鈥淭here is always a possibility. Our role as a vendor, together with our customers and their governance frameworks, is to control that probability. If you respect the design principles and put appropriate governance around the technology, you can reduce the probability significantly. Cyber security is also evolving alongside AI capability. The same underlying technological capabilities can be deployed defensively,鈥� he said.

鈥淵ou therefore need the right governance internally while heightening external defences to create a shield around your systems. The objective is to prevent this becoming an issue in future.鈥�

Dr Pierre le Roux, MD of digital consultancy MOYO, said AI is moving from technology that tells people what they can do to technology that can increasingly do it for them.

That shift could pose a major governance challenge as businesses move from generative AI and copilots to autonomous AI agents that can access systems, initiate transactions, communicate with customers, change data and execute business processes, said Le Roux.

鈥淯ntil now, much of the AI conversation has been about the accuracy of the answer,鈥� he added. 鈥淎gentic AI changes the question. If the technology is able to take an action, the issue is no longer simply whether the answer was right or wrong. You have to ask who gave it permission to act, what it was allowed to access, where its authority ended and, ultimately, who is accountable for the outcome.鈥�

Le Roux said the issue is becoming more urgent as businesses move beyond AI experimentation. He cited a Gartner prediction that by 2027, 40% of enterprises could demote or decommission autonomous AI agents because governance gaps are discovered only after deployment.

鈥淎I may execute the decision, but accountability remains with the organisation that designed the process, gave the agent access and determined its authority. The question is no longer whether you trust AI, but whether your organisation has engineered the boundaries within which AI is allowed to act.鈥�

Raptopoulos said companies were increasing their investment in AI and integrating it into their operations, but questions remained about their ability to control agents operating within their systems.

鈥淎wareness has risen. Recent high-profile events have helped make the issue visible even beyond people who are highly technically literate,鈥� he said.

鈥淭he concern is that you do not want agents moving around enterprise systems in ways that could make data available to the wrong person. That is a potential boardroom nightmare. One important design principle is to make agents very specific about what they are supposed to do and give them clearly defined objective functions. An agent is ultimately an application trying to fulfil an objective. If that objective is defined too broadly, it has greater scope to find unintended ways of fulfilling it. Agents should therefore be specific to the task at hand and grounded in the enterprise objective.鈥�

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The exact figures vary considerably by crop, country and measurement method. The discrepancy is itself revealing: In many markets, we do not know precisely where losses occur or which interventions would have the greatest effect.

This post-harvest crisis is often presented as an infrastructure problem. Africa does need more warehouses, refrigerated vehicles, processing facilities and reliable electricity, but beneath these physical constraints lies another, less visible problem: an information deficit.

Under the , African leaders have set a goal of reducing post-harvest losses by 50% by 2035. Meeting that ambition will require continued investment in storage, energy, transport and processing. But building more infrastructure without improving the flow of information risks reproducing today鈥檚 inefficiencies at a larger scale.

Data drought impacts food security

Agricultural value chains frequently lack timely, reliable data about what has been harvested, where it is stored, how quickly it must reach market and where demand exists. Without this visibility, businesses cannot co-ordinate transport, plan processing capacity or intervene before produce spoils. Infrastructure is essential, but it cannot deliver its full value when decisions are based on incomplete or outdated information.

What we often call food loss is therefore also the result of disconnected decisions. A processor may not know that a harvest is arriving early. A logistics provider may lack the information needed to allocate refrigerated transport, and a warehouse operator may identify a temperature breach only after a shipment has deteriorated. Farmers without access to market signals may sell immediately after harvest, when prices are lowest, because they cannot assess whether storage would produce a better return.

Artificial intelligence can help close these gaps by turning fragmented operational data into co-ordinated action. Yield forecasts can give processors and distributors earlier visibility of expected supply. Connected sensors can monitor temperature and humidity during storage and transport. Predictive models can identify spoilage risks, while intelligent planning systems can match products with available warehouse capacity, vehicles, processing facilities and buyers.

Building a connected agri value chain

The greatest opportunity, however, comes from connecting these capabilities across the value chain. A temperature alert has limited value if it does not trigger a logistics response. A yield forecast cannot prevent waste if procurement, labour and processing plans remain unchanged. Traceability data will not unlock premium export markets unless it flows into quality management, compliance reporting and financial systems.

Enterprise technology plays a critical role by linking procurement, inventory, manufacturing, quality, logistics, finance and traceability, intelligent business systems to create a shared operational picture from farm gate to retailer. AI then helps organisations anticipate disruptions, co-ordinate responses and continuously adjust plans as conditions change.

These systems can support practical decisions: redirecting vulnerable produce to the nearest processor, prioritising a refrigerated shipment, adjusting warehouse labour ahead of a delivery, or identifying a quality risk before goods reach an export customer. Over time, this moves agricultural supply chains from reacting to losses toward preventing them.

Technology tailored to local conditions

We can already see elements of this model taking shape. are making refrigeration more accessible to small-scale producers in Nigeria. Digital sourcing platforms are helping incorporate farmers into formal value chains; one in partnership with was designed to connect 850 000 Nigerian maize producers with buyers and services. The next step is to bring infrastructure, farm-level information and enterprise operations together as one connected system.

Local conditions and infrastructure must be considered. Many smallholders do not own smartphones or have dependable connectivity. Successful solutions will therefore need offline capabilities, simple mobile interfaces and trusted intermediaries who can capture information and deliver insights locally. Technology should accommodate the realities of African agriculture rather than assume every farmer is continuously connected.

Better visibility over agricultural value chains also offers benefits beyond food security. Traceable, well-managed supply chains can strengthen access to finance, support sustainability reporting and help agricultural businesses meet the quality and compliance requirements of high-value export markets. Reducing losses improves margins without requiring more land, water or agricultural inputs, delivering both an economic and an environmental opportunity.

Africa needs an intelligent, connected value chain capable of seeing problems early and co-ordinating action across organisational boundaries.

When physical infrastructure is combined with trusted data and AI-enabled operations, post-harvest loss becomes a preventable failure that businesses, governments and technology partners can address together.

Shiraz Khota

Managing Director: Emerging Africa

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Among large technology firms, AI is now a leading reason cited for job cuts. As of mid-July, the sector has shed just shy of 140 000 jobs in 2026, according to Challenger, Grey & Christmas, a global outplacement, executive coaching and workforce transition firm. Cisco announced 4 000 job cuts in May, saying the move was due to AI adoption. Oracle has laid off around 21 000 in 2026 after accelerating investment in AI technologies. Thomson Reuters is set to eliminate about 500 engineering roles, around 1.8% of its workforce, as it rolls out AI in its businesses.

South Africa has not yet seen any mass corporate layoffs directly linked to AI. Here, the technology鈥檚 most immediate impact is being felt across the employee lifecycle. AI is helping HR streamline hiring, answer employee queries, summarise performance reviews, and onboard new staff. It is also enabling businesses to better forecast future skills needs by analysing workforce data. According to Sage, 86% of HR and payroll leaders see AI as transformative and believe that specialist AI tools will make their jobs easier. According to similar IBM data, the use of AI in HR is expected to increase productivity by 35%, training effectiveness by 30%, and retention by 20% by 2027.

Much of the talk about AI use for HR is focused on making the function more strategic, but in many cases, the real benefit lies in taking the grunt work out of the job 鈥� such as regulatory filings, day-today payroll and benefits management, recruitment list-building, initial candidate evaluation, repetitive training tasks, basic employee support, and monthly reporting, says Mark Walker, a director at the T4i consultancy. 

It just makes sense for employees to be able to easily search for information like how many leave days they have without needing to contact the HR department.

Celiwe Ross, Old Mutual Limited

Author

鈥淟ess time is being spent on paper-shuffling and more on actively managing the workforce,鈥� he says. Industry benchmarks show that up to 60% of HR support time is routinely consumed by basic queries, says Merel van der Lei, CEO of Wyzetalk. 鈥淒eploying AI assistants to handle this tier-one triage allows HR teams to reclaim days of capacity,鈥� she says. The BCG 2026 ‘AI at Work: Why Strategy Matters More Than Tools’ report found that most HR professionals using AI save the equivalent of an eight-hour workday every week.

Genevieve Koolen, HR director at 麻豆原创 Africa, says it鈥檚 seeing AI change the expectations placed on the function. 鈥淏ut useful productivity gains should not be mistaken for strategic transformation. The more significant opportunity is that AI can provide better workforce insights by bringing together data on skills, performance, capability, learning and organisational trends. These insights help HR make more strategic decisions.鈥�

THE AI HIRING CONUNDRUM

Recent research from Resume Genius shows a double standard across the recruitment process. According to a survey of 1 500 US hiring managers, 87% use AI to streamline hiring. Yet almost as many (82%) expressed concern about candidates using AI during their job searches. The survey revealed that HR and people teams are using AI to screen resumes (58%), write job descriptions (46%), match candidates to positions (44%), and schedule interviews (41%), but almost 60% expressed concern after seeing AI-generated CVs or cover letters, and almost half have noticed that candidates use AI to answer interview questions. With both sides now embracing the technology, the conversation is less about whether it should be used and more about how it can be used without compromising trust and authenticity.

Celiwe Ross, group chief human capital and corporate affairs officer at Old Mutual Limited, says one area where AI is adding the most value is in streamlining the recruitment process. This is particularly true for bulk recruitment of call centre staff or financial advisors. 鈥淏ecause there’s not a lot of stickiness in these roles, people come in and do a short stint and then leave, so HR teams are constantly screening candidates and reviewing CVs,鈥� she says.

At redPanda Software, the human capital team is using AI to draft job descriptions, compile interview questions and personalise candidate communications.

鈥淭his used to take us so much time,鈥� says Insaaf Daniels, group human capital director.

鈥淣ow we can compile details about the role, like required skills and level of seniority, for example, within minutes,鈥� she says. But she draws the line at using AI to interview applicants. AI can鈥檛, for example, assess whether someone is a good culture fit. And where humans can understand nuance and context, AI is likely to flag something like a big gap in someone鈥檚 CV as an anomaly. Similarly, traits like leadership potential and communication style are impossible to assess without talking to someone and asking follow-up questions based on their responses. 鈥淪ome get candidates to record answers to pre-set questions, but this format doesn鈥檛 allow for any natural conversation, making it harder to get an in-depth idea of who someone is,鈥� she says.

For existing employees, enabling self-service should be a consideration, says Old Mutual鈥檚 Ross. 鈥淚t just makes sense for employees to be able to easily search for information like how many leave days they have without needing to contact the HR department.鈥�

But governance, accountability, privacy, potential bias, trust and compliance are still major stumbling blocks. These concerns are amplified with the introduction of AI agents, which can be set to autonomously carry out certain HR workflows. If AI is used to automate decision-making, the logic behind how decisions are made needs to be transparent, says Walker, from T4i. HR administrators require complete visibility into the questions being asked and the answers the agent provides.

Not every AI application carries the same level of risk, says 麻豆原创鈥檚 Koolen. Supporting employees with routine HR queries or recommending learning content presents different governance considerations from using AI to inform recruitment decisions, support performance management, motivate for promotion, implement fair pay remuneration or run nuanced workforce restructuring. 鈥淭he closer AI moves towards influencing decisions that materially affect people鈥檚 careers, livelihoods or legal rights, the greater the need for transparency, auditability, robust governance and meaningful human oversight. AI can inform decisions, but it should not replace accountability.鈥�

And AI cannot replace the judgement used to navigate complex organisational dynamics, lead change or earn employee trust. 鈥淥rganisations that see AI as a cost-reduction tool are unlikely to realise its full value,鈥� says Koolen. 鈥淭hose that combine AI with strong leadership, sound governance and a deep understanding of people are far more likely to strengthen the strategic contribution of HR.鈥�

This oversight and care are important so that we don鈥檛 develop an unhealthy overreliance on the technology, Ross says. For her, there are usually two types of employees who approach the discovery of a new technology in very different ways. 鈥淏oth are exposed to an AI tool and realise how powerful it is. The one employee says: I will never use it; it’s going to take my job. The other says: I will always use it. I no longer need to do my job.

鈥淣either is good.鈥� 

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In a market predicted to  by 2031, consumers are more connected and digitally sophisticated than ever, mobile phones shape how people discover products, compare prices and interact with brands.

Yet physical retail remains central, and informal traders, neighbourhood stores and open-air markets still account for a substantial share of everyday spending across many African economies, with analysts estimating that  of total food sales in sub-Saharan Africa take place via informal channels.

This creates a uniquely demanding operating environment. Retailers must serve increasingly digital consumers while managing fragmented channels, infrastructure constraints, volatile supply chains, intense price sensitivity and continued pressure on margins.

The challenge is therefore no longer simply having access to more information, but building capabilities to act on that information quickly and accurately.

A retailer may know demand is changing, yet replenish too late. A promotion may be approved centrally but fail to reach every store or channel consistently.

Stock may appear available online while the store cannot fulfil the order, or a pricing decision may make sense commercially but arrive too late to prevent lost margin or dissatisfied customers. Each of these represent an execution gap, one that artificial intelligence is helping to close.

Closing the execution gap

Artificial intelligence is already helping retailers forecast demand, optimise pricing, improve customer service and identify changing purchasing patterns. But much of this intelligence remains concentrated within individual tasks or functions, for example a merchandising team getting better forecasts, or a supply-chain team receiving earlier warnings.

Yet people must still coordinate decisions across merchandising, finance, logistics, stores and digital commerce before anything happens.

The next opportunity is to connect intelligence more directly to action, which is a guiding idea behind the autonomous enterprise. An autonomous enterprise is not an organisation without people, but one in which people determine strategy, priorities, policies and acceptable levels of risk, while AI assistants and specialised agents help coordinate routine decisions and actions across end-to-end processes.

Instead of simply presenting an insight, an AI agent identifies an emerging problem, determines which processes are affected, recommends an appropriate response and initiate approved actions, and escalates to a person when judgement or accountability is required.

The goal is to improve execution, not simply automate every and all processes. Take the example of a grocery promotion. Demand can change quickly according to location, weather, competitor activity or consumer response.

Traditionally, merchandising, planning, pricing, logistics and stores may each operate with only part of the picture. By the time the implications become clear, a retailer may already be dealing with stockouts, excess inventory or lost margin.

In a more autonomous model, systems continuously assess sales signals, promotional activity, supplier lead times, inventory positions and store capacity. When conditions change, they adjust replenishment recommendations, identify stores at risk or flag an exception before it becomes a customer problem. All of this happens with people still retaining responsibility for the rules and for all important decisions.

An African model of autonomy

This opportunity is particularly relevant in Africa because retail here rarely follows a simple progression from physical to digital commerce.

Instead, consumers move fluidly between them. A customer may discover a product on social media, ask questions over WhatsApp, visit a store before buying and pay using whichever mechanism is most convenient.

We describe this emerging behaviour as a distinctly African “click-and-mortar” model, with digital discovery complementing rather than replacing physical retail.

The continent’s enormous informal retail sector adds another dimension. Across many markets, informal channels continue to dominate food sales and transaction volumes.

Distribution, fulfilment and customer relationships are therefore shaped as much by trust, local networks and last-mile realities as by formal systems.

African retailers cannot simply import operating models designed for highly consolidated developed markets. Their systems need to accommodate different store formats, varying connectivity, local fulfilment models and significant differences between markets.

Autonomy must therefore be grounded in context.

An AI agent tasked with preventing stockouts, for example, cannot work from historical sales data alone. It needs to understand promotions, supplier constraints, substitution options, delivery capacity, store conditions and working-capital parameters.

Without that context, automation may be fast but poorly informed.

Start with the decisions that matter

For retailers considering this next phase, the most effective starting point may be surprisingly practical.

Identify the execution gaps that repeatedly affect revenue, margin or customer experience. Obvious candidates include promotion compliance, price accuracy, fresh-food replenishment, fulfilment reliability and inventory availability.

Then connect the data and processes required to make those specific decisions better. Retailers do not need to solve every data problem before beginning, but autonomous systems do require reliable business context if their decisions are to be trusted.

Governance matters just as much. Organisations need to decide which actions agents may take independently, which require approval and when an issue must be escalated to a person.

And people must be prepared for changing roles. Planners may spend less time manually coordinating information and more time designing policies and managing exceptions.

Merchandisers can concentrate more heavily on judgement and commercial strategy, while store teams can receive more precise guidance rather than another stream of reports. This is ultimately the opportunity presented by the autonomous enterprise: not removing people from retail, but removing friction from the decisions they make.

African retailers already operate in one of the world’s most complex and dynamic consumer environments. Competitive advantage will increasingly belong to those that can connect signals, decisions and actions faster than the market around them. The next leap in retail will not come simply from knowing more, but from the ability to act intelligently on what the business already knows.

The post Beyond the Dashboard: Why African Retail Needs AI that Can Act appeared first on 麻豆原创 Africa News Center.

Held virtually on 3 September 2026, the session, 麻豆原创鈥檚 鈧�100M AI Opportunity: How to Secure Funding for Your Next AI Project, brought together 麻豆原创 experts , , , and to unpack the opportunity, eligibility requirements and practical process for accessing the incentive.

The discussion came at an important time for organisations exploring artificial intelligence. While investment and interest in AI continue to accelerate, many organisations are still struggling to translate experimentation into sustainable business value. The presentation highlighted that data access and quality remain significant barriers, while many organisations remain caught in pilot mode without a clear path to scale.

From AI experimentation to business value

A key message from the session was the need to move beyond isolated AI use cases towards AI that is embedded into business processes, supported by appropriate governance and connected across the organisation.

麻豆原创 described the evolution towards an autonomous enterprise where agents can support routine transactions and coordinate actions across business processes, while people remain focused on higher-value decisions. Importantly, governance, auditability and traceability need to be built into AI-enabled processes from the outset.

The webcast also reinforced the importance of keeping people at the centre of AI adoption, with human-triggered and monitored agent actions forming part of understanding the 鈧�100 million opportunity

At the heart of the webcast was 麻豆原创鈥檚 鈧�100 million Business AI Partner-led Adoption Incentive, a pilot programme aimed at helping customers realise greater value from their 麻豆原创 Business AI investments by encouraging adoption and consumption through the 麻豆原创 partner ecosystem.

Approved partners can deliver projects across 麻豆原创 Business AI, including 麻豆原创 Build and Joule Studio, creating agents, applications and workflows around customer use cases. The programme is running through the end of 2026.

The programme includes different levels of support depending on the scope of the implementation, ranging from 鈧�15,000 for 麻豆原创 Agent Adoption, through 鈧�25,000 and 鈧�50,000 packages, to an 鈧�100,000 Enterprise Package supporting three or more custom Joule Studio or 麻豆原创 Build agents together with workflow or pro-code application development.

Who can participate?

The session also provided clarity on customer eligibility. The programme is aimed at qualifying existing 麻豆原创 Business AI Platform customers with active subscriptions as well as certain new customers entering eligible 麻豆原创 Business AI Cloud Services agreements during the programme period. Specific commercial and subscription criteria apply, while public-sector and government entities are excluded from the current programme.

Customers do not apply independently. Eligible customers are nominated through an 麻豆原创 representative, after which 麻豆原创 validates programme eligibility. Once approved, customer consent is obtained before enrolment.

Importantly, the final choice of implementation partner remains with the customer. Customers may nominate a preferred eligible partner, or 麻豆原创 can provide available approved partner options where a preferred partner has not yet been identified.

Helping the AFSUG community navigate AI adoption

For AFSUG, the webcast was about more than introducing a funding programme. It was an opportunity to give the community practical information that can help organisations ask the right questions about AI investment, partner selection, scalability and business value.

As AI moves rapidly from experimentation into enterprise operations, collaboration between customers, 麻豆原创 and the partner ecosystem will become increasingly important. Through sessions such as this, AFSUG will continue creating opportunities for the community to connect, learn, share experiences and make more informed decisions about emerging 麻豆原创 technologies.

Connect. Create. Collaborate.

The post AFSUG Webcast Explores 麻豆原创鈥檚 鈧�100M Business AI Adoption Incentive appeared first on 麻豆原创 Africa News Center.

The breach, which does not appear to be recorded on the Information Regulator鈥檚 website, is a focus area for the platinum miner鈥檚 audit and risk committee for the next financial year, says its audited annual financial statements for the year to June.

In its 2025 ESG report, Implats 鈥� one of the world鈥檚 major platinum group metals producers 鈥� says there was a 鈥渓imited impact cyber security breach to a managed operation鈥檚 IT environment, which was quickly detected and resolved by the group鈥檚 IT service providers鈥�. In 2024, Implats 鈥渄id not have any material security/cyber security breaches鈥�.

The miner, with a market capitalisation of R208.35 billion, did not identify the affected managed operation, give a precise date, explain the nature of the breach, say whether data was compromised, or quantify any financial or operational impact.

Implats has six mining operations across South Africa, Zimbabwe and Canada, plus refining and processing operations, employing more than 60 000 people across its operations.

South Africa is the world鈥檚 largest producer of platinum, supplying almost three-quarters of the metal 鈥� used in vehicle catalytic converters, jewellery, industrial applications and emerging hydrogen technologies.

Batten down

Dawn Earp, chairman of the audit and risk committee, says its priorities for the year ahead include cyber security, AI and digital risk, and that it will oversee the group鈥檚 cyber security and AI risk management and control environment, including 鈥渢he remediation of actions arising from the recent cyber incident鈥�.

鈥淟earnings from this incident were incorporated into the group鈥檚 security posture to improve maturity,鈥� it said in 2025. Implats adds that the outcome reflected the effectiveness of its layered security strategy and continued investment in digital resilience.

Implats describes cyber security as a material risk because of its potential to disrupt business continuity and compromise sensitive information and intellectual property.

Among its cyber security measures are ongoing cyber security awareness training, annual assessments of the potential impact of cyber breaches on data and operational technology systems, regular audits and penetration testing by independent external specialists, as well as escalation protocols for incident response and breach management.

Earp says key controls, including 鈥渧ulnerability monitoring, ransomware recovery testing and backup management,鈥� operated effectively during the year.

The committee will also monitor risks associated with the integration of IT and operational technology, and oversee AI governance, responsible adoption and compliance with evolving regulatory and ethical requirements.

Digging deeper

Implats also conducted a deep dive into AI-related risks, including cyber security, data privacy, regulatory compliance and responsible AI practices, Earp says. These measures are intended to support the responsible adoption of AI across the group.

This comes as the miner expects the economic, technological, regulatory and geopolitical environment to remain uncertain, specifically pointing to 鈥渢he accelerating convergence of cyber threats, rapid advancements in AI, evolving regulatory requirements and ongoing economic volatility鈥� as factors that continue to 鈥渟hape boardroom priorities and challenge traditional risk management approaches鈥�.

Implats has also identified investment in automation, digital systems and data analytics as a way of improving operational performance and decision-making. Along with the rest of the mining industry, it is using advanced technologies to improve mineral-resource modelling and metallurgical processes.

鈥淭he mining sector is increasingly adopting automation, AI, data analytics and remote operations to improve safety, productivity and cost-efficiency.鈥�

Getting smarter

Priorities included using digital tools, automation and data analytics to improve mining efficiency and reduce waste, while exploring remote operations and smart-mining technologies to improve productivity and reduce labour intensity, it says.

Its IT strategy is focused on extracting value across support, compute, storage and security. The digital push includes cloud services, 麻豆原创 S/4HANA and further ERP investment, near-real-time reporting, data analytics and data science, process digitisation and robotic process automation.

It is also investing in above- and below-ground connectivity, internet of things integration, IT, operational technology, convergence and cyber security, all of which are embedded in performance metrics.

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• Shoprite CEO Pieter Engelbrecht says rivals can replicate the group鈥檚 Sixty60 platform, but doing so will take years and significant investment
• Sixty60 grew sales almost 35% to R25.5 billion in the 52 weeks to end June, adding a record R6.6 billion to the previous year鈥檚 sales, and is now gaining traction at Shoprite-branded stores.
• Anchor Capital analyst Steph Erasmus called Shoprite the 鈥済orilla in the room鈥�, saying it is bigger than all its rivals in grocery retail and is using that to its advantage.

Shoprite is signalling that rivals can imitate its on-demand service Sixty60, but doing so will take years and significant investment 鈥� money the retail giant has already committed.

And even then, SA鈥檚 largest retail group intends to stay ahead of the game by constantly introducing new features and capabilities to the platform. This will include everything from new agentic abilities for its AI assistant Pixie to broadening its product and adjacent store ranges that can be curated for delivery.

Asked about the threat other retailers鈥� on-demand platforms and apps pose to Shoprite, CEO Pieter Engelbrecht said while a Sixty60 platform and its app features can be replicated, it鈥檚 鈥済oing to take time and money鈥�.

鈥淎nd that doesn鈥檛 mean we鈥檙e standing still. Every single day, we are improving on [Sixty60]. That鈥檚 why you can now 鈥榓sk鈥� [through Pixie] Jamie Oliver what鈥檚 for dinner on the app. You can take a picture of your shopping list, and Pixie will understand your language and your writing.鈥�

Shoprite has a long-standing partnership with the British celebrity chef.

Engelbrecht, who was speaking to News24 after the publication of full-year results on Tuesday, said what Shoprite offers on Sixty60 is 鈥渘ot so unique that nobody else can do it鈥�; the advantage is that the group has done the heavy lifting in terms of significantly investing over many years into several technologies and platforms that ultimately laid the foundation for Sixty60 and helped Shoprite evolve into what it is today.

This includes its 麻豆原创 enterprise resource planning platform, introduced in 2017, which serves as its 鈥渃ore system of record鈥� and connects its core business operations. The system provides a single data source that gives Shoprite access to accurate information about sales and customer habits. It ultimately laid the framework for building out its
Xtra Savings reward programme, and later Sixty60 in 2019.

Another benefit is that Shoprite owns the underlying code that Sixty60 runs on.

鈥淭here are no licence fees and things like that because we developed the code ourselves. We have already made the investment, so every single day we鈥檙e doing something or adding something to it to make the experience better. In the end, it boils down to your level of execution.鈥�

This echoes what he said at an investor presentation earlier that day. Engelbrecht told investors that apart from the money rivals would need to invest, it would take them a long time to reach Sixty60鈥檚 current 鈥渓evel of execution鈥�.

He rated the Sixty60 platform against rival platforms in the market and said it came out on top.

鈥淚 tested it all. For me, there is no question that Sixty60 is the best online platform. It鈥檚 reliable. That is logically driving the behaviour of customers because they have a great experience.鈥�

This may explain why it continues to command such strong growth 鈥� in the 52 weeks to 28 June, for example, Sixty60 grew sales almost 35% to R25.5 billion, adding a record R6.6 billion in sales. The platform is also gaining traction among Shoprite customers, with the service now available from 137 Shoprite stores.

鈥楧efinitely a need鈥�

Engelbrecht said while Shoprite Sixty60 was not expected to grow as fast as Checkers Sixty60, the group was still 鈥渧ery surprised鈥� by the level of acceptance shown by Shoprite customers.

He said their basket size was 鈥渟lightly smaller鈥� than Checkers shoppers鈥� baskets, but it was 鈥渟till profitable 鈥� There鈥檚 definitely a need for it.鈥�

He also said there was still an opportunity to add all the other parts of its business to Sixty60, combining them with food delivery for a single checkout.

鈥淚t鈥檚 a single delivery, but it鈥檚 actually from four or five different businesses. And that, for me, is probably the top level of convenience you can ask for, and that is what is driving growth.”

The key to the Shoprite group鈥檚 success has been its focus on ensuring 98% stock availability for both online and in-store customers, Engelbrecht told News24. He noted that having suboptimal stock levels could mean a customer ends up receiving only half of what they ordered online.

Over the years, the group has also invested significant capital in improving its tech capabilities and logistics. In the past three years alone, it spent R22.6 billion on digital technology, supply chain upgrades, and store expansions. This equated to R7.8 billion in 2024, R8 billion in 2025, and R6.8 billion in 2026.

In the upcoming year, the group intends to spend R7.7 billion.

Shoprite is also modernising its IT system, with Engelbrecht saying the group will be transitioning to the latest 麻豆原创 S/4HANA platform for financial systems and HR over the next three years.

Engelbrecht also said there were 鈥渓ots of opportunities鈥� for growth for Shoprite in SA, adding that adjacent businesses such as pet (Petshop Science stores now number 185 and have increased sales 74.5%) and pharmacy (standalone Medirite Plus stores now number 22) were 鈥渟lowly but surely starting to contribute鈥�.

鈥淲e鈥檝e still got a long way to go with Outdoor, Uniq by Checkers, Petshop Science, and Medirite Plus,鈥� he said, adding that these adjacent businesses are also helping Sixty60 grow.

Anchor Capital investment analyst Steph Erasmus said his main takeaway from the Shoprite results, particularly the information it shared on Sixty60, was that the retailer is the 鈥済orilla in the room鈥�.

鈥淭hey are vastly bigger than any of their competition in food retail, and they are using that to their advantage, especially in areas like Sixty60 and adding in the adjacencies and the like.鈥�

Erasmus agreed with Engelbrecht about rivals being able to copy the Sixty60 model, saying it 鈥渋s going to take a lot of ammo and a lot of time鈥�.

鈥淚 think Shoprite are just ahead of the curve. They are heading the pack, and it is difficult to see anybody really making a big difference to their market position for the next while.鈥�

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From the rise of the Autonomous Enterprise to the case for embedding AI at the very core of business operations, the world of work is undergoing seismic shifts that are fundamentally altering what it means to work inside an enterprise.

The question organisations now face is not whether AI will reshape the workforce, but whether they are ready to lead that transition in a way that genuinely empowers their people.

At 麻豆原创 Sapphire 2026, under the banner of The Beginning of Better, 麻豆原创 made its most consequential set of workforce-related announcements to date. Taken together, they represent a clear and considered answer to that question.

From process operators to leaders of judgment

The Autonomous Enterprise framework introduced at Sapphire is built on an explicit design principle: humans must remain at the centre, not as operators of process, but as leaders of judgment, strategy and change. As AI assistants take on the coordination work that has long consumed employee time and attention, people are freed to focus on the decisions, relationships and creative problem-solving that machines cannot replicate.

Joule Work, 麻豆原创鈥檚 reimagined AI engagement layer, illustrates this shift vividly. Rather than navigating multiple systems and entering data across screens, employees can now describe what they need to accomplish in plain language 鈥� fill an open headcount, manage my team鈥檚 time-off backlog 鈥� and Joule orchestrates the right workflows, data and agents to make it happen.

The model of work is shifting from task navigation to intent-driven interaction, reducing operational friction and returning cognitive bandwidth to the things that matter most.

Planning for a workforce that includes people and agents

One of the most strategically significant themes at Sapphire was how organisations plan and design for a workforce that now includes both human employees and AI agents executing tasks in concert.

 reveals that 62% of C-suite executives are dissatisfied with how well people data connects to business performance, making it harder to translate strategy into workforce action. 麻豆原创鈥檚 new AI-enabled workforce planning capability directly addresses this, connecting financial data from 麻豆原创 Cloud ERP, contingent workforce data from 麻豆原创 Fieldglass, and people and skills data from 麻豆原创 SuccessFactors into a unified foundation for decision-making. Planning shifts from a periodic exercise to a continuous discipline, with AI agents able to model scenarios, simulate KPI impacts and recommend actions in real time.

New organisational modelling capabilities within 麻豆原创 SuccessFactors Employee Central allow leaders to explore alternative structures, evaluate changes to roles and reporting lines, and understand implications before they are implemented, supporting thoughtful, data-informed change rather than disruptive restructuring.

Ongoing efforts to close skills gap

Across every industry and every region, leaders are confronting the same reality: new roles are emerging, new competencies are required, and the processes that have defined work for decades are being reimagined at speed by generative AI.

Traditional approaches to workforce development, such as annual training cycles, standalone learning systems, and scheduled courses, cannot alone keep pace. The recently unveiled Workforce Upskilling Assistant offers a new capability that delivers personalised, AI-driven learning directly within the flow of daily work, whether that is in Microsoft Teams, Slack or 麻豆原创 SuccessFactors itself.

By converting organisational content into adaptive micro-learning and embedding timely nudges into workflows, the assistant brings the right learning to the right person at the right moment instead of when a calendar event dictates it. Organisations that embed learning into culture and workflows with AI-powered tools are significantly ahead of peers, and .

Trust is the foundation

None of this transformation succeeds without the trust of the workforce it is designed to serve. Accenture found that , while significant proportions report burnout and anxiety about the future of their roles.

Here, the emphasis that humans remain firmly in control reflects a commercial and human reality that enterprise leaders across our region understand well: technology that erodes trust fails. Technology that builds trust, endures.

The organisations that will lead this era are those that invest as intentionally in workforce readiness, reskilling and governance as they do in AI platforms and automation.

The Autonomous Enterprise is, at its heart, a vision for human progress, one where intelligent systems handle the operational burden and people are empowered to pursue the work that only they can do. 麻豆原创 is committed to helping organisations across the region build enterprises where that potential is fully realised.

The Beginning of Better starts with the people inside it.

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The introduction of agentic AI into the enterprise resource planning (ERP) space is set to underpin the era of the autonomous enterprise, according to Nazia Pillay, MD of 麻豆原创 Southern Africa.

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Pillay spoke to ITWeb TV recently about AI-infused ERP, the autonomous enterprise concept 鈥� which relies on an army of AI agents at its core 鈥� and how organisations need to adapt to benefit from the opportunity.

鈥淎utonomous ERP is not just about using AI to engage with end-users, to assist with navigation and pull data back; it is also about seeing agents as co-workers 鈥� systems that are working and performing tasks from end to end with human oversight,鈥� she said.

Pillay added that this fundamental shift requires change management for both human employees and business processes.

鈥淵ou are adopting a technology that fundamentally needs to be embedded, and your business processes need to be adapted for how you want to use this in your business,” she said. “That鈥檚 the main thing that our customers are facing 鈥� they need to look at this as a transformation and not just as a plug-and-play technology.鈥�

Pillay cautioned that curiosity about AI does not necessarily indicate organisational readiness. Success, she said, comes down to understanding business objectives and getting data foundations in place.

鈥淢ost of our customers are trying to figure out how to navigate this technology 鈥� how do I introduce it and how do I do it in a way that is adding value,” she said. “What we鈥檝e found is that when they use AI as a proof of concept or an experiment, they are not leveraging the full value of the technology.鈥�

Beyond experimentation, governance frameworks are essential, Pillay said.

鈥淢ost of the organisations that we are speaking to need to look at how they are deploying it at an enterprise level, because when you are just deploying it as a proof of concept, you don鈥檛 necessarily need to have all those frameworks in place.鈥�

Pillay said the wealth of data that clients have generated through 麻豆原创 installations provides a good foundation for companies shifting towards the autonomous enterprise.

鈥淲hat we know about AI is that it requires context 鈥� so you need to understand where the data is and how it is being used, and this we already have based on our knowledge graphs within 麻豆原创,” she said.

鈥淭he second part is governance 鈥� what 麻豆原创 puts into place to ensure that we have security. We have invested because we have the context and the governance to support our customers in having agents run in a responsible, reliable way.鈥�

With AI agents being deployed across end-to-end business processes, getting governance right will be key, she concluded.

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