A tech confession and a debate with an AI robot was the recipe for the future of work at 麻豆原创 Connect Southern Africa in Kyalami near Johannesburg last week.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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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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Organisations across industries are using cloud modernisation to build stronger digital foundations, unlock greater agility and embed artificial intelligence directly into business processes.

Companies across Europe are demonstrating how cloud transformation is creating the foundation for more intelligent, resilient and efficient business operations, with several organisations showcasing recent implementation progress at 麻豆原创 麻豆原创PHIRE, held in Madrid between 19 and 21 May.

, Managing Director for Southern Africa at 麻豆原创, says: 鈥淐loud transformation has become the foundation for the next phase of business innovation. By modernising core systems and moving to more scalable, secure and intelligent platforms, organisations can simplify complexity, strengthen resilience and create the conditions for AI to deliver measurable business value.鈥�

Companies RISE to the cloud

is emerging as a leading force in AI-driven retail transformation, combining large-scale cloud adoption with hands-on co-innovation to shape the future of the industry together with 麻豆原创. Following its successful go-live in 2025, Salling Group now operates what is widely regarded as the largest private cloud deployment of globally.

This transformation has laid the digital foundation for embedding AI directly into core retail processes鈥攖urning ERP from a transactional backbone into an intelligent, real-time decision engine.

For Salling Group, technologies such as and 麻豆原创 Business AI are not incremental improvements, but a fundamental change in how the business operates. From forecasting and replenishment to store operations and reporting, AI is enabling faster decisions, reducing manual workloads, and helping employees focus on higher-value tasks.

Agents automate time-consuming procurement task

The Danish wholesaler has deployed artificial intelligence to automate one of the most time-consuming tasks in procurement: processing supplier order confirmations. The solution consists of multiple AI agents, each responsible for a clearly defined task, orchestrated into a single automated workflow built on 麻豆原创鈥檚 Business AI framework. The outcome is faster processing, improved data quality, and more accurate delivery information for customers.

With the coordinated AI agents in place, the company can now automatically identify delays, quantity changes, and price discrepancies 鈥� and respond significantly faster.

鈥淲e have previously tried both robotic process automation (RPA) and traditional automation approaches without really achieving the desired effect. The key difference this time is that we broke the task down into multiple independent AI agents, each responsible for a specific part of the process. Together, they now handle what previously required manual review,鈥� says , IT Director at 尝别尘惫颈驳丑-惭眉濒濒别谤.

The solution was implemented in close collaboration with , which was responsible for making the solution production-ready and fully integrated into 尝别尘惫颈驳丑-惭眉濒濒别谤鈥檚 麻豆原创 landscape.

鈥淏y distributing responsibilities across multiple AI agents, 尝别尘惫颈驳丑-惭眉濒濒别谤 has been able to automate a complex process without losing transparency or control. This has enabled a fast and secure transition from pilot to production and ensures a more robust solution that can easily be expanded as new requirements emerge,鈥� says , 麻豆原创 UX Manager at NTT DATA Business Solutions.

鈥淭his is the first AI agent solution we have put into production. The experience has given us the confidence to consider similar approaches across other areas, including invoice processing and order management,鈥� Frederik Aakerlund concludes.

Pillay adds: 鈥淭hese examples show how cloud modernisation and AI adoption are becoming inseparable. When organisations have a strong digital core, trusted data and scalable cloud infrastructure, they can move beyond experimentation and start embedding intelligence into the processes that matter most. That is where transformation begins to translate into real business impact.鈥�

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For ,听a leading pan-African financial services group focused on emerging markets headquartered in South Africa, with a strong presence in 31听countries,听this transformation journey has accelerated over the past 18 months as the organisation expanded its focus beyond HR modernisation toward a broader cloud, data and AI-enabled human capital strategy.

, Group Human Capital: Chief Operating Officer at Sanlam, says the organisation鈥檚 approach balances immediate operational requirements with longer-term transformation objectives. 鈥淲e have adopted an ambidextrous strategy for our digital and data transformation journey, simultaneously exploiting operational excellence, proficiency and efficiency in our current landscape while exploring incremental innovation that enhances and elevates the user experience and driving our longer-term transformation journey 鈥� focused on leveraging intelligent, transformative technology to unlock business value.鈥�

A central focus throughout the implementation has been ensuring that transformation delivers tangible business value while also enabling the teams responsible for sustaining change. Sanlam is supported by a dedicated support structure for the programme that includes a technical centre of excellence team, programme management, change management, and business analysis capabilities.

鈥淭ransformation required us to approach change from multiple perspectives simultaneously. We always look at transformation through the lens of people, process and technology. One of the realities organisations face is that many people are not ready to embrace the pace of technological change, which makes change adoption and leadership sponsorship critically important.鈥� 

According to Bandyopadhyay, Sanlam鈥檚 broader transformation strategy spans several interconnected focus areas. 鈥淎 key priority being rebuilding our job architecture for relevance to the current and future workforce and more importantly to transition to a skills-based organisation. This will then unlock the opportunity for us to leverage 麻豆原创鈥檚 AI-enabled Talent Intelligence Hub which is the golden thread that links all talent practices to a skills currency.鈥�

The organisation has also prioritised the implementation of a cloud data solution, master data management and platform scalability as part of its effort to establish a more unified data core across the business. With multiple business entities operating across the group, standardising and scaling trusted workforce and operational data remains a significant strategic focus area.

, Group: Head of HC Tech and Talent Intelligence at Sanlam, says cloud adoption will continue to accelerate over the next two years. 鈥淲e are moving aggressively towards a cloud-first strategy, including payroll migration, data platform evolution, and broader business integration initiatives. Our focus is on how we build scalable platforms and data capabilities that position us for the future.鈥�

As part of this strategy, Sanlam is piloting -related capabilities and is progressing towards broader business data cloud initiatives planned over the coming years. The organisation is also exploring how 麻豆原创 Business Data Cloud, 麻豆原创 agents, and Joule capabilities can support orchestration, analytics, and employee-related workflows across the employee life cycle.

Alongside its platform modernisation efforts, Sanlam is re-evaluating core HR services and processes, including employee experience, employee relations, and document management, while strengthening its talent intelligence and people analytics capabilities to support better workforce planning and predictive decision-making. The Talent Intelligence function has matured significantly from reports and dashboards to trends, insights and predictive analytics. A key achievement has been the development of a solution to measure productivity across the various Sanlam business entities.

Intelligent automation is also playing an increasing role across areas such as service management, payments, compliance, and administrative HR functions, alongside the digitisation of compliance related scorecards like skills development to improve planning, monitoring and achievement of compliance targets. This has allowed Sanlam to service an increased customer base with a greater service offering at reduced costs.

Govindsamy says the transformation journey remains ongoing rather than a once-off implementation project. 鈥淭he next phase of our journey is focused on accelerating cloud propagation, strengthening analytics and data capabilities, and embedding intelligent automation more deeply into the business. Success depends not only on technology, but on sustained leadership sponsorship, adoption, and preparing the workforce for the future.鈥� 

, Managing Director for Southern Africa at 麻豆原创, says: 鈥淎s organisations modernise their workforce strategies, there is growing recognition that human capital transformation depends on more than digitising HR processes. It requires an integrated foundation that combines cloud, data, analytics and intelligent technologies to support better decision-making, stronger employee experiences, and greater organisational agility. Sanlam鈥檚 ongoing transformation journey reflects how forward-looking organisations are building scalable, future-ready HR environments that can evolve alongside changing business and workforce requirements.鈥�

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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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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 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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The timing could hardly have been more pointed. Across the world, executives are losing patience with AI experiments that overpromise and underdeliver. Boardrooms have heard enough about pilots and proofs of concept, and now want systems that improve margins and forecast outcomes.

TechEd took place in that atmosphere, amid assurances that performance could replace promise. 麻豆原创 used the event to demonstrate how deeply AI now runs through its own operations before unveiling its next leap forward.

Rather than another round of hype about possibilities, 麻豆原创 aimed to show a working example of AI at scale, handling everyday complexity inside one of the world鈥檚 largest software organisations.

麻豆原创 chief technology officer and chief AI officer, Philipp Herzig, told Business Times at TechEd that the clearest evidence of maturity came from within 麻豆原创 itself. The company鈥檚 AI assistant, Joule, acts as a conversational layer across its software stack, connecting data, applications, and agents to automate tasks and surface insights on demand.

鈥淚f you look at AI at scale, what is really real and what is working very well, just look at Joule,鈥� he said. 鈥淚t鈥檚 used by more than 30,000 employees every month, about a third of the 麻豆原创 workforce. We have more than 100,000 policies and documents in different languages, and depending on where you work, in Brazil or South Africa, you get the correct HR or travel policy surfaced to you.鈥�

Herzig said Joule had become the company鈥檚 single interface for daily tasks. 鈥淵ou can do your expense reports, indirect procurement, and financial tasks all in one place. It works, and it works at scale. We have a thumbs-down rate of only 1%, which is phenomenal when you think about the size of the company.鈥�

Innovations across 麻豆原创鈥檚 unique flywheel of applications, data and AI put developers in the
driver鈥檚 seat 鈥� Muhammad Alam, 麻豆原创 executive board member

That success set the stage for TechEd鈥檚 central announcement: an AI model called 麻豆原创-RPT-1, short for relational pre-trained transformer. The model introduces a new class of AI: the enterprise relational foundation model. It interprets structured business data and the relationships within it, mapping how orders, invoices, logistics, and payments interact to forecast what comes next.

麻豆原创 described it as a model that 鈥渃an make fast and accurate predictions for common business scenarios like delivery delays, payment risk or sales order completion鈥�. Instead of producing text, it reads how data behaves across systems, turning business logic into predictive insight.

Herzig said 麻豆原创-RPT-1 marked the transition from incremental automation to full predictive architecture.

鈥淲e see a shift from what I call a cloud-native architecture to an AI-native architecture, as AI becomes an ever-increasing part of the software stack. 鈥淲hat we wanted to solve are the problems where we have a reason to solve them: because we have the data, the relational data, the structured business data and so on. We set out this research project two years ago, talked a little about it, but now it actually becomes a reality.

鈥淚t鈥檚 a shift that needs several things to come together: the knowledge graph, this predictive model now with RPT-1, and of course the large language models. So there are many elements in the software stack that change. Every day, each little piece adds to this picture and solves a particular challenge in the stack.鈥�

Herzig said the greatest challenge lay in making this intelligence work at enterprise scale.

鈥淎nyone can do a demo. Getting it enterprise-ready at scale 鈥� that鈥檚 the tough challenge. That鈥檚 why we鈥檙e solving one problem after another, each for a specific outcome in the overall stack.鈥�

麻豆原创 executive board member Muhammad Alam tied this philosophy to the developer community.

鈥溌槎乖粹�檚 announcements give developers the tools they need to deliver at the speed of AI,鈥� he said.

鈥淚nnovations across 麻豆原创鈥檚 unique flywheel of applications, data and AI put developers in the driver鈥檚 seat.鈥�

That 鈥渇lywheel鈥� anchored the narrative of TechEd:
鈥� applications generate data;
鈥� data trains predictive models;
鈥� models return intelligence to the applications.

Each loop strengthens the next, creating a flywheel effect of acceleration of innovation. 麻豆原创 announced that it would equip 12-million individuals worldwide with AI-ready skills by 2030 through a partnership with Coursera that provides hands-on certification in 麻豆原创鈥檚 ecosystem. The goal is to align those skills with the AI-native architecture now taking shape inside the company. It also sends the message that people remain at the heart of the AI journey.

This article first appeared in the

The post Making AI Real for Business Success appeared first on 麻豆原创 Africa News Center.

BERLIN听鈥� At 麻豆原创 TechEd in 2025,听 (NYSE: 麻豆原创) brings AI deep into the development process to level up how developers build.

New AI-driven capabilities in the 麻豆原创 Build solution, an expanding data ecosystem and powerful Joule Agents empower developers to move from idea to impact with unprecedented speed and confidence. As AI transforms the nature of professional work, 麻豆原创 also pledges to equip 12 million people worldwide with AI-ready skills by 2030.

鈥溌槎乖粹�檚 announcements today give developers the tools they need to deliver at the speed of AI,鈥� said Muhammad Alam, member of the Executive Board of 麻豆原创 SE. 鈥淚nnovations across 麻豆原创鈥檚 unique flywheel of applications, data and AI put developers in the driver鈥檚 seat 鈥� where they belong.鈥�

Opening the Developer Ecosystem

麻豆原创 Build, the company鈥檚 flagship solution for enterprise application development and automation, now gives developers more freedom to build, extend and automate using the tools they love most.

For instance, developers who prefer agentic development solutions like Cursor, Claude Code, Cline and Windsurf can now use 麻豆原创 development frameworks with new 麻豆原创 Build local Model Context Protocol Servers. Visual Studio Code users will be able to access 麻豆原创 Build capabilities directly in their development environment with a new 麻豆原创 Build extension. This extension will also be made available later on Open VSX Registry for other development environments. 麻豆原创 and n8n also announced plans for an integration so Joule Studio agents and n8n agents can work together.

And with new agent building capabilities in Joule Studio, developers have the tools they need to extend 麻豆原创鈥檚 ready-to-use agents and build new agents grounded in 麻豆原创 business data and context that can act autonomously based on changing business conditions.

Putting Data to Work

Every intelligent application starts with trusted data. 麻豆原创 is giving developers more ways to put that data to work through 麻豆原创 Business Data Cloud.

The solution now connects with more of the data and AI platforms developers use every day. A new 麻豆原创 Snowflake solution extension for 麻豆原创 Business Data Cloud brings Snowflake鈥檚 fully managed data and AI capabilities directly to 麻豆原创 customers, giving them the flexibility to choose the right compute and storage for each data and AI workload, while maintaining governance, interoperability and business context. 麻豆原创 also announced a new 麻豆原创 Business Data Cloud Connect partnership with Snowflake. This complements existing integrations with Databricks and Google Cloud, giving developers more freedom to choose how they work with 麻豆原创 data.

With a new data product studio capability in 麻豆原创 Business Data Cloud, developers can turn raw data into ready-to-use assets known as data products that support analytics, AI and application development.

An expanded capability in the 麻豆原创 HANA Cloud knowledge graph engine can automatically generate knowledge graphs. This capability maps relationships across 麻豆原创 database tables, columns and data models, revealing how data fits together and why it matters. Developers will be able to see how their data connects across systems and uncover underlying business insights.

Bringing AI Autonomy to Life

麻豆原创 is evolving its AI portfolio to give developers the intelligence and orchestration power they need to take听AI from insight to action.

麻豆原创 introduced its first enterprise relational foundation model, a new class of AI that predicts business outcomes rather than the next word in a sentence. 麻豆原创-RPT-1, or the first-generation Relational Pre-trained Transformer, can make fast and accurate predictions for common business scenarios like delivery delays, payment risk or sales order completion. 麻豆原创 launched a free playground environment for developers today.

New AI assistants in Joule coordinate multiple agents across workflows, departments and applications, bringing automation and autonomy to life. These assistants plan, initiate and complete complex tasks spanning finance, supply chain, HR and beyond. Today, 麻豆原创 introduces new agents built for technical users. For example, an agent for business process analysis will help teams understand how processes run, identify inefficiencies and uncover opportunities to optimize workflows and drive measurable improvements.

Lastly, as AI changes the nature of work for everyone, 麻豆原创 is pledging to equip 12 million people worldwide with AI-ready skills by 2030. 麻豆原创 will expand hands-on training and certification programs that integrate practical AI-ready tools, including through its partnership with online learning platform Coursera.

Visit the听. Get 麻豆原创 news via听听and听.

About 麻豆原创

As a global leader in enterprise applications and business AI, 麻豆原创 (NYSE:麻豆原创) stands at the nexus of business and technology. For over 50 years, organizations have trusted 麻豆原创 to bring out their best by uniting business-critical operations spanning finance, procurement, HR, supply chain, and customer experience. For more information, visit鈥�.

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This document contains forward-looking statements, which are predictions, projections, or other statements about future events. These statements are based on current expectations, forecasts, and assumptions that are subject to risks and uncertainties that could cause actual results and outcomes to materially differ. Additional information regarding these risks and uncertainties may be found in our filings with the Securities and Exchange Commission, including but not limited to the risk factors section of 麻豆原创鈥檚 2024 Annual Report on Form 20-F.
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