Not so, says , MD: Southern Africa at 麻豆原创.

鈥淓RP is no longer just a system of record,鈥� she tells delegates to 麻豆原创 Connect taking place today in Kyalami.

鈥淲ith agentic AI, we can ensure it runs end-to-end business processes, creating autonomous ERP.鈥�

Today, AI supports 30% of tasks in the average business, according to an Oxford Economics study. And this is projected to rise to 48% in the next two years.

vice-president: customer advisory for EMEA at 麻豆原创, points out that around 40 000 of 麻豆原创鈥檚 customers have already adopted 麻豆原创 Business AI in their productive systems.

鈥淎nd we are learning from that,鈥� he says.

Nolla believes 麻豆原创 is well placed to help customers adopt AI, through its 麻豆原创 Autonomous Enterprise vision.

The key components of this vision are three-fold

鈥淵ou have the capability to bring teams long with one engagement layer 鈥� Joule.

鈥淭eams connect with embedded agents and AI in 麻豆原创 Autonomous Suite, using best practices and processes.

鈥淎nd customers have the ability to extend agents, maintaining context and reasoning, and ensuring governance with .鈥�

Nolla explains that is the engagement layer that connects experts in the business with the AI processes.

It has been available for some time and has been widely used to unify the AI experience. It has also been updated along the way, for instance with Joule Consultants that have more than 100 certifications.

A few weeks ago, 麻豆原创 announced additional evolutions. leveraging Harness-powered Joule and Joule Work to create spaces for specific tasks.

鈥淛oule is the engagement layer to bring the people along,鈥� Nolla summarises.

Adding the processes and best practices, 麻豆原创 Autonomous Suite embeds AI scenarios throughout the portfolio, from HR, customer service, supply chain, finance and industry solutions.

With hundreds of agents available, Joule Assistants help workers by bringing together the relevant agents to perform specific tasks.

鈥淭hink of it as a new team member helping staff accomplish their goals,鈥� Nolla says.

In addition, 麻豆原创 Industry AI is AI that already knows how specific industries operate, so the system can work with context.

鈥淏ut does AI have a true understanding of your business? Is it aligned to your KPIs, and does it have the governance in place?鈥� Nolla asks.

鈥溌槎乖� Business AI Platform brings these capabilities to the business.鈥�

With Joule Studio giving workers the ability to create new agents on the fly, the possibility of hundreds 鈥� even thousands 鈥� of agents within the business could become unwieldly.

鈥淪o governance is more important than ever,鈥� Nolla says. 鈥淭he AI Agent Hub gives IT the ability to connect and understand these agents. They can discover, manage and govern all the agents in one place.

鈥淭his gives the business power, control and trust.鈥�

Within the next few months, 麻豆原创 will also release enriched agentic AI tools and assistants to help with data management, configuration, development and testing to improve migration projects.

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Companies are reviewing their ERP strategies as they balance digital transformation goals with tighter budgets, operational complexity and the need to demonstrate measurable business value, they say.

Andrew Strachan, director of sales at Spinnaker Support Southern Africa,  companies across Africa are reassessing ERP support strategies as decision-makers seek to modernise their environments while managing costs.

“Enterprise software decisions are not like other technology decisions,” said Strachan. 鈥淭imelines are longer, dependencies run deeper and the cost of reversing course 鈥� if reversing course is even possible 鈥� is often measured in years and very substantial sums.”

According to Strachan, the relationship between a major ERP vendor and its customers rests on more than product capability alone. “It is built on trust: trust in the roadmap, trust in the commercial model and trust that the terms you commit to today will not be materially redefined once your business is locked in.”

Gerhard Alberts, head of customer evolution at 麻豆原创 Africa, said trust is ultimately measured by business outcomes.

鈥淲hile customers rightly expect transparency and choice, the greater risk in today’s competitive environment is often the cost of inaction,” he said. “Ultimately, success is measured not by the technology deployed, but by the operational improvements, agility and competitive advantage it enables.鈥�

Alberts said the pace of technological change means companies must adopt relevant technologies to remain competitive, and that customers must be equipped with the most relevant tools to navigate a shifting market. “By not innovating, customers run the risk of attracting direct and potential costs in terms of system and infrastructure stability, security and compliance. This results in an inability to stay competitive across multiple markets due to a lack of agility.鈥�

Strachan said new platforms and regular vendor upgrade announcements are adding pressure on companies already facing constrained budgets, skills shortages, infrastructure complexity and currency pressures.

鈥淚n South Africa and across the African market, large technology decisions are made under intense scrutiny,” he said. “Boards and executive teams are balancing growth ambitions against currency pressure, constrained capital budgets, skills shortages, infrastructure complexity and the need to show clear business value from every major investment. In that context, organisations cannot afford to keep revisiting foundational ERP decisions every few years because the vendor鈥檚 commercial priorities have changed.鈥�

Migration is not modernisation

Courtney Hounsell, client experience manager at Microsoft managed partner Braintree, said AI is reshaping the role of ERP platforms while exposing the limitations of legacy systems. He said outdated ERP infrastructure is hindering AI adoption because many companies mistake cloud migration for digital modernisation.

鈥淢igration is not modernisation,” Hounsell said. “Unfortunately, many companies have merged the two, and the result is a gap that鈥檚 creating incoherence and a lack of visibility, constraining their ability to benefit from AI and its capabilities.”

He explained that cloud migration has been sold as digital modernisation, but this only changes the address of the system. Real modernisation, he said, changes architecture, data models, integration logic and the capacity to generate value. “Companies want their modernisation projects to actively reduce other costs, show long-term ROI and empower their staff with systems that work faster and smarter. So when migration and modernisation are confused, companies end up paying cloud prices for on-premises problems and arrive in the AI era without the foundations required to ensure AI can function effectively.”

Hounsell said the gap between technology investment and business outcomes is widening, leaving companies with limited AI capability and poor data visibility. He cited McKinsey’s “The State of AI in 2025” report, based on a survey of 2 000 executives in 105 countries, which found that 88% of companies have adopted AI in at least one business function.

He said on-premises ERP systems struggle to support AI because data must be extracted, cleaned and transferred before analysis.

鈥淥n-premises ERP systems are struggling uphill within the AI economy,” Hounsell said. “They hold their data in local databases, and to make this usable for AI, it has to be extracted, cleaned and pushed somewhere accessible. Without real-time data pipelines feeding into core systems, AI deployments remain siloed and limited.鈥�

Braintree said ERP modernisation also delivers financial benefits, citing the Forrester Total Economic Impact study of Dynamics 365 Business Central, which found that companies could achieve a 265% ROI, with productivity gains of 12.5% in operations, 15% in sales and 15.6% in finance.

The study also found that companies that modernised successfully avoided more than $30 000 a year in third-party consulting fees, while cloud ERP platforms reduce infrastructure costs and receive automatic updates, including new AI capabilities.

Braintree argued that choosing the right migration partner is as important as selecting the ERP platform itself.

Alberts added that technical debt continues to grow as companies rely on siloed data, ageing infrastructure and extensive custom code, while shortages of legacy skills compound the problem.

鈥淥ver time, doing nothing to save money becomes the most expensive system to maintain, together with the inability to adapt, which has the potential to result in the demise of some organisations,鈥� he said.

Arthur Goldstuck, CEO of World Wide Worx,  after 麻豆原创 Sapphire 2026 that 麻豆原创 CEO Christian Klein described ERP as the “brain” of every business in the age of agentic AI. Goldstuck quoted Klein: 鈥淔or over 15 years, we have been developing an ERP with incredibly deep process and data domain know-how. On top of that, all your governance requirements and customer-specific extensions are stored in the ERP. The ERP is the trusted system of execution running your company.鈥�

According to Garth Ridgway, senior director for 麻豆原创 at NTT DATA, misconceptions about implementation timelines, affordability and suitability for mid-market companies continue to delay modernisation.

“Many executives still assume ERP transformation requires years of disruption and significant upfront investment,” Ridgway said. “In reality, modern cloud ERP platforms have evolved considerably, enabling organisations to adopt best-practice processes more rapidly, scale according to business needs and realise value far sooner than was historically possible.”

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The next phase of artificial intelligence (AI) should be shaped with Africa, not simply about or for the continent, according to 麻豆原创’s Director of Global Government Affairs and CSR for Africa, .

His comments follow the launch of the , announced yesterday by international leaders to expand access to AI, strengthen trust and increase its social and economic impact.

The commission brings together representatives from governments, businesses and international organisations to identify practical ways to unlock AI’s potential while promoting equitable access to the technology.

Its inaugural meeting will take place during the International Telecommunication Union’s (ITU) AI for Good Global Summit, from 7鈥�10 July in Geneva, Switzerland.

The summit forms part of Digital Week, running from 6鈥�10 July, alongside the first UN-mandated Global Dialogue on AI Governance and the WSIS Forum 2026.

Geness, who will participate in discussions at the summit, said Africa should approach global AI governance with a clear agenda centred on economic growth and inclusion.

“Africa must meet that room with clarity, not caution. Our agenda should be simple and bold: AI governance that expands prosperity.

“That means compute access, skills investment, trusted data systems, open standards, local-language innovation, accountable public procurement, and regulation that protects people without suffocating entrepreneurs.

“It means turning the African Union’s Continental AI Strategy from a document into national roadmaps, investment pipelines and regional co-operation. This is technology diplomacy: 54 nations aligning where they can, rather than negotiating as 54 separate voices. This is where I hope to add value.”

Africa’s representation on the commission has been strengthened by the appointment of Rwandan President Paul Kagame as co-chair alongside Salesforce Chair and CEO Marc Benioff.

According to the ITU, the commission aims to promote equitable access to AI and help narrow the global digital divide.

The organisation said an estimated 2.2 billion people remain offline, leaving around a quarter of the world’s population excluded from AI-driven opportunities.

“A key focus of the AI for Good Global Commission will be to bridge digital divides and help ensure that AI becomes a tool for solving global challenges, not deepening inequalities,” the ITU said.

President Kagame said technology should reduce inequality and broaden access to AI’s benefits.

“One thing is certain: technology is supposed to be a force for good, and we have a responsibility to use it accordingly. Let us work together to reduce inequality and allow more of our citizens to benefit from the good AI can deliver to all of us.”

Benioff said AI’s economic potential depends on maintaining public trust.

The promise of AI is built not only on incredible opportunities for economic growth, but on the foundation of trust required for our shared success.”

ITU Secretary-General Doreen Bogdan-Martin, vice-chair of the commission, said collaboration across sectors would be essential to ensure AI benefits people globally.

“No organisation can single-handedly put AI at the service of all humanity. It will take collective leadership and the combined expertise of partners across sectors to ensure AI benefits everyone, everywhere.”

This article first appeared here:

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, Head of Cloud ERP, 麻豆原创 Business Suite for 麻豆原创 Middle East & South Africa, on why the road to the autonomous enterprise still runs through clean core, trusted data, and the people who must live with the change.

Christian Klein gave Sapphire 2026 its most quotable line when he said no AI agent can compensate for a bad data landscape. For the Middle East and Africa markets that聽Pieter Van Der Merwe聽covers at 麻豆原创, that reads more like a starting gun. Van Der Merwe advises customers across the UAE, a region split between government-mandated agentic AI in the Gulf and parts of Africa still securing their first foundational cloud workloads, yet all staring down the same 2027 ECC deadline.

, he gave a working practitioner鈥檚 view of the market. He was candid about where RISE programs stall, why the first year is organizational change wearing a technology costume, and why prized customizations are often historical workarounds in disguise.

麻豆原创insider: 麻豆原创鈥檚 Africa messaging has intensified throughout 2026, and the autonomous enterprise framing surfaced early from MEA leadership. Is this a pivot, and why does the idea land here first?

Pieter Van Der Merwe: Africa has always been a growth market; what changed is the convergence of cloud ERP, business AI, data sovereignty and skills development. Some African enterprises carry less embedded legacy customization and can move faster. Others run highly customized environments built over years around payroll, procurement, and financial reporting.

The framing of a business that can sense, decide, and act with minimal human intervention resonates here for practical reasons, including resource constraints and the opportunity to leapfrog rather than replicate the mature-market journey. The opportunity is around clean cloud ERP, trusted data and embedded AI as the foundation for more agile, competitive organizations.

麻豆原创i: Africa and the UAE are in very different places. What does a realistic autonomous enterprise roadmap look like for these regions in 2026?

Van Der Merwe:聽It starts with the unglamorous essentials such as modernizing the core and fixing the data foundation. Our CEO, Christian Klein, noted at Sapphire 2026 that no AI agent can compensate for a poor data landscape, so good data is the prerequisite for everything else.

The near-term value lies in the embedded AI scenarios that already live in RISE, such as finance automation, HR workflow and supply chain exception management. These are switched on inside existing 麻豆原创 Cloud environments. The agentic layer arrives as the foundations mature. The African and Middle Eastern organizations well-positioned in 2028 are the ones investing in that foundation now, even when the autonomous vision feels distant.

麻豆原创i:聽When a customer commits to RISE, what do the first 12 months look like, and where does it diverge from expectations?

Van Der Merwe: The first 12 months are a business transformation program disguised as a technology migration. The technology, 麻豆原创 S/4HANA Cloud, infrastructure, and licensing are relatively straightforward. The depth of organizational change, such as how people work, how decisions are made, and how processes are structured, catches organizations off guard. Without governance and stakeholder engagement, even the best platform underdelivers.

The most common divergence is data readiness. Poor-quality, inconsistent master data surfaces early and erodes user trust, which is hard to recover from. The second is customization. Organizations underestimate how much of the current configuration is genuine business logic versus historical workaround, and that distinction is decisive for clean core. One welcome change post-Sapphire is that RISE customers get three Joule Assistants activated in their first year, giving a tangible AI experience during the foundational phase.

麻豆原创i: Post-Sapphire, are customers feeling more urgency on the 2027 ECC deadline, or using the AI vision as a reason to pause?

Van Der Merwe: The 2027 deadline is real. 麻豆原创 has made it clear that ECC customers who commit to migrating most of their landscape to 麻豆原创 Cloud ERP gain access to select AI scenarios in the interim, so there is a bridge. The best-placed customers when agentic capabilities mature are those already running clean-core 麻豆原创 S/4HANA, because that is the foundation the autonomous suite is built on. Waiting for the full vision before starting the foundation is the wrong sequence. If anything, the bigger risk is reorganizing priorities too ambitiously and trying to tackle everything at once.

麻豆原创i: Clean core is the prerequisite for everything 麻豆原创 promises, but in heavily customized landscapes, it can feel like undoing years of work. How do you win a buy-in?

Van Der Merwe: By reframing what those customizations represent. Some encode genuine differentiation, process logic that reflects how the business truly operates, and deserve to be preserved as extensions on  using clean-core methodology. But much of the customization in mature landscapes is a historical workaround, through integration patches and decisions made under time pressure decades ago.

The conversation must involve finance, operations, and process owners. The framing that works is forward-looking: what does clean core unlock, specifically the AI scenarios now in RISE, and what does it cost to keep maintaining the current complexity? As support deadlines approach, that calculation changes sharply. The people dimension matters too. Teams whose identity is built around managing customized systems need a new value proposition of supervising AI-augmented processes rather than wrestling with complexity.

麻豆原创i: The autonomous vision depends on trusted data and standardized processes. How prepared are organizations, and are some underestimating the foundational work?

Van Der Merwe: Both are genuinely hard, and neither is solved at contract signing. Data readiness is the most underestimated challenge. 麻豆原创鈥檚 platform response- 麻豆原创 Business Data Cloud (BDC), Knowledge Graph and the Reltio acquisition- is meaningful, but zero-copy sharing and semantic enrichment do not substitute for master data governance.

Process standardization is the other half. The autonomous suite is built around standardized, cloud-aligned processes, so organizations that customized for years face a real renegotiation. The most forward-thinking organizations treat it as an opportunity to redesign processes for the AI era, rather than replicate old ones in a new system.

麻豆原创i: Where do RISE programs most commonly stall, and what separates the customers who push through?

Van Der Merwe: The stall points cluster in three areas: data-readiness failures that force unplanned remediation; change-management breakdowns where the business disengages and IT drives alone; and integration complexity across mixed 麻豆原创 and non-麻豆原创 landscapes. Those who push through have the qualities of change leadership, such as engaged executive sponsorship, quick decision-making governance, and a willingness to standardize where standardization is available. The technology is mature and stable; the implementation process and organizational alignment are where programs succeed or fail.

麻豆原创i: Joule was positioned at Sapphire as 麻豆原创鈥檚 new front door. What does it mean for a customer mid-journey on RISE?

Van Der Merwe: Customers should be thinking about Joule now, even if full deployment is progressive. The positioning of Joule Work is a significant UX shift. For customers mid-journey, the practical question is which Joule capabilities are already available in their current RISE configuration and whether they are activating them, because the embedded scenarios and early assistant capabilities do not require a completed transformation. The broader Joule Work experience, across web, desktop, mobile and voice, moves to general availability in H2 2026. That timeline is closer than many realize.

麻豆原创i: 麻豆原创 has more than 295 AI-powered scenarios embedded across its applications. Which are getting real traction, and which are still promising on paper?

Van Der Merwe: The strongest traction is where AI is embedded directly into existing workflows, and the value is obvious: finance, HR, procurement, supply chain, and customer service, because they involve high-volume processes and measurable outcomes. Think faster financial close, guided buying and invoice processing in procurement, demand sensing and exception management in supply chain.

Where a use case stays more promising than practical, the problem lies with the customer environment. If data is fragmented, processes are inconsistent, or governance is immature, even strong AI struggles to deliver value at scale.

麻豆原创i: The autonomous enterprise means AI executing actions humans perform today. How are the most forward-thinking customers approaching the human side?

Van Der Merwe: Organizations must treat AI as a work redesign strategy, not a replacement strategy. Employees move from repetitive execution toward supervision, exception handling, judgment, and continuous improvement. People must understand why the organization is changing and what skills keep them valuable, or AI copilots become another layer nobody trusts or uses.

It also demands reskilling, like learning to work with AI, interpreting its recommendations, and escalating exceptions where accountability still rests with people. The most mature organizations build cross-functional teams across business, technology, operations, compliance and change. The principle is simple: autonomous does not mean human-free. It means people focus on higher-value work while intelligent systems handle the repetitive and predictable.

麻豆原创i: Have you seen the RISE-to-autonomous journey deliver something genuinely different from previous 麻豆原创 transformation cycles?

Van Der Merwe: The example of IBM migrating from 麻豆原创 ECC to 麻豆原创 Cloud ERP Private across 175 countries and more than 150,000 users in 18 months is instructive. The move contributed to over $4.5 billion in productivity savings. What is different in this cycle is the speed and scope. A transformation at that scale would once have taken far longer, and the AI tooling now applied to the migration itself, reducing effort by up to 35% on 麻豆原创鈥檚 own figures, is part of that story.

In advanced deployments, the qualitative shift is from static reporting to active intervention. What feels genuinely new is the convergence of data, AI and process in a single governed environment, delivering systems that learn and adapt in real time.

麻豆原创i: What is the one piece of advice you would give to a CIO or IT director in the Middle East and Africa navigating the 2027 deadline and autonomous vision?

Van Der Merwe: Do not treat these as separate issues. The 2027 deadline, the move to cloud ERP, and the autonomous enterprise are the same strategic decision: How ready your organization is for the next decade. The immediate priority is to modernize the core and fix the data foundation, because without it, AI stays trapped in isolated use cases. Then focus on business value: the processes where AI can protect revenue, improve cash flow, reduce risk, or unlock capacity. Do not modernize for technology鈥檚 sake.

Finally, bring people in early. This is as much a leadership, skills, and operating model shift as a technology one. My strongest advice is simple. Start now, start with the core, and build a roadmap that integrates migration, data, AI, and people into a single transformation agenda.

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For years, companies throughout Europe, the Middle East and Africa have experimented with AI by testing chatbots, piloting analytics tools and exploring generative AI features. Some pilots have delivered incremental gains, while many others have stalled.

This year, AI can no longer be treated as experimentation at the edge of the business. It must become embedded at the core.

Embedded AI, intelligence that lives directly inside enterprise processes, is now one of the most powerful levers available to boards seeking profitability, productivity and long-term competitiveness. Embedded AI represents a profound shift from AI features to AI-native operations.

From AI features to AI-native operations

In the past, AI sat outside core systems, relegated to dashboards, standalone analytics tools or isolated pilots. Today, it is moving directly into transaction flows: inside enterprise resource planning (ERP), supply chain planning, finance, procurement, human resources and customer experience systems.

Embedded AI is context-aware, action-oriented and governed. It understands the role, process and underlying business data of tasks, triggers and automate processes, and operates within enterprise-grade security, compliance and data frameworks.

In practical terms, this means a chief financial officer can receive automated explanations for financial variances directly inside the closing process. A procurement leader can generate intelligent category recommendations within sourcing workflows, and a planner can optimise production schedules in real time without switching systems.

Business leaders are confronted with a range of unique regional pressures. Demographic shifts in Europe, combined with regulatory complexity, are placing demands for higher productivity on smaller workforces. In the Middle East, national AI strategies and diversification agendas require faster innovation cycles; while in Africa, youthful talent and digital acceleration present enormous opportunity, but only for companies that can scale efficiently and govern complexity.

The case for embedded AI as board-level issue

Boards are broadly asking the same questions: how do we protect margins; how do we increase throughput without increasing headcount? and how do we move from reactive decisions to predictive ones?

Embedded AI answers these questions, not as a technology initiative but as a shift in operating model. In 2023 and 2024, AI initiatives were built for experimentation. Last year and in the year ahead, the focus has shifted to unified AI platforms built on clean data foundations, and delivering measurable business outcomes.

There is one consistent lesson across our customer base: AI only works on a clean foundation.

Embedded AI requires harmonised processes, standardised ERP systems and high-quality data. That is why clean core strategies and unified AI infrastructures are so critical.

AI-native enterprises are consolidating scattered pilots into governed platforms. 麻豆原创鈥檚 ,  and  form a unified backbone where models, data, identity, security and extensibility operate together instead of in silos.

Without a unified approach, organisations face duplicated models, inconsistent decisions and uncontrolled risk exposure. With it, ERP becomes a strategic AI engine.

The measurable impact of embedded AI is already evident. In HR, embedded intelligence in  is enabling skills-based workforce planning directly within manager workflows, aligning talent decisions to strategic priorities. Across finance functions globally, AI-assisted explanations and reconciliation processes are reducing manual effort by up to 70% while improving transparency and auditability.

Board-level priorities

For boards and executive teams across southern Europe, the Middle East and Africa, three priorities stand out:

1. Treat embedded AI as part of core architecture, not as an optional feature.
2. Invest in clean data and standardised processes to unlock higher value automation.
3. Measure AI success in terms of profitability, cashflow protection, throughput and resilience, and not by pilot counts.

AI is now a core lever of competitive advantage, but only when paired with process redesign, data discipline and an empowered workforce. In 2026, the companies that lead will not be those experimenting at the margins. They will be those embedding intelligence into the heart of their business, turning ERP systems into engines of prediction, optimisation and growth.

At 麻豆原创, we are committed to supporting this journey: helping organisations across EMEA South build intelligent, resilient enterprises where embedded AI drives measurable business value, today and into the future.

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The Bellville Civic Centre in Africa now has a “digital twin,” a virtual copy that uses 1,200 sensors and a drone to watch everything in real-time. This smart system helps fix problems before they start, like finding leaky pipes or saving energy.聽It’s built for Cape Town’s exact needs, not just copied from Europe,聽and even helps with social issues and teaches people about upkeep. Now, this building can “talk” and help the city run better and save money.

What is a digital twin and how does it work?

A digital twin is a virtual model of a physical object or system, updated in real-time with sensor data. For Bellville Civic Centre, 1,200 sensors, a LiDAR drone, and a cloud physics engine created a digital replica, allowing real-time monitoring and predictive maintenance. This helps identify inefficiencies and potential issues proactively, optimizing building performance.

1. A Quiet Thursday That Changed Everything

The janitor鈥檚 clipboard still showed the same temperature columns, the electricians still argued about fluorescent tubes, and the leak in the basement still had to be fixed before the next council session. Yet, beneath this ordinary choreography, Bellville Civic Centre had quietly grown a second brain. Overnight, 1 200 credit-card-sized sensors, a spinning LiDAR drone, and a cloud physics engine 60脳 faster than real life stitched themselves into an invisible mesh. The concrete giant could now interrogate itself the way a doctor interrogates a heartbeat – asking, not just answering.

Inside the foyer, a Finnish suitcase snapped open. Out came matchbox fog-computing gateways, a stack of district-heating printouts from Helsinki, and – most convincing of all – signed data-sharing contracts with Nordic utilities that proved the jump from pilot to portfolio was already bankable. A projector threw the building鈥檚 new pulse onto the marble wall: cool blues for steady organs, warning reds for arrhythmias. One red vein leaked two-percent inefficiency from a chilled-water pump; another showed a third-floor zone that had spent three nights 0.4 掳C warmer than its set-point. A fire-exit sensor whispered that someone was forcing the door with three extra newtons – hinges beginning to seize.

In the old script those micro-clues would have become phone calls, Excel rows, and work orders already yellow with age. Now the digital twin ranks every intervention by life-cycle cost, authors its own weekly 鈥渕aintenance dance card,鈥� and beams it to handhelds. The deputy director tapped the red stairwell on the screen; instantly the model surfaced a 2024 actuator invoice, cross-checked the manufacturer鈥檚 mean-time-to-failure curve, and advised a swap before quarter-end. Predicted disruption: twelve minutes during a planned fire-system test. Likely downtime prevented: eleven hours. The Finns grinned; they had seen an Espoo courthouse cut annual energy use 18 % after only six months of the same ritual.

2. Built for Cape Town, Not Copy-Pasted from Europe

Espoo doesn鈥檛 stage blackouts; Cape Town does. Procurement here demands three quotes for anything above R2 000, and one depot may serve 250 scattered sites. So the architects tucked palm-sized, fan-less edge servers into electrical risers. When the fibre dies, these nodes keep a compressed, privacy-scrubbed clone of the twin alive, steering local loops: rooftop PV, battery UPS, smart-lighting relays. During outages, the conference wing stays lit on a 60 kWh buffer while corridors dim to 30 %, trimming 12 kW of peak without a human finger.

Sensor playlists are equally mixed. Some wings are 1970s concrete, others 1990s steel sheds, others brand-new mass-timber chasing net-zero. Thread, LoRaWAN and Wi-Sun radios chat in parallel; legacy breaker panels wear NFC tags that any TVET-college student can reflash in 90 seconds. Instead of marrying one vendor, the City hosts an 鈥淢-Bus to MQTT鈥� translator whose YAML recipes live on GitLab under Creative Commons. The only lock-in is curiosity.

Citizen privacy is engineered, not promised. Visitors renewing licences trigger anonymised heat-maps that forget faces after 900 seconds. Maintenance staff sign in with biometric fobs; an HVAC tech may raise a chiller set-point yet cannot unlock court archives. University researchers receive time-boxed API tokens that auto-expire without two-key renewal inside the firewall. The mantra is 鈥渟hare insights, not identities.鈥�

3. From Valves to Social Infrastructure – Ripple Effects No Spreadsheet Predicted

Borrowing from shipyards, the Finns gave every component a tamper-proof digital passport. Snap a photo of a corroded valve and the hash, signature, and timestamp lock into a private Ethereum fork. Smart contracts release micro-payment to the plumber only when AI vision confirms the fix. Early runs show disputed invoices down 35 % and warranty cycles shrinking from months to days. The building pays for proof, not promises.

Energy planning dives deeper than kilowatt dashboards. The twin ingests Time-of-Use tariffs, Eskom鈥檚 wind forecasts, and weather-service gust predictions to build a 72-hour optimiser. In a late-June rehearsal it slid 210 kWh from evening peak to pre-dawn, pre-cooling slabs and charging fleet EVs. Saved: R3 420 – one librarian鈥檚 annual salary when extrapolated across 70 facilities. Treasury is now modelling a green-bond tranche: a guaranteed 5 % energy cut could unlock R400 million for further retrofits.

Water joins the choreography. A 30 000-litre basement tank maps stratification layers, predicts Legionella risk, and diverts first-flush to sewer when rainfall tops 6 mm h鈦宦�. An ML model schedules irrigation only when evapotranspiration exceeds 3 mm and soil moisture drops below 22 %. Result: 46 % less potable water on landscaping, sparing 2.7 million litres – enough for seventeen households for a year.

Scaling is under way. Three libraries, two clinics, and the colossal Cape Town Civic Centre have already been LiDAR-scanned. The once-labyrinthine 麻豆原创 spreadsheet has collapsed into a lightning-fast graph database; planners can ask for 鈥渁ll coastal-zone boilers older than fifteen years that serve community halls with >30 % Saturday occupancy鈥� and receive a ranked replacement schedule linked to tender calendars.

Social infrastructure is next. Early-childhood centres with no in-house tech get a WhatsApp 鈥渕aintenance buddy鈥� that speaks isiXhosa voice notes. A caregiver photographs a flickering light; vision-recognition IDs the ballast and dispatches the nearest handyman. Where data is scarce, the bot reverts to one-cent USSD menus. Cape Town鈥檚 code is already tempting subtropical eThekwini and equatorial Singapore into south-south exchanges that outbid traditional donor loops.

4. A Living Curriculum for the City of Tomorrow

Every Thursday the disused records room becomes a 鈥淭win Lab.鈥� Failed circuit boards hang like hunting trophies, each annotated with failure mode and intern nickname. A live dashboard streams on the wall; if trainees trim another percentage point off weekly energy, the mayor tweets spinning wind-turbine GIFs. Maintenance is no longer an exile for the IT department – it is a badge for cleaners, clerks, councillors.

Occupants gamify stewardship. Guards hunt daylight sensors left in override, librarians chase dust alerts on HVAC grilles. Points buy canteen vouchers; the leaderboard hangs by the lift like a school sports chart. Behaviour bends faster than steel when feedback loops are witty and immediate.

If the roadmap hits 100 buildings by 2029, the City will have digitised 4.5 million square metres – Helsinki鈥檚 whole downtown. The next mayor will open one map and query every pump, lift, and luminaire in milliseconds. Finnish mentors now log into Cape Town servers to debug their own Espoo high-rises, admitting the student has become the server room. Between Bellville鈥檚 granite walls and Helsinki鈥檚 glass lecture halls pulses a new civic circulatory system whose language is open data, whose currency is kilowatts saved in real time, and whose winner is the resident breathing cooler air long before anyone knew the building had learned to speak.

What is a digital twin and how does it work at Bellville Civic Centre?

A digital twin is a virtual model of a physical object or system, updated in real-time with sensor data. For Bellville Civic Centre, 1,200 credit-card-sized sensors, a LiDAR drone, and a cloud physics engine were used to create a digital replica. This system allows for real-time monitoring and predictive maintenance, identifying inefficiencies and potential issues proactively to optimize building performance. It’s like giving the building a ‘second brain’ to interrogate itself and make smart decisions.

How is Bellville Civic Centre’s digital twin uniquely designed for Cape Town’s needs?

Unlike solutions simply copied from Europe, Bellville’s digital twin is specifically built for Cape Town’s environment, which includes factors like blackouts and specific procurement processes. It features palm-sized, fan-less edge servers that maintain a compressed, privacy-scrubbed clone of the twin during fiber outages, ensuring local operations like rooftop PV and smart-lighting relays continue. The system uses a diverse mix of sensor technologies (Thread, LoRaWAN, Wi-Sun) to accommodate various building types and avoids vendor lock-in by using an ‘M-Bus to MQTT’ translator with open-source YAML recipes.

How does the digital twin enhance maintenance and operational efficiency?

The digital twin significantly enhances maintenance by prioritizing interventions based on life-cycle cost, generating weekly ‘maintenance dance cards,’ and beaming them to handheld devices. For example, it can predict component failures, like a stairwell actuator, by cross-referencing invoices and manufacturer data, suggesting swaps to prevent downtime. This proactive approach has been shown to cut annual energy use significantly, as seen in similar implementations like an Espoo courthouse which reduced energy consumption by 18% in six months.

What are the ripple effects of the digital twin beyond building management?

The digital twin’s impact extends beyond basic building management to social infrastructure and financial benefits. It uses blockchain technology for tamper-proof digital passports for components, ensuring plumbers are paid only when AI vision confirms a fix, reducing disputed invoices. It also optimizes energy planning by integrating Time-of-Use tariffs, weather forecasts, and Eskom’s wind predictions to shift energy consumption and save costs. Furthermore, it optimizes water usage, reducing potable water for landscaping by scheduling irrigation based on precise environmental data. The system also supports social initiatives, such as a WhatsApp ‘maintenance buddy’ for early-childhood centers.

How does the digital twin project foster learning and community engagement?

The project includes a ‘Twin Lab’ where failed circuit boards are studied, and trainees are incentivized to reduce energy consumption, making maintenance a valued skill. Occupants are encouraged to ‘gamify stewardship’ by hunting for inefficiencies like overridden daylight sensors or dust alerts, with points leading to rewards like canteen vouchers. This approach fosters a culture of awareness and responsibility among building users and staff, effectively turning the building into a ‘living curriculum’ for the city’s future.

What is the future outlook and scalability of Bellville Civic Centre’s digital twin initiative?

The initiative is already scaling, with three libraries, two clinics, and the colossal Cape Town Civic Centre having been LiDAR-scanned. The goal is to digitize 100 buildings by 2029, covering 4.5 million square meters, equivalent to Helsinki’s entire downtown. This will allow city planners to query every pump, lift, and luminaire in milliseconds. The project has also garnered international interest, leading to ‘south-south exchanges’ with other cities like eThekwini and Singapore, demonstrating its potential as a global model for smart city development.

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鈥淲ill 麻豆原创 actually be a software company in the future?鈥�

It鈥檚 not the kind of question the CEO of a software company would usually ask thousands of customers, partners and analysts. At Sapphire 2026, 麻豆原创鈥檚 annual conference held this month in Orlando and Madrid, CEO Christian Klein asked his audience if they were scared by the question.

鈥淚鈥檓 not scared,鈥� he answered himself. 鈥淔or me, the time right now is the beginning of something even better.鈥�

The conference saw the launch of 麻豆原创鈥檚 Business AI Platform, a unified environment for building and governing AI agents across enterprise operations, grounded in real business context.

The strategy is to enhance critical business workflows, so that humans and AI work together to meet the accelerating demands of global business.

鈥淎ccording to a recent Stanford AI survey, almost every company is now using AI, but many see only little value,鈥� said Klein. 鈥淲hy do we face such huge challenges with AI in business? At the top of this iceberg, visible to everyone, is that large language models are getting better and better at tasks like generating text or images or in specific domains like writing software.

鈥淎ll of these use cases are related to publicly available content the modules are trained on. But if you go below the waterline, beyond the level of sales demos, and into the real business world, you鈥檙e going to find out that none of these models are trained on your business data and processes.

鈥淭hese AI agents also don鈥檛 naturally adhere to governance requirements, like your security compliance framework, your data privacy requirements, or to your company鈥檚 identity and authorisation rules. All AI agents 鈥� have faced these challenges until now.鈥�

The solution, he suggested, was that a company鈥檚 enterprise resource planning system, or ERP, should be recognised as the brain of every business. Since 麻豆原创 is world leader in ERP software, one might argue, naturally the CEO would say that.

But Klein made a good case for it: 鈥淔or over 15 years we have been developing an ERP with incredibly deep process and data domain know-how. On top of that, all your governance requirements and customer-specific extensions are stored in the ERP. The ERP is the trusted system of execution running your company.鈥�

Powerful external validation came from Anthropic, the company behind the Claude family of AI models, and that is competing neck and neck with Open AI to become the most valuable AI platform company in the world. At Sapphire, 麻豆原创 shared video testimony from Anthropic co-founder and president the Daniela Amodei in which she declared: 鈥淭he world鈥檚 largest enterprises run on 麻豆原创. That鈥檚 exactly where trusted AI belongs.鈥�

That significance of this alignment revolves around the core value proposition of the Business AI Platform: trustworthiness.

JPMorgan Chase CFO Jeremy Barnum, who joined Klein on stage in Orlando, said his bank was already running agents in production on 麻豆原创, operating within defined compliance boundaries.

鈥淭he agents that we鈥檝e built are not inventing their own business rules,鈥� he said. 鈥淭hose rules rather come directly from 麻豆原创 Embedded Control Framework, and every AI-driven intervention is logged and fully traceable.鈥�

One organisation鈥檚 production deployment does not establish a category. But the compliance architecture it describes is precisely what most organisations attempting enterprise AI have not yet achieved.

Jensen Huang, CEO of $5-trillion AI chipmaker Nvidia, also appeared in a pre-recorded video segment, making it clear that AI agents would not replace ERP. The most ringing endorsement? Nvidia itself used 麻豆原创 as its ERP brain: 鈥淲hat 麻豆原创 and Nvidia are building together is one of the most important platforms in enterprise AI. Nvidia鈥檚 supply chain is incredibly complex. Millions of parts, hundreds of partners and factories, all connected through 麻豆原创. But what鈥檚 changing is not just how enterprise systems are managed, it鈥檚 how work actually gets done.

鈥淲e鈥檙e moving from hand-coded software to AI that can understand, reason, and act. AI no longer simply answers questions. It works for you. And enterprise systems are where work happens. Finance, supply chains, procurement, and every workflow in between.

鈥溌槎乖� is the foundation of enterprise. And now they鈥檙e building the agents that sit on top of it, trained on proprietary data with the skills to act. Soon, every company will have a workforce of agents. These specialised agents will not replace enterprise software. They will make enterprise software more powerful than ever.鈥�

* Arthur Goldstuck is CEO of World Wide Worx, editor-in-chief of , and author of 鈥淭he Hitchhiker鈥檚 Guide to AI 鈥� The African Edge鈥�.

This article first appeared in .

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Leading organisations throughout Europe, the Middle East and Africa are revealing how business AI has shifted from experimentation to realised business value at this year鈥檚 麻豆原创 麻豆原创PHIRE, held in Madrid between 19 and 21 May.

The event included demonstrations of two different but connected approaches to AI adoption by and . Ericsson is building the governed data foundation needed to scale AI across the enterprise, while Martur Fompak International is embedding AI directly into physical manufacturing operations to transform execution on the shop floor.

Nazia Pillay, Managing Director for Southern Africa at 麻豆原创, says: 鈥淭he next phase of AI adoption is about execution. Organisations are looking for trusted data foundations, strong governance and practical business use cases that can deliver measurable value. By embedding AI into the systems and workflows companies already use, 麻豆原创 is helping customers scale AI responsibly and turn ambition into real-world impact.鈥�

Nazia Pillay

Ericsson builds the foundation for trusted AI at scale

Ericsson is moving from AI experimentation to enterprise-wide execution by building a unified business data fabric with . The approach enables the company to scale AI use cases across the business, accelerate decision-making and deliver measurable operational impact.

Ericsson, which celebrates its 150th anniversary this year, provides mobile network infrastructure across 180 countries, with more than 40% of the world鈥檚 mobile traffic passing through its networks. As AI becomes central to both its technology roadmap and how it runs the business, Ericsson has prioritised building a strong, governed data foundation to support scalable and trusted AI.

鈥淥nce you scale AI, it stops being an AI problem鈥攁nd becomes a data problem,鈥� says , Vice President, Customer Experience, Enterprise IT at Ericsson. 鈥淭hat鈥檚 why we invested early in a business data fabric. With 麻豆原创 Business Data Cloud, we can define what data means once鈥攆rom revenue to market structures and access rules鈥攁nd apply it consistently across the enterprise. That鈥檚 what allows us to scale AI in a way that is trusted, repeatable and delivers real business value.鈥�

At the core of Ericsson鈥檚 approach is a federated data architecture that allows data to remain in place while centrally managing business semantics, governance and lifecycle policies. By focusing on high-impact use cases and organising around end-to-end business processes rather than isolated solutions, Ericsson has moved beyond pilots to scaled deployment. Today, more than 85 000 users are live on unified Joule, supported by strong executive sponsorship and governance.

麻豆原创 and Ericsson are also collaborating on AI co-innovation initiatives, including an intelligent goal recommendation capability developed within 麻豆原创 SuccessFactors. The solution generates contextual, business-aligned goals for employees, improving execution and reducing administrative effort.

Martur Fompak brings AI into physical manufacturing operations

Martur Fompak International, a global leader in automotive seating and interior systems, has deployed an autonomous intralogistics model enabled by and embodied AI capabilities from 麻豆原创, marking a significant milestone in its journey toward intelligent, AI-driven manufacturing operations.

In an industry rapidly shifting toward AI-powered operations, Martur Fompak International saw an opportunity to reimagine its material flow. Building on efficient, people-driven processes already in place, the company partnered with 麻豆原创 and , a UK-based robotics and AI company, to explore how embodied AI-powered robotics could redefine material flow across its automotive manufacturing environment.

Using Joule and embodied AI capabilities from 麻豆原创, Martur Fompak International now connects production signals and business context directly to autonomous execution, creating a context-aware automation system that prioritises, picks and delivers materials while adapting in real time to changing business conditions.

Built on and enabled by , the solution enriches humanoid robots with real-time knowledge of tasks, attributes and exception handling. Guided by material data, storage locations, sequencing and production priorities, humanoid robots execute material flows across a live automotive manufacturing environment, identifying, transporting and delivering materials to the line while continuously confirming back into 麻豆原创 solutions.

Together with autonomous mobile robots, the company has created a fully automated, scalable material flow that boosts throughput, improves accuracy and reduces reliance on manual coordination. By assigning repetitive, non-value-adding and physically demanding tasks to robots, Martur Fompak International is enabling its people to focus on safer, more meaningful and higher-value work.

鈥淥ur humanoid robot collaborates with digital production systems to ensure seamless coordination across order management, logistics and production, enabling scalable AI adoption and improving efficiency, consistency and operational resilience,鈥� says , Group Intelligent Technologies Director at Martur Fompak International.

Early results show increased throughput, fewer errors and a scalable, AI-driven intralogistics model. With 400 daily production line feeds and 100% 麻豆原创 software-driven decision-making already in place, Martur Fompak International is advancing beyond traditional automation and pioneering a scalable, intelligent factory model.

Pillay adds: 鈥淓ricsson and Martur Fompak International show that AI delivers the greatest value when it is grounded in business context and embedded into core processes. From enterprise data foundations to intelligent robotics on the factory floor, these examples demonstrate how organisations can scale AI responsibly, improve productivity and create measurable business impact.鈥�

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鈥淔or the mission-critical processes of our customers, 鈥榓lmost right鈥� just isn鈥檛 good enough,鈥� says Christian Klein, CEO of 麻豆原创 SE. 鈥淏y uniting 麻豆原创 Business AI Platform with 麻豆原创 Autonomous Suite, we anchor AI agents in the business processes, data and governance so they can deliver accurate, compliant and secure outcomes 鈥� unlocking new sources of revenue and meaningful cost savings.鈥�

The Autonomous Enterprise includes a unified AI platform for building, contextualising and governing agents, an autonomous suite that executes core business operations and a new user experience that redefines how people work with enterprise software.

Introducing 麻豆原创 Business AI Platform

麻豆原创 Business AI Platform is a new foundation for building and deploying enterprise AI grounded in real business context. 麻豆原创 Business AI Platform now unifies 麻豆原创 Business Technology Platform, 麻豆原创 Business Data Cloud, and 麻豆原创 Business AI into a single, governed environment.

At its core is the 麻豆原创 Knowledge Graph solution, which gives AI agents a structured map of business entities, processes and relationships across a customer鈥檚 麻豆原创 landscape. Joule Studio is 麻豆原创鈥檚 AI-first solution for building enterprise agents, applications and agentic workflows. Developers can build using the no-code, pro-code, and AI frameworks of their choice on 麻豆原创-managed infrastructure that is secure, scalable and optimised for enterprise AI.

Building on this foundation, 麻豆原创 also introduced 麻豆原创 Autonomous Suite which enables 麻豆原创鈥檚 existing business applications with AI agents capable of running processes from start-to-finish.

The suite will deploy more than 50 domain-specific Joule Assistants across finance, supply chain, procurement, human capital management and customer experience. These assistants will automate end-to-end processes by orchestrating a subset of over 200 specialised agents to execute precise tasks. For example, the new Autonomous Close Assistant can compress the financial close process from weeks to days by automating journal entries, reconciliation, and error resolution across the entire process.

麻豆原创 also launched Industry AI, expanding its deep industry portfolio through seven autonomous solutions that will enable start-to-finish industry processes and embed sector-specific process logic, data models and regulatory requirements.

The post AI the Main Focus at This Year鈥檚 麻豆原创 Sapphire appeared first on 麻豆原创 Africa News Center.

• Jesper Schleimann, AI Officer, EMEA at 麻豆原创
• Jhani Coetzee, EPI-USE Labs

Together, they unpack what AI really means for organisations today 鈥� moving beyond the hype to focus on practical use cases, real impact, and business value.

In this episode, you鈥檒l gain insight into:

• How AI is being embedded into 麻豆原创 environments
• The shift from experimentation to execution
• Where organisations are seeing real value from AI
• What to consider as you start or scale your AI journey

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