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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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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Chris Shortt, chief technology officer at Shoprite Group, says the work of the next five years is meeting the customer at their point of need, wherever that is 鈥� and that this does not mean the end of the store.

Speaking on TechCentral鈥檚  podcast series, brought to you by NTT DATA, Shortt said Africa鈥檚 largest retailer is building towards a unified experience regardless of how a customer reaches it: ordering by voice from a home assistant, or walking in to choose their own tomatoes and their own fillet.

鈥淗ow do we blend that for you so that it鈥檚 actually how you want to do things, as opposed to how we might want you to do things,鈥� he said. The group鈥檚 store footprint, he added, is as alive and active as it has always been, and its point of presence in communities still makes a difference to customers.

Delivering on that promise rests on a real-time inventory layer, which is why modernising Shoprite鈥檚 core 麻豆原创 retail platform is the largest technology project the group has running.

You can listen to the podcast here:

Shortt also discusses:

• How the CIO/CTO鈥檚 job has changed, with the infrastructure now simply expected to work and the role shifting towards helping business counterparts understand the art of the possible;
• Why about 12 000聽, and how the gateway the group built picks the least expensive model that can handle a request;
• Why his product teams now pair one or two people with several agents, and what he put to the group鈥檚 chief people officer about recruits who arrive with agents of their own;
• Why he will not comment on other retailers鈥� ERP failures, and the 鈥渄ay in the life of鈥� testing he relies on instead;
• What he tells the audit and risk committee when they ask what keeps him up at night; and
• The book that shaped his thinking, and the productivity habit he will not work without.

Don鈥檛 miss a great discussion on the future of retail.

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Experts warn, however, that as machines take on more responsibility, accountability cannot also be automated.

For the past few decades, many governments鈥� digital footprints have largely been about transferring services online; that is, transforming paper forms into digital applications, replacing physical queues with online portals and turning manual registers into databases.

Many of the changes have made some government processes more accessible but they have not necessarily changed the way the system works.

Now, the rapid development of AI could usher in a different phase of digital transformation. AI agents are being developed to do more than answer questions. They can potentially plan tasks, make recommendations, process information and coordinate multiple steps in workflows with limited human intervention.

This could mean AI systems helping to process applications, manage records, identify exceptions, respond to citizen queries and route cases to the appropriate officials.

Gartner has projected that at least 80% of governments will be using AI agents to automate routine decision-making by 2028. The shift moves digital transformation away from simply automating individual administrative tasks towards coordinating entire processes.

The concept is being described as 鈥渁gentic AI鈥� 鈥� systems capable of carrying out multistep tasks rather than simply responding to individual prompts.

The idea is not necessarily to replace public servants. Instead, AI could take on some of the coordination and case-processing burden, while officials remain responsible for interpreting policy, dealing with exceptions and engaging directly with citizens.

The South African Revenue Service, for example, has been exploring AI-driven 鈥渄igital twinning鈥� approaches designed to replicate human decision logic in tax processing.

The potential benefits are clear: faster processing, greater consistency and fewer administrative bottlenecks.

But there is another question that becomes important as AI takes on more responsibility: who is accountable when the system gets it wrong?

For AI to be trusted in public services, accountability needs to be built into the system. This includes clear procurement standards, defined escalation procedures, comprehensive audit trails and named officials who have the authority to override or suspend an AI system.

Lance Williams, the public sector lead at 麻豆原创 Africa, says AI should be viewed as a tool for strengthening public administration rather than removing accountability.

鈥淭he real opportunity is to use AI to remove administrative friction, help public servants make better-informed decisions and ultimately improve the experience of citizens.鈥�

Williams says governance and accountability need to be considered. 鈥淎I agents should operate within clearly governed processes, with appropriate permissions, auditability and human oversight 鈥� but accountability for decisions that affect citizens must remain with authorised public officials.鈥�

He says the measure of success will be whether it helps government deliver services that are faster, more consistent, transparent and accessible.

Putting an AI agent on top of a broken or poorly designed administrative system will not necessarily produce better outcomes. Governments will need to examine their processes, data, policies and accountability structures before deciding where AI can add value.

The post AI Agents Could Change the Face of Service Delivery 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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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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A tech confession and a debate with an AI robot was the recipe for the future of work at 麻豆原创 Connect Southern Africa in Kyalami near Johannesburg last week.

鈥淚 have a confession to make: I have been afraid of AI,鈥� said Stanley Dube, 麻豆原创 head of solution advisory. Standing alongside a robot wearing a tracksuit carrying 麻豆原创鈥檚 AI assistant brand Joule, Dube said: 鈥淭he fear I have had is that AI is going to take my job.鈥�

However, having moved past that fear, he and Joule addressed questions about how organisations can adapt to AI, redesign jobs and processes, maintain governance and build a competitive advantage. In a theoretical example, Dube asked about the specifics of a project.

鈥淚 already know the status,鈥� answered Joule. 鈥淚 also know who is usually responsible for the delays: you, Stanley Dube.鈥�

Debate around AI replacing workers and robots taking control has become familiar, but 麻豆原创鈥檚 demonstration gave Joule a physical presence beyond a computer screen. The robot appeared to serve as a representation of the software assistant rather than a demonstration of autonomous physical AI. Although the presentation carried an element of theatre and Joule鈥檚 responses may have been preprogrammed, the exchange pointed towards a future in which AI agents could take on a more visible and collaborative role in the workplace.

Dube asked Joule: 鈥淲hat do we, as entities and companies, actually have to do to survive and thrive in the age of AI?鈥�

Joule answered: 鈥淢ost organisations do not have an AI problem; they have an adaptability problem. [This is] a massive waste of time and capital. I am built directly into your 麻豆原创 applications. While you slept, I autonomously reconciled 84% of your finance accounts, predicting a pump failure at Secunda to generate a maintenance order and rerouted logistics around a major issue.鈥�

Joule鈥檚 response framed the main obstacle as organisational rather than technological. AI systems may process information and act within seconds, but the benefit can be lost when decisions still depend on lengthy approval chains and slow human processes. In that situation, advanced technology becomes the equivalent of placing a Ferrari engine in a donkey cart: the system has considerable power, but the surrounding organisation cannot move at the same speed.

Dube said employees might find themselves arguing with AI agents in future, but that should not necessarily be a cause for concern.

Joule said: 鈥淩emember, AI is the enabler. Adaptability is the challenge. The future belongs to those who learn how to adapt because of AI, not just those who adopt.鈥�

The presentation extended a strategy outlined at 麻豆原创鈥檚 Sapphire conference in May. As , 麻豆原创 presented the Business AI Platform as an environment for developing and governing agents that work across enterprise operations. The approach uses ERP systems as the source of business data, processes and controls, allowing agents to perform tasks within established organisational boundaries.

Standard Bank provided a practical example of the preparation required to support 麻豆原创鈥檚 vision. The bank has modernised core enterprise systems using 麻豆原创 technology, establishing a platform from which further automation and AI applications could develop.

Vanessa Padiachee, Standard Bank Group CIO for corporate functions, said: 鈥淚f you look at the pace of change, technology is changing rapidly. AI is no longer just around experimentation and production use cases. Organisations are now going beyond that to look at enterprise scaling use cases.鈥�

Padiachee said 麻豆原创 was an important part of Standard Bank鈥檚 broader technology programme. She said organisations must manage rapid technological change while keeping major systems aligned with practical business requirements.

Standard Bank is focused on modernising the systems needed to support future automation and AI deployments rather than describing an autonomous AI implementation.

That work receives far less attention than demonstrations of agents and automated decisions. However, 麻豆原创鈥檚 autonomous-enterprise vision still depends on businesses having systems, processes and employees capable of supporting the technology.

Joule may have stepped beyond the computer screen at 麻豆原创 Connect, but the route towards an autonomous enterprise still runs through the less theatrical work of modernising systems, data and processes.

* Jason Bannier is a data analyst at World Wide Worx and deputy editor of . Follow him on .

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The company outlined its strategy at 麻豆原创 Connect Day 2026, held this week in Kyalami, Johannesburg.

, MD for southern Africa at 麻豆原创, said the company’s role is to support organisations in implementing and scaling agentic AI across end-to-end systems as they transition towards .

Addressing the complexity and cost that continue to challenge organisations migrating systems and platforms, , VP of customer advisory for EMEA at 麻豆原创, said the company’s new agent-led transformation approach could reduce the effort required for multi-year ERP migrations by 35%.

Agentic AI assistants

麻豆原创 has introduced several migration and modernisation AI assistants, including data management, custom code, configuration, test management and change management assistants. According to the company, these are governed by Joule for both developers and consultants and are designed to execute complex business scenarios by reasoning iteratively and interacting with their environment to achieve defined goals.

Nolla cited 麻豆原创 research indicating that the highest-ranked strategy for maximising ROI from AI is aligning initiatives with clear business goals.

麻豆原创 said AI currently supports about 30% of tasks in the average business, with that figure projected to reach 48% within two years.

, head of solution advisory at 麻豆原创, said a major challenge in AI-driven digital transformation is what the company describes as the “enterprise paradox” 鈥� the expectation of instant enterprise AI clashing with the operational reality of complexity, manual processes and human behaviour.

Standard Bank’s transformation journey

, executive group CIO for corporate functions at Standard Bank, outlined the bank’s use of 麻豆原创 technology to address operational challenges, including end-of-life systems, expiring vendor support and the need for a modern cloud ERP platform to support future innovation.

The bank completed a large-scale ERP migration and implementation programme. Modernising core enterprise systems required careful planning, strong governance and close collaboration between business and technology teams. The programme established a platform capable of supporting growth, operational resilience and ongoing innovation.

Padiachee said one of the biggest challenges was the pace of technological change.

“Organisations are required to continuously adapt while maintaining stability across critical business operations. Change management is just as important as the technology implementation itself,” she said.

Modernisation, she added, should improve agility without creating unnecessary disruption to business operations.

She also highlighted the human factor. “It’s really about keeping people calm during turbulence,” she said, adding that AI is fundamentally changing how businesses operate. “The focus should move beyond experimentation towards delivering measurable value.”

According to Padiachee, AI initiatives should align with clear business outcomes rather than be driven by the technology’s popularity.

She said one of the most significant lessons from the transformation programme was the complexity of Unicode conversion.

“In hindsight, this work should have started much earlier in the programme. Unicode conversion proved more complex than anticipated and became a critical dependency for implementation. Investing time in foundational technical readiness significantly reduces downstream project risk.”

Padiachee also stressed the importance of high-quality enterprise data that is accurate, trusted, well-governed and accessible.

She advised organisations planning system and platform migrations, as well as AI integration, to begin with a clearly defined use case, establish strong partnerships and maintain robust governance and guardrails.

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