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.听It’s built for Cape Town’s exact needs, not just copied from Europe,听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.

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• 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

Click below to watch!

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For much of the past decade, AI has lived in labs, pilots and PowerPoint decks. The next phase is different. AI is moving into the operational core of organisations, reshaping how decisions are made, work is executed and value is created.

AI is becoming the most significant technology shift enterprise leaders will face in this generation. Not because the algorithms are new, but because the operating model required to make AI work at scale is fundamentally different.

One of the clearest changes heading into 2026 is the move from AI that assists humans, to AI that acts on their behalf.

Early enterprise AI tools functioned as copilots: surfacing information, generating insights or suggesting next steps. Increasingly, organisations are deploying autonomous AI agents that recommend actions 鈥� and take them 鈥� executing multi-step business processes within defined roles and controls.

This transition matters because it forces leaders to confront new questions of trust, accountability and governance. Autonomous AI can deliver significant productivity gains, but only if organisations are prepared to define where machines can act independently, where human approval is required, and how exceptions are handled.

In practice, this means treating AI agents less like software features and more like a digital workforce: assigned roles, clear permissions, monitored performance and escalation paths when things go wrong. Without this discipline, autonomy becomes risk rather than advantage.

Intelligence must be built in, not bolted on

Another defining trend is the move toward AI-native systems. Many organisations still treat AI as an add-on: a layer of intelligence bolted onto processes designed decades ago. That approach is reaching its limits.

AI-native architecture embeds intelligence directly into core workflows, allowing systems to understand intent rather than simply execute transactions. Instead of navigating complex menus and dashboards, users express what they want to achieve, and systems orchestrate the necessary steps across functions.

For leadership teams, this is not a user-interface upgrade, but a shift in how work gets done. Ideally, decision-making accelerates, organisational friction reduces, and the boundary between analysis and execution begins to disappear.

However, this only works when underlying systems are clean, standardised and integrated. Which leads to a harder truth many organisations are discovering.

Data quality is the real AI constraint

The biggest barrier to AI success is not model sophistication, but data reality. AI systems amplify whatever foundations they are given. Clean, consistent data produces reliable outcomes, while fragmented, poorly governed data produces confident nonsense.

This is why data has become the strategic nervous system of the modern enterprise. AI depends on shared definitions of customers, products, suppliers and processes. It requires transactional integrity, accessible historical context and the ability to combine internal and external information in real time.

Organisations that have postponed data discipline are finding that AI exposes weaknesses instantly, often in ways that affect customers, regulators or financial performance. In the year ahead, leaders will increasingly be judged on whether they treated data as a strategic asset early enough, rather than as an IT hygiene issue.

Closely linked to data readiness is a simple but central principle: keeping core enterprise systems clean.

Years of excessive customisation have left many organisations with fragile ERP environments that are difficult to upgrade and harder to integrate with modern AI capabilities.

The shift toward standardised cores with extensions built outside the core system creates an environment where innovation doesn鈥檛 break operations.

For boards and executive teams, this requires a mindset shift. Standardisation is not a loss of competitive differentiation, but the price of adaptability. The differentiation moves to how organisations use data, design experiences and make decisions, not how many lines of custom code they maintain.

Technology alone will not deliver results

Perhaps the most underestimated factor in AI success is change management, which consistently accounts for a larger share of AI outcomes than technology itself.

AI changes roles, not just tools. Finance teams move from processing transactions to managing exceptions. HR shifts from administrative workflows to skills intelligence.

Operations leaders rely more on forecasts and simulations than static reports. These changes demand new skills, new incentives, and new ways of measuring performance.

This year, leaders must invest in adoption with the same commitment and focus as they invest in new capabilities. AI literacy should be a core leadership competency not just a specialist function.

As AI initiatives multiply, so does the risk of fragmentation. Different business units experimenting independently can create inconsistent standards, duplicated effort and unmanaged risk.

This is why many organisations are establishing AI centres of excellence that coordinate AI innovation. Effective governance frameworks address five questions: how AI systems are approved and retired, how decisions are logged and audited, how policies are enforced, where human oversight is required, and how performance is measured.

In 2026, AI governance will be viewed much like financial governance: a prerequisite for trust, not a brake on progress.

From pilots to production or paralysis

A final challenge looms large: scaling. Many organisations are stuck in what has become known as 鈥減ilot purgatory鈥�, where successful experiments never reach enterprise impact.

The reasons are consistent: poor integration with core systems, unclear ownership, lack of user trust, weak data foundations and vague ROI metrics. Moving from pilot to production requires deliberate planning, phased rollout and visible executive sponsorship.

Leaders who expect AI to scale itself will be disappointed, while those who design for scale from day one will pull ahead quickly.

As we accelerate into 2026, AI is an operational reality. The real strategic question for leaders is whether their organisations are structurally ready for AI, with clean systems, trusted data, skilled people and disciplined governance. With these foundations, AI becomes a durable source of advantage.

In a volatile global environment, leadership is increasingly defined by the ability to move forward without perfect certainty. Business AI, deployed responsibly and at scale, is becoming one of the most powerful tools leaders have to do precisely that.

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From managing customer support workflows to orchestrating complex supply chains, agentic AI is redefining how businesses operate, respond, and grow. These intelligent digital co-workers act with autonomy, context, and speed, with growing capabilities for reasoning, making decisions, and working alongside humans to execute multi-step processes across departments.

听, agent-driven applications are rapidly becoming the standard for enterprise management. Global estimates suggest AI agents could contribute trillions to the world economy by 2030 through productivity gains, faster decisions, and cost reductions.

Transformative impact

Despite pervasive AI skills shortages, South African companies are moving quickly from experimentation to execution. Financial institutions are embedding AI agents into ERP systems to reroute inventory and manage disputes. Healthcare providers use AI meeting agents to generate follow-ups and automate patient admin. Legal firms use AI to prepare case files and speed up settlements.

This shift is being driven by a combination of pressure and potential. Faced with economic headwinds, skills shortages, and rising customer expectations, South African companies are looking to AI agents to unlock productivity, streamline operations, and free up human talent for higher-value work.

But deploying AI agents effectively requires more than buying the latest tool. The success of AI agents depends on deep integration of data, processes, and applications through a suite-first approach.

Leading with a suite

According to an IDC Spotlight Report, companies that adopt AI-powered suites like 麻豆原创鈥檚 see measurable gains:

• 37%听report improved process productivity
• 39%听achieve greater cost efficiency
• 36%听boost workforce productivity
• 35%听accelerate speed to market

By leveraging an AI-powered suite integrated to a core business technology platform, companies can empower their AI agents to act with full business context. Unlike siloed tools, a suite-first approach supports real-time collaboration between agents, humans, and systems, making AI agents not just smarter, but more impactful on the overall performance of the business.

麻豆原创鈥檚 Joule, an AI agent framework embedded into the 麻豆原创 Business Suite, offers companies a system of intelligent agents that collaborate across business functions, from finance and procurement to HR and supply chain, to execute complex workflows and drive better decisions at scale.

These agents leverage knowledge centres and data cloud to ground actions in real-time, contextual business data. Working alongside teams, the agents augment human decision-making, accelerate task completion and minimise manual errors. In finance functions, agents can optimise working capital by accelerating accounts receivable matching, while in procurement they can surface the most relevant suppliers based on business rules and past performance.

AI agent readiness check

Before companies deploy AI agents like Joule, they need the right digital foundation. 麻豆原创 recommends a four-part readiness framework:

1 Data quality and accessibility 鈥�听Agents are only as good as the data they use. Clean, structured, and real-time data from across the enterprise is critical for effective agent decision-making. Silos, outdated data, or missing context will slow adoption and risk poor outcomes.

2 Process maturity 鈥�听AI agents thrive on well-defined workflows. Before automation, companies must ensure their business processes are standardised, documented, and ready for orchestration. Automating chaos just creates faster chaos.

3 Organisational clarity 鈥撎�Who will use these agents? For what tasks? How will they hand off to human employees? Clear role definitions and communication are essential for adoption and trust.

4 Governance and guardrails 鈥�听Just like human employees, AI agents need rules. Define permissions, escalation paths, ethical boundaries, and auditing practices. Agents should act autonomously but within the boundaries of the businesses in which they operate.

AI agents are more than just another layer of automation. They represent a new model of work, one that is collaborative, contextual, and continuous. The true value of AI agents is unlocked only when companies are ready. And the companies that unlock the greatest value the quickest are those deploying their AI agents through an AI-powered suite integrated to a core business technology platform.

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The comments were made by Sunil Geness, director of global government affairs and CSR at 麻豆原创 Africa, and Sergio Maccotta, senior VP and GM for 麻豆原创 Middle East and Africa 鈥� South.

Geness and Maccotta said businesses that can sense change, make decisions and act with minimal human intervention are reshaping how companies operate and compete. According to 麻豆原创, autonomous operations 鈥� where nearly half of all business processes run independently and most operational work is听automated听or AI-augmented 鈥� are being deployed across finance, supply chain and human resources in industries including energy, retail and manufacturing.

鈥淭his is made possible through AI embedded within enterprise resource planning processes, from finance and procurement to supply chain and HR systems, that understand context and act autonomously,鈥� said Maccotta. 鈥淪elf-optimising systems that learn, improve and adapt in real-time are matched to clean-core ERP architecture to give organisations simplified, cloud-based systems that drive profitability, reduce risk and enhance decision-making.鈥�

麻豆原创 cited data from听听indicating that the global autonomous enterprise market is projected to grow from about $49 billion in 2024 to more than $118 billion by 2030, with adoption accelerating across Europe, the Middle East and Africa.

The company said while few surveys use 鈥渁utonomous enterprise鈥� as a formal category, data on AI, ERP and AI agents reflects technologies underpinning autonomous models.

Recent data for the Middle East and Africa region indicates a market size set to grow at a compound annual growth rate of 8.7%, reaching $10.2 billion by 2032.

鈥淎I is enhancing efficiency and fostering innovation across industries, from automating routine tasks to enabling complex data analysis and providing predictive insights, while also improving decision-making and optimising business operations,鈥� Geness said.

麻豆原创 added that some analysts estimate AI could contribute $1.5 trillion to Africa鈥檚 economy if the continent captures 10% of the global AI market by 2030.

Data sovereignty

麻豆原创 executives said Africa鈥檚 digital transformation continues, with data sovereignty emerging as a key factor.

鈥淔or multinational organisations and technology companies, success depends on the ability to localise without losing global efficiency, to build trust while maintaining innovation, and to invest strategically in infrastructure and partnerships that align with both regulatory demands and customer expectations,鈥� Maccotta added.

The company said Africa鈥檚 data centre market is projected to exceed $9 billion by 2029, although it remains a small share of the global total.

Executives said autonomy does not eliminate human roles but changes them, shifting employees from managing repetitive processes to supervising systems and making strategic decisions.

Maccotta advises business leaders seeking to build autonomous enterprises to start by modernising the core and fixing the data foundation for AI-driven innovation.

鈥淩emember that technology is only part of the story, and that the strategy, people and partners that businesses choose are as important to building a truly connected autonomous enterprise. Focus then on automating processes that protect revenue, improve cashflow or unlock capacity first, and take care to prepare people by prioritising reskilling and upskilling.

“Finally, collaborate with technology partners that act more as advisors than vendors, and can guide the process of redesigning core business processes for the AI era. True transformation happens when businesses combine intelligent systems with visionary leadership, skilled people and trusted partnerships.鈥�

This article first appeared in .

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The good news, says , Managing Director: Southern Africa at 麻豆原创, is that real-world AI value is already becoming clear. 鈥淎cross critical business functions like finance, HR, procurement, sales and service, and marketing, embedded AI is driving efficiency, accuracy, and smarter decisions at scale. From automating repetitive processes to delivering real-time insights and predictive recommendations, South African companies are unlocking unprecedented gains in productivity and efficiency.鈥�

Analysts suggest African countries could unlock as much as $100-billion in economic value per year听, while a PwC study estimates听听if Africa can claim a 10% share of the global AI market.

鈥淭o unlock this vast economic potential, organisations must integrate AI into core business processes and achieve measurable outcomes for clear use cases. A clean core strategy further allows businesses to respond faster to market changes and adopt new technologies like AI and advanced analytics more easily, creating clear pathways to significant ROI and business impact.鈥�

Of critical importance too is defining and implementing a comprehensive AI framework that prioritises governance at the outset. 鈥淎n effective framework should include a clear AI vision, business-aligned goals, an operating model, and, critically, establishment of AI governance to ensure ethical, secure, and compliant scaling of AI across the organisation.鈥�

Pillay highlights high-impact use cases across five key business functions that South African organisations can implement to boost productivity and drive growth.

1 Smarter financial insights & automation听

Finance departments are under increasing pressure to speed up reporting cycles, reduce operational costs, and strengthen cash flow visibility. 鈥淚n South Africa, financial institutions are leveraging AI for everything from fraud detection to multilingual support, but practical automation is where the biggest gains are currently being made,鈥� says Pillay.

Automated receivables matching is one such win. By applying machine learning to past payment behaviours, companies can automatically match and clear bank statement items,听听and accelerating payment cycles.

Another high-impact tool is AI-assisted cost centre analysis. 鈥淚nstead of manually wading through complex reports, finance teams can use generative AI to instantly summarise data, highlight KPIs, and recommend next steps,听听and freeing up analysts to focus on strategic insights,鈥� says Pillay.

2 Smarter hiring and better talent fit

AI is transforming HR from a reactive cost centre into a strategic function. Nearly 70% of South African HR teams听听to streamline recruitment, performance reviews, and workforce planning, delivering up to 35% process efficiency gains.

鈥淥ne practical use case gaining widespread adoption is AI-powered applicant screening, where machine learning scans CVs to match applicant skills with job requirements. This听听and speeds up hiring for hard-to-fill roles.鈥�

Pillay points to AI-generated job descriptions as another time-saving tool. 鈥淗R professionals input a few keywords and receive polished, bias-reducing job listings in seconds, helping improve candidate fit and听.鈥�

3 Scaling service and sales excellence

South African telecoms, ecommerce platforms and financial institutions are seeing measurable gains from AI-powered customer service.

鈥淭ools like chatbots and virtual assistants now handle routine queries with 24/7 availability, cutting service costs and improving customer satisfaction,鈥� explains Pillay. 鈥淲here a human touch is still needed, AI is helping agents generate case summaries, allowing them to respond faster by compiling email threads and communications into a single, easy-to-read brief. This can听听and improve first-contact resolution by 10%.鈥�

In field service, AI-driven equipment insights give technicians a clear picture of past service activity, parts used, and common failure patterns, resulting in 65% higher productivity and a 5% bump in first-time fix rates,听.

4 Streamlined sourcing and planning

Procurement teams face mounting complexity as they juggle supply risk, regulatory compliance, and shifting market dynamics. According to Pillay, AI is easing this burden by automating strategic sourcing tasks and offering real-time decision support.

鈥淎I tools have become indispensable to category planning efforts,听. This is helping managers move 90% faster and make more informed and proactive decisions.鈥�

Another valuable application is automated statement-of-work (SOW) generation. With minimal inputs,听, slashing processing time by 71% and improving project alignment by providing clearer briefs to suppliers.

5 Improved customer engagement and inventory management

AI鈥檚 value in marketing is growing fast, especially in segmenting audiences and triggering personalised campaigns. South African firms are tapping into predictive tools to boost engagement and pre-empt churn.

鈥淲ith AI-powered audience segmentation, marketers can automatically group customers based on predicted behaviours such as likelihood to convert or churn, driving higher rates of engagement and more relevant, timely outreach,鈥� says Pillay.

In back-office support, AI-generated sales orders now听, cutting manual effort, speeding up processing, and reducing errors by 25%.

鈥淎s embedded AI matures and business systems become more intelligent, these use cases will continue to expand, driving a new era of productivity, insight, and competitive advantage across every function,鈥� says Pillay.

This article first appeared here:

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The era of digitisation is giving way to something more profound: the Autonomous Enterprise听鈥撎�a business that can sense change, make decisions and act with minimal human intervention, all while empowering people to pursue revenue-driving strategic activities.

Autonomous operations are already being deployed across finance, supply chain, human resources, an in Industries like Energy, Retail and Manufacturing. In 2026, they will separate the most resilient and profitable businesses from the rest.

An Autonomous Enterprise goes beyond automating individual tasks by integrating autonomous ERP, business AI and clean data into the core of business operations. More than 50% of business processes run independently, and up to 80% of operational work is automated or AI-augmented.

This is made possible through AI embedded within enterprise resource planning processes, from finance and procurement, to supply chain and HR systems, that understand context and act autonomously. Self-optimising systems that learn, improve and adapt in real time, are matched to clean-core ERP architecture to give organisations simplified, cloud-based systems that drive profitability, reduce risk and enhance decision-making.

Crucially, autonomy does not remove humans: it redefines and empower their work. Employees move from managing repetitive processes to supervising intelligent systems, making strategic decisions and creating new value throughout the business.

2026 鈥榓 tipping point鈥�

The year ahead will be critical for organisations across Europe, the Middle East and Africa. In Europe, business leaders face an ageing workforce, complex supply chains, regulatory pressure, and the push for sustainability and data sovereignty. Middle Eastern nations are already deploying national AI strategies and sovereign cloud infrastructure as part of a rapid diversification from the energy sector. And despite inflation and debt pressures, a world鈥檚 fastest-growing digital economy and most youthful workforce are emerging in Africa.

While the challenges in each region are unique, at their core every organisation is seeking the same capabilities: faster decision-making, greater profitability, improved resilience, and sustainability at scale.

Businesses that fail to build these capabilities, and instead rely on manual processes, disconnected systems and spreadsheets, face growing risks: slower reaction time, shrinking margins, and higher operational costs.

This is why the business opportunity for Autonomous Enterprises is significant. The global Autonomous Enterprise market is expected to grow from听, and adoption is accelerating across EMEA.

听Global partner for business transformation

While I can see the scale of the challenge businesses face to transform into autonomous enterprises, I am equally excited at bringing 麻豆原创鈥檚 strengths in this arena to bear. Through our flagship 鈥� now more accessible than ever thanks to the RISE with 麻豆原创 initiative 鈥� we equip businesses with modern, clean core ERP systems that are upgrade-safe and AI ready.

, 麻豆原创鈥檚 generative AI copilot now supports 11 languages and features more than 400 embedded AI use cases across 26 industries, while the enables extensibility, data orchestration and clean core innovation for the world鈥檚 most critical industries.

My advice, to leaders seeking throughout the region to build Autonomous Enterprises, is to start modernising the core and fix the data foundation for AI-driven innovation. Remember that technology is only part of the story, and that the strategy, people and partners that businesses choose are as important to building a truly connected Autonomous Enterprise.

Focus then on automating processes that protect revenue, improve cashflow or unlock capacity first, and take care to prepare people by prioritising reskilling and upskilling. Finally, collaborate with technology partners that act more as advisors than vendors, and who can guide the process of redesigning core business processes for the AI era. True transformation happens when businesses combine intelligent systems with visionary leadership, skilled people and trusted partnerships.

In 2026, the most successful companies will go beyond digital transformation to achieve faster decision-making, greater operational certainty, and the ability to unlock new forms of growth and innovation.

This article first appeared in .

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