麻豆原创 has announced a series of AI innovations at its annual 麻豆原创 TechEd conference, expanding its portfolio for developers. The enhancements to Joule Studio and Joule Work, as well as the 麻豆原创 Business AI Platform 鈥� including 麻豆原创’s latest tabular foundation model 鈥� offer new tools to build and extend AI agents.

麻豆原创 said its strategy is to advance the development and adoption of agentic AI by providing richer business and data context for agents, and governance controls to manage their use across organisations.

Philipp Herzig, CTO of 麻豆原创 SE, said the 麻豆原创 Business AI Platform, along with 麻豆原创 Knowledge Graph, 麻豆原创 Business Data Cloud, Joule Studio, Joule Work and agentic orchestration and integration capabilities, is now generally available.

鈥淭his entire platform is now generally available with all its capabilities, starting at the top with Joule Work, available on every device, every computer, in every browser. Joule Work is where you put your intent and get work done, in one experience across the entire 麻豆原创 landscape and beyond. That is the shift we are delivering at scale. Everything underneath runs on 麻豆原创 Business AI Platform.鈥�

Asked how 麻豆原创 connects with external AI agents entering the market and supports customers already using agents from other vendors, Herzig said the company follows an open strategy.

鈥淐ustomers say that if 麻豆原创 provides an agent in the core domains where they have 麻豆原创, there鈥檚 no need to build and own one. There are tons of agents out there, and we have made sure, via our agent gateway, that there is full interoperability. Our AI agent hub allows you to discover the agents, 麻豆原创 agents and non-麻豆原创 agents, and you can document them and make them compliant.鈥�

Herzig also addressed vendor lock-in and the challenges organisations face when using AI models from different vendors, discussing 麻豆原创鈥檚 approach to letting customers select models for specific use cases.

He said that tailoring a use case for optimisation inevitably brings some lock-in, but that model switchability remains viable for organisations keeping their AI use cases fairly generic.

AI to serve autonomous enterprise operations

Florian Kunzke, global director of AI strategy at 麻豆原创, echoed Herzig鈥檚 view that the company is moving from AI that learns how a business operates to AI that runs it, with the objective of mobilising AI to serve autonomous enterprise operations.

He said 麻豆原创鈥檚 AI strategy continues to evolve, particularly from a user interface and user experience point of view, with agents embedded in its technology suite.

Kunzke said Joule Work and Joule Studio, alongside 麻豆原创 Knowledge Graph 鈥� which provides a structured semantic foundation for Joule 鈥� are central to 麻豆原创鈥檚 AI strategy. The company aims to help organisations become autonomous enterprises while addressing challenges such as the impact of geopolitical tensions on supply chains, the need to improve productivity through AI investment, and the requirement to generate ROI.

He cited , released in August 2026. According to the research, 80% of respondents said AI had improved individual productivity, while 50% said it helped them make better decisions.

However, he stressed the need for perspective on how AI is being adopted and used across organisations. 鈥淚t is important to distinguish between what happens at organisational level and what happens at individual level. There is a gap, and that is what 麻豆原创 is looking to address,鈥� said Kunzke.

He also acknowledged that shadow AI, rogue AI, cyber security threats and the maturity of digital infrastructure continue to affect AI adoption. However, he added that 麻豆原创 is confident its technology empowers customers to select the right AI for a job, as well as the necessary level of control.

鈥淵ou can improve what you are able to observe and measure. With agentic solutions and processes, and the adoption of these processes, the question is what to aim for in terms of governance, what tools will the organisation need. It makes sense to have tools be as close as possible to the applications being used.鈥�

Independent AI expert Johan Steyn, founder of , said the shift to agentic AI marks an inflexion point. “AI strategy stops being about which tools you buy and becomes about how you orchestrate and govern agents,” he said. “Capability is becoming abundant; governance is the scarce advantage.”

Latest tabular AI model

麻豆原创 announced its latest tabular AI model, 麻豆原创-RPT-2.0, which will expand beyond classification and regression to support anomaly and outlier detection, and richer AI-powered analysis through embeddings.

According to 麻豆原创, these native, multi-table capabilities will further reduce manual engineering while delivering faster, deeper insights from structured data.

In partnership with HPE, 麻豆原创 also announced plans to deploy 麻豆原创-RPT-2.0 on the HPE Spaceborne Computer-2 aboard the International Space Station (ISS) in 2027.

The model is scheduled to run directly on the ISS to support anomaly triage, predict potential problems before failures escalate, reconstruct missing sensor values using historical data, and generate mission-health reports for astronauts and ground teams.

Herzig added: 鈥淭he same trusted 麻豆原创-RPT model that powers global supply chains can also work in space. 麻豆原创鈥檚 deep-rooted, proven expertise has led to the International Space Station鈥檚 use of our tabular foundation model to predict potential mission-critical failures in orbit. With 麻豆原创-RPT and TabPFN from Prior Labs, we bring the unique capabilities of tabular AI into the enterprise and across every frontier.鈥�

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麻豆原创 Business AI Platform gives organizations one place to build, contextualize and govern AI across 麻豆原创 and third-party environments. With Joule Studio, business users and developers can use no-code and pro-code tools to build and extend agents, choosing from more than 70 AI models. Customers in the Joule Studio early adopter program also can design and run n8n workflows in Joule Studio, with broader availability to follow. The 麻豆原创 Business Data Cloud and 麻豆原创 Knowledge Graph solutions ground those agents in trusted business data and context, helping organizations select the right model for each use case and better manage AI value and consumption. Importantly, 麻豆原创 customers鈥� data remains their data. 麻豆原创 does not share customer data with anyone to train or improve large language models.

鈥溌槎乖� TechEd has always been where builders shape what comes next,鈥� said Philipp Herzig, CTO of 麻豆原创 SE. 鈥淭his year, we are showing how we are moving from AI learning your business to running it. Joule Work is where you put your intent and get work done, in one experience across the entire 麻豆原创 landscape and beyond. That is the shift we are delivering at scale. Everything underneath runs on 麻豆原创 Business AI Platform, delivering faster time to value through out-of-the-box business understanding, higher accuracy, enterprise-readiness that is secure, governed and compliant from the first deployment, and a unique context layer built on over 50 years of process and industry expertise, encoded into every agent action. This is AI built where real work happens.鈥�

Joule Work and Joule Studio Make the Autonomous Enterprise Real

New capabilities for Joule Work will help make AI agents more effective at handling complex business processes via open protocols including Agent2Agent (A2A) and Model Context Protocol (MCP). These innovations advance Joule Work to better understand context, make decisions and execute multistep business workflows across enterprise systems and business domains:

• New agentic thinking capabilities that plan and orchestrate the steps assistants and agents need to reliably complete complex, multistep business workflows across 麻豆原创 landscapes.
• New capabilities in 麻豆原创 Knowledge Graph will incorporate data from additional 麻豆原创 and non-麻豆原创 sources that are unique to each customer. Users can query and explore their customized data in natural language and get accurate answers grounded in their own information.
• Advancements in interoperability allow third-party agents to securely invoke Joule Agents through a governed agent gateway. This enforced access control enables agents from different providers to collaborate across business workflows without sacrificing governance or control.
• New 麻豆原创 Enterprise AI Gateway solution will extend governance to AI interactions, offering a centralized way to manage AI traffic across the enterprise that will consolidate authentication, policy enforcement, routing, monitoring, usage tracking and auditing.
• New Joule Assistant and 麻豆原创 AI Agent Hub expand governance capabilities, and a new AI Governance Assistant helps customers meet compliance requirements for the EU AI Act. These are designed to address critical barriers to scaling enterprise AI: keeping AI systems classified, compliant and operating within defined organizational guardrails and context.

麻豆原创 Business Data Cloud as the Grounding Engine of the Autonomous Enterprise

麻豆原创 Business Data Cloud Ontology Manager in 麻豆原创 Business Data Cloud (麻豆原创 BDC) will provide an AI-native authoring experience for building ontologies across 麻豆原创 and third-party data. By helping AI understand how data from different teams, systems and applications relate to one another, AI agents can more accurately reason and make decisions across the enterprise. The solutions complement 麻豆原创 Knowledge Graph, which provides rich business context for 麻豆原创 data.

Additionally, 麻豆原创 is expanding its lakehouse capabilities with Dremio, adding native support for Apache Iceberg and Apache Polaris plus a high-performance, federated query engine that will enable unified data access without moving or copying the data. 麻豆原创 expands its partnership with Databricks, bringing the full scope and latest innovations of data engineering and enterprise AI capabilities into 麻豆原创 Business Data Cloud as 麻豆原创 Databricks. Core innovations such as Lakeflow, Agent Bricks, Unity Gateway, and Lakebase will help customers to simplify data management and provide a strong data foundation for their AI journey. With a deeper integration to 麻豆原创 Business Data Cloud, a new 麻豆原创 Application Data Foundation, as well as an integration between Joule and Genie, 麻豆原创 and Databricks are helping customers establish an enterprise-wide lakehouse as the foundation for the Autonomous Enterprise and Joule Work.

The 麻豆原创 Reltio solution, recently announced at 麻豆原创 Connect, will bring multidomain master data management to 麻豆原创 BDC. It unifies and enriches customer, supplier and product records across 麻豆原创 and non-麻豆原创 systems into trusted, reusable data products that can power applications and AI agents.

Tabular Foundation Model Trusted for Business, Validated in Space

麻豆原创 announced its latest tabular AI model, 麻豆原创-RPT-2.0, which will expand beyond classification and regression to support anomaly and outlier detection, and richer AI-powered analysis through embeddings. These native, multi-table capabilities will further reduce manual engineering while delivering faster, deeper insights from structured data. In addition, a new 麻豆原创-RPT-TS model will help customers predict values across time-indexed business metrics for key forecasting use cases using the 麻豆原创 Integrated Business Planning solution without requiring custom machine learning.

In partnership with HPE, 麻豆原创 also announced the deployment of 麻豆原创-RPT on the HPE Spaceborne Computer-2 aboard the International Space Station (ISS), one of the most significant edge-compute validations of any enterprise AI model to date. 麻豆原创-RPT is planned to run directly onboard the ISS to support anomaly triage, predict issues before failures escalate, reconstruct missing sensor values from historical data, and generate explainable mission-health reports for astronauts and ground teams 鈥� all without requiring data to be sent back to Earth for analysis.

鈥淭he same trusted 麻豆原创-RPT model that powers global supply chains also can work in space,鈥� said Herzig. 鈥溌槎乖粹�檚 deep-rooted, proven expertise has led to the International Space Station鈥檚 use of our tabular foundation model to predict potential mission-critical failures in orbit. With 麻豆原创-RPT and TabPFN from Prior Labs, we bring the unique capabilities of tabular AI into the enterprise and across every frontier.鈥�

Out-of-the-Box Assistants and Agents Define Our Advantage

For five decades, 麻豆原创 has built the systems of record that run your business. We will deliver, out of the box, a suite of assistants and agents across five horizontal domains all running on 麻豆原创 Business AI Platform 鈥� with industry specialization 鈥� that provides a baseline for systems of intelligence and systems of action.

Ecosystem Partners Help Expand Platform Capabilities

The global 麻豆原创 partner ecosystem brings new capabilities to the ongoing operation and extension of solutions on 麻豆原创 Business AI Platform, helping customers advance toward the Autonomous Enterprise. At 麻豆原创 TechEd, the following partner announcements were highlighted:

A collaboration with Intel closes the loop between enterprise workflows and autonomous robots, bridging informational technology and operational technology through physical AI.

In addition, new 麻豆原创 solution extensions with , and extend autonomous capabilities for customers, helping them run their core processes end to end, without handoffs between systems.

Beyond technology, 麻豆原创 is empowering its strong developer community with new enablement and engagement with tens of thousands joining for Devtoberfest leading up to 麻豆原创 TechEd, new reference architectures, tutorials and hands-on opportunities attendees can try onsite at the event and at home.

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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� 

This article first appeared in .

The post The AI Will See You Now appeared first on 麻豆原创 Africa News Center.

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.

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.

This podcast first appeared on .

The post Meet the CIO | Shoprite鈥檚 Chris Shortt on What a Supermarket Becomes appeared first on 麻豆原创 Africa News Center.

NTT DATA is a $30+ billion global leader in AI, digital business and technology services, serving 75% of the Fortune Global 100.

The 麻豆原创 solutions will help NTT DATA replace multiple legacy HR systems with one unified platform for people data and processes, strengthening decision-making, employee experience and workforce planning. 麻豆原创 SuccessFactors solutions will serve as the system of record for people and talent data, working alongside NTT DATA鈥檚 existing employee service platform and specialist workforce planning tools. 麻豆原创 Business Data Cloud will connect this data with insights across other business functions.

鈥淭he initiative reflects NTT DATA鈥檚 view of talent as a strategic differentiator and AI as a capability that should be embedded across enterprise organizations,鈥� said , Chief People Officer, NTT DATA, Inc. 鈥淯ltimately, this is about creating an environment where our people feel empowered to do their best work, wherever they are in the world.鈥�

The deployment builds on a strategic partnership between 麻豆原创 and NTT DATA spanning more than 36 years, during which NTT DATA has collaborated as an 麻豆原创 platinum partner, global service partner and global reseller for 麻豆原创. In 2025, NTT DATA adopted 麻豆原创 Cloud ERP Private solutions to modernize its core systems.

麻豆原创 SuccessFactors solutions will now be deployed internally at NTT DATA over a 12-month period, with the company applying its own 麻豆原创 expertise to design and roll out the platform. This will create a single, authoritative source of HR data and processes, laying the foundation for faster, more consistent HR services across the organization. Leading its own implementation will also strengthen NTT DATA鈥檚 ability to guide clients through AI-driven HR transformation, with firsthand experience of the solutions it delivers.

鈥淣TT DATA is demonstrating how AI and cloud technology can redefine the employee experience,鈥� said , Chief Customer Officer and Member of the Executive Board of 麻豆原创 SE. 鈥淲ith a unified, intelligent HR platform, the company will unlock new levels of productivity and scale a people strategy that supports a connected workforce worldwide.鈥�

The post 麻豆原创 Brings 麻豆原创 SuccessFactors and Joule to NTT DATA鈥檚 Global People and Culture Transformation appeared first on 麻豆原创 Africa News Center.