• Jesper Schleimann, AI Officer, EMEA at SAP
• Jhani Coetzee, EPI-USE Labs

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

In this episode, you’ll gain insight into:

• How AI is being embedded into 51ˇçÁ÷environments
• The shift from experimentation to execution
• Where organisations are seeing real value from AI
• What to consider as you start or scale your AI journey

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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 – and take them – 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’t 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 “pilot purgatory”, 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.

Ěý, 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 SAP’s see measurable gains:

• 37%Ěýreport improved process productivity
• 39%Ěýachieve greater cost efficiency
• 36%Ěýboost workforce productivity
• 35%Ěýaccelerate 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.

SAP’s Joule, an AI agent framework embedded into the 51ˇçÁ÷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. 51ˇçÁ÷recommends a four-part readiness framework:

1 Data quality and accessibility –ĚýAgents 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 –ĚýAI 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 –ĚýWho 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 –ĚýJust 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.

The post The Suite Spot: A Practical Guide to Business AI Agents appeared first on 51ˇçÁ÷Africa News Center.

The comments were made by Sunil Geness, director of global government affairs and CSR at 51ˇçÁ÷Africa, and Sergio Maccotta, senior VP and GM for 51ˇçÁ÷Middle East and Africa – 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 SAP, autonomous operations – where nearly half of all business processes run independently and most operational work isĚýautomatedĚýor AI-augmented – are being deployed across finance, supply chain and human resources in industries including energy, retail and manufacturing.

“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,” said Maccotta. “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.”

51ˇçÁ÷cited data fromĚýĚýindicating 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 “autonomous enterprise” 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.

“AI 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,” Geness said.

51ˇçÁ÷added that some analysts estimate AI could contribute $1.5 trillion to Africa’s economy if the continent captures 10% of the global AI market by 2030.

Data sovereignty

51ˇçÁ÷executives said Africa’s digital transformation continues, with data sovereignty emerging as a key factor.

“For 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,” Maccotta added.

The company said Africa’s 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.

“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 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.”

This article first appeared in .

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The good news, says , Managing Director: Southern Africa at SAP, is that real-world AI value is already becoming clear. “Across 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.”

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

“To 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.”

Of critical importance too is defining and implementing a comprehensive AI framework that prioritises governance at the outset. “An 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.”

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 & automationĚý

Finance departments are under increasing pressure to speed up reporting cycles, reduce operational costs, and strengthen cash flow visibility. “In 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,” 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,ĚýĚýand accelerating payment cycles.

Another high-impact tool is AI-assisted cost centre analysis. “Instead of manually wading through complex reports, finance teams can use generative AI to instantly summarise data, highlight KPIs, and recommend next steps,ĚýĚýand freeing up analysts to focus on strategic insights,” 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 teamsĚýĚýto streamline recruitment, performance reviews, and workforce planning, delivering up to 35% process efficiency gains.

“One practical use case gaining widespread adoption is AI-powered applicant screening, where machine learning scans CVs to match applicant skills with job requirements. ThisĚýĚýand speeds up hiring for hard-to-fill roles.”

Pillay points to AI-generated job descriptions as another time-saving tool. “HR professionals input a few keywords and receive polished, bias-reducing job listings in seconds, helping improve candidate fit andĚý.”

3 Scaling service and sales excellence

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

“Tools like chatbots and virtual assistants now handle routine queries with 24/7 availability, cutting service costs and improving customer satisfaction,” explains Pillay. “Where 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 canĚýĚýand improve first-contact resolution by 10%.”

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,Ěý.

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.

“AI tools have become indispensable to category planning efforts,Ěý. This is helping managers move 90% faster and make more informed and proactive decisions.”

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

5 Improved customer engagement and inventory management

AI’s 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.

“With 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,” says Pillay.

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

“As 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,” says Pillay.

This article first appeared here:

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The era of digitisation is giving way to something more profound: the Autonomous EnterpriseĚý–Ěýa 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 ‘a tipping point’

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’s 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 fromĚý, and adoption is accelerating across EMEA.

ĚýGlobal 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 SAP’s strengths in this arena to bear. Through our flagship – now more accessible than ever thanks to the RISE with 51ˇçÁ÷initiative – we equip businesses with modern, clean core ERP systems that are upgrade-safe and AI ready.

, SAP’s 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’s 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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51ˇçÁ÷was founded in 1972 by five former IBM employees. In a publicity shot from the time, they’re posed around a table in a nondescript office, all holding pens and hunched over a large blueprint. The hair is bouffant, the suits dark, and no one is smiling. These are clearly men who take their jobs very seriously.

As the story goes, Xerox wanted its systems migrated to IBM, and the latter firm put five engineers on the project, who all worked in Mannheim, Germany. The project ground to a halt for reasons unclear, but the five had seen the gap in the market, and left to form their company, which they called Systemanalyse und Programmentwicklung, or System Analysis and Programme Development. It has been a long haul since the ‘70s, with the company constantly tinkering with its solutions while persuading many of the world’s largest firms that they can run their businesses better with its suite of solutions.

At the core of this suite is . This is an in-memory database, andĚýdataĚýis stored in RAM instead of disk. These databases can handle spikes in traffic, such as at telcos orĚýbanks, and allow for real-time analytics. Customers can have the platform installed in their own server room, or in the public or private cloud, or both. And with each passing year, more features are added, such as GenAI assistant ; its sparkly icon now appears on seemingly every page of the platform. Joule, on which 51ˇçÁ÷partnered with , can run queries on all kinds of data, produce forecasts, book meetings, and make graphs and charts on the fly.

There was still an impression in the market that 51ˇçÁ÷was extremely expensive, very complicated, and not very pretty.

Garth Ridgway, NTT

All this work has borne fruit, and in March 2025, the company overtook Danish drugmaker Novo Nordisk to become the EU’s most valuable company, with a market cap of €313bn. It reported strong results for its third quarter in October 2025, seeing revenue rise by 7% to €9.08bn. Cloud revenue saw growth of 22%. But there are challenges ahead, not least of which is convincing customers that it’s now past the time to migrate their workloads from the old system called ECC, which is usually run on-prem, to S4. The company would like nothing better than for all its customers to move to S4, but many are proving to be recalcitrant. Support for ECC, introduced in 2015, is meant to come to an end in 2027, but customers can buy extended support until 2030, which will cost them an extra 2% in addition to their annual maintenance costs. Many companies contend that the current systems seem to be working just fine, and many are also cognisant, and terrified, of the upheaval theĚýmigrationĚýis going to cause. To this, the company dangles the prospect of new features with S4, along with the constant refrain that support for the old systems is, at some point, going to grind to a halt.

An estimated 40% of 51ˇçÁ÷customers in North America are yet to start migrating, according to a survey run by the Americas’ 51ˇçÁ÷User Group. About 60% of the 173 members are already live on S4, or are in the process of moving over, according to the research in November. Gartner said in March 2025 that most ECC customers had still not bought S4 licences. It said that in Q4 in 2024, 29% of 35 000 ECC customers had bought or subscribed to S4 licences.

Christian Hestermann, Gartner senior director and analyst for business applications, believes this is likely to remain the status quo. Speaking toĚýThe Register, he said that since September 2023, the company’s messaging had been about AI and Joule. “The message now is, ‘You have to have AI, or you’ll not be able to survive’. So far, that did not have a major influence. So none of those big events or big announcements has made a massive change up to now; the [migration] progress has remained fairly steady.”

No such third-party survey has been run in South Africa, and the company doesn’t make public either its customer numbers or how many have migrated.

I asked , CTO, SAP; , MD of 51ˇçÁ÷South Africa; and , senior director, SAP, at NTT, for their views on how this migration conversation is progressing, and they spoke as one voice. Pillay says 51ˇçÁ÷had “made a clear line in the sand” with the date past which it won’t continue with maintenance. “Customers are aware of that and are making their plans to transform.”

Pillay was appointed to the top job in August 2025, and says she’s going to ensure the local operation is well equipped to have these conversations with its customers, as well as partners.

Why haven’t they moved yet?

It’s a matter of context, she says. “We have customers who have been with us for 20, 30 years, and that’s a godsend. But they bring a lot of legacy with them, and not just technical legacy. They have unarchived environments, non-optimised processes, or a lot of custom-built systems. They may have implemented ERP back in the day, and, in most cases, it was a project with a long duration, with a lot of blood and sweat going into it. Most companies understand they need to take that technology transformation journey, but they’re trying to balance the timing and effort required to bring a legacy system across [to S/4HANA]. There’s hesitation, because in the back of their minds, they remember that when they did it [last], it was really very difficult.”

Financial services is one vertical where customers have realised the imperative of the move, she says. The move was a “no-brainer” for Pillay, and offered data analysis, AI, agentic AI, and GenAI capabilities. She says if a customer isn’t speaking to 51ˇçÁ÷about the move, it may well be too late to get a system installed before the cut-off date.

“We’re prioritising helping those who are speaking to us to make that decision. We have to work with every customer individually to understand how they can do that transformation. There are different routes, based on capacity, budget and risk appetite. If you delay a decision, it’s going to be too late.”

The slow movers are also going to bring pressure to bear on system integrator partners, as Pillay envisages them being at capacity, or overcapacity, doing 51ˇçÁ÷implementations as the deadline draws ever closer.

“Then you’ll pay for skills, either locally, or you’ll have to look abroad. There’s going to be a material impact if a business takes this decision later on, and that’s what we’re trying to discuss with our customers and help them plan properly, so they don’t get into a position where they say, ‘Wow, we’ve left this too late, and now this is a monster’.”

51ˇçÁ÷NTT’s Ridgway says he’s spent the better part of three decades implementing 51ˇçÁ÷at customers, during which time the ERP landscape has changed dramatically. In the past, it was a two-horse race, the other being Oracle, and both companies served the large enterprise almost exclusively, he says. Large implementations took a lot longer than they do nowadays, two to four years, on average. He says 51ˇçÁ÷now comes more pre-configured, or more off-the-shelf than in the past. He and his team will do a quick discovery at a customer, and will then do what he calls a “vanilla” implementation to get the system up and running. Any innovation can come later, post the go-live date.

Another change in the market is that 51ˇçÁ÷is now competing for business with companies such as Microsoft and Sage.ĚýRidgway says he’s seen a shift in the attitude and sales pitch from SAP.Ěý“In South Africa, there was a time when 51ˇçÁ÷owned 60%, 65% of the large enterprise ERP market, and Oracle was lagging behind. Every single large enterprise in South Africa has some form of ERP. There was still an impression in the market that 51ˇçÁ÷was extremely expensive, very complicated, and not very pretty.”

As for looks, he says the new interface is more user-friendly, and “much nicer to play with”. The company has also realised that there aren’t any R100mn projects out there anymore, which has meant its gone looking for customers among medium-sized businesses. “These aren’t small businesses, but they’re just not the $1bn organisations. I don’t think 51ˇçÁ÷wants to play in the small space, because that’s where Sage and QuickBooks are playing. But there’s a massive market in the mid-tier, which wasn’t untapped, but which 51ˇçÁ÷wasn’t focused on,” he says.

Customers have also become “smarter than they used to be”. “In the old days, we would go and sell 51ˇçÁ÷to them, explaining what it did. Today, the CIO knows everything about 51ˇçÁ÷and what it can do for them. It’s less about consulting around the roadmap, and more about, ‘This is what I want. Can you get me there?’ CIOs are m ore well-read, and they know what they want. It’s no longer about selling the technology; maybe it wasn’t ever about selling the technology. It’s more about selling the business solution. Once it’s implemented, what are the benefits that they’re going to realise? What is the innovation that we can bring to it, and what will be the ease of integration?”

Ridgway says the biggest part of its business is its application managed service (AMS) offering, and it’s serving around 100 clients on that platform.

As for the transition to S4, he thinks SAP, “tried to use a stick to force people to move. It realised that there weren’t enough consultants and capacity worldwide to move every single 51ˇçÁ÷customer from ECC to S4. It found a lot of resistance from clients, and it had a couple of clients that left them because they felt irritated by the stick and the threat of having support cut off. Coca-Cola Beverages Africa moved off 51ˇçÁ÷and went onto [Microsoft] Dynamics, which I think it found quite difficult. It’s a massive change and huge investment to start all over again. So 51ˇçÁ÷did lose some clients, but it eventually moved the date out to 2027. It also changed the sales approach from a stick to more of a carrot.”

The carrot appears to involve talking up the benefits and exciting new features of S4. Ridgway says 51ˇçÁ÷has dramatically changed the way it does business. “In the past, I think 51ˇçÁ÷was seen as very arrogant, very expensive and very complex. Today, it seems less arrogant and is playing in a different market.”

The company is now far more flexible with its pricing and subscription models. “It’s also not as complex as it used to be. I’ve been involved in projects that took 24 months from start to go-live. We’ve just completed a project that was five weeks from start to go-live.”

Do you still have customers who are on ECC? “Lots,” he says. “The biggest part of our consulting business is trying to convince customers who are on ECC that we get to the cloud, and we can get them to S4, with all the innovations and advantages.

“Some customers will say, ‘if it’s not broken, I don’t need to fix it. It’s giving me what I need’,” he says, but he believes this will add business risk because if something does go wrong, “come January 1 2028, you have no support from SAP”.

This conversation with customers reminds him of those held when the cloud model was nascent around a decade ago, and “everybody said they had to move to the cloud”, which he says is similar to where AI is at the moment. “Nobody actually knows what it means. A lot of clients did rush to the cloud, and realised two years later that it was quite expensive, and it’s more complicated. But we’re a lot more mature now, 10 years in. There are a lot of clients who are hybrid. I think the way 51ˇçÁ÷designed its solution allows for that.”

He says with the application managed services part of the business, NTT will provide support for customers, many of which will sign up for a 36-month contract that will give them access to a call centre with certified, senior agents. As for what the customers are struggling with, he says the majority of the calls are related to password resets and authorisations.

Herzig, 51ˇçÁ÷CTO, speaking at this year’s Saphire conference in Madrid, said the company doesn’t disclose the official numbers, but it has around 10 000 customers worldwide that are on the cloud path. “The majority of customers are now making the cloud decision, specifically because the maintenance will definitely be over in 2028. And with all the innovation, there’s a very clear reason why they need to move.”

Why don’t they want to move?

“It depends on the customer,” he says. “Their systems have grown, historically, and they’ve been heavily customised. Back in the day, you could code left and right, because the code was open. Every table and every function module, and the systems integrators added their part to it. They did a lot of custom code. Maybe they were adding functionality, but they didn’t know [what effect it would have]. You’d start off with a very clean version of what 51ˇçÁ÷delivered, and then we innovated with the product. We built capabilities, and we changed the technology, and the customer did the same. And the longer this branching took, the harder it is to reconcile.”

* Article first published onĚý

The post In Search of a Clean Core appeared first on 51ˇçÁ÷Africa News Center.

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. 51ˇçÁ÷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, 51ˇçÁ÷aimed to show a working example of AI at scale, handling everyday complexity inside one of the world’s largest software organisations.

51ˇçÁ÷chief technology officer and chief AI officer, Philipp Herzig, told Business Times at TechEd that the clearest evidence of maturity came from within 51ˇçÁ÷itself. The company’s 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.

“If you look at AI at scale, what is really real and what is working very well, just look at Joule,” he said. “It’s used by more than 30,000 employees every month, about a third of the 51ˇçÁ÷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.”

Herzig said Joule had become the company’s single interface for daily tasks. “You 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.”

Innovations across SAP’s unique flywheel of applications, data and AI put developers in the
driver’s seat — Muhammad Alam, 51ˇçÁ÷executive board member

That success set the stage for TechEd’s central announcement: an AI model called SAP-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.

51ˇçÁ÷described it as a model that “can make fast and accurate predictions for common business scenarios like delivery delays, payment risk or sales order completion”. Instead of producing text, it reads how data behaves across systems, turning business logic into predictive insight.

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

“We 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. “What 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.

“It’s 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.”

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

“Anyone can do a demo. Getting it enterprise-ready at scale — that’s the tough challenge. That’s why we’re solving one problem after another, each for a specific outcome in the overall stack.”

51ˇçÁ÷executive board member Muhammad Alam tied this philosophy to the developer community.

“SAP’s announcements give developers the tools they need to deliver at the speed of AI,” he said.

“Innovations across SAP’s unique flywheel of applications, data and AI put developers in the driver’s seat.”

That “flywheel” anchored the narrative of TechEd:
• applications generate data;
• data trains predictive models;
• models return intelligence to the applications.

Each loop strengthens the next, creating a flywheel effect of acceleration of innovation. 51ˇçÁ÷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 SAP’s 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 51ˇçÁ÷Africa News Center.

51ˇçÁ÷has taken artificial intelligence beyond the realm of language. At the 51ˇçÁ÷TechEd 2025 conference in Berlin this week, the company introduced a new class of AI designed to predict business outcomes rather than words. The result is what 51ˇçÁ÷claims to be the first enterprise relational foundation model, called SAP-RPT-1, or Relational Pre-trained Transformer.

It represents a shift in what a foundation model can be: rather than being a generator of sentences, it is a predictor of decisions.

51ˇçÁ÷describes SAP-RPT-1 as an AI that “can make fast and accurate predictions for common business scenarios like delivery delays, payment risk, or sales order completion”. It uses the relationships between business data rather than linguistic patterns to anticipate what happens next in an organisation’s operations. In simple terms, it reads the business, as opposed to mere text.

To encourage developers to explore what that means in practice, 51ˇçÁ÷has launched a free playground environment where they can test predictive scenarios, simulate business situations, and see how relational AI behaves when exposed to live data. For developers accustomed to fine-tuning language models, it represents a different kind of creativity, rooted in the mechanics of business itself.

“SAP’s announcements today give developers the tools they need to deliver at the speed of AI,” says Muhammad Alam, member of the executive board of SAP. “Innovations across SAP’s unique flywheel of applications, data and AI put developers in the driver’s seat – where they belong.”

That flywheel was clearly visible across the rest of TechEd’s announcements.

The company’s 51ˇçÁ÷Build platform, its centrepiece for enterprise application development and automation, has been re-engineered to give developers more freedom to use tools they already rely on.

Developers who prefer agentic development environments like Cursor, Claude Code, Cline and Windsurf can now use 51ˇçÁ÷development frameworks through new 51ˇçÁ÷Build local Model Context Protocol Servers. Visual Studio Code users can access 51ˇçÁ÷Build capabilities directly in their existing development environment via a new 51ˇçÁ÷Build extension, which will later also appear in the Open VSX Registry for use with other development environments.

51ˇçÁ÷revealed plans for integration with automation platform n8n, allowing its Joule Studio agents to work alongside n8n’s own agents. This kind of cross-agent collaboration reflects the company’s growing emphasis on orchestration: enabling multiple intelligent systems to coordinate tasks across applications and departments.

The data layer that feeds these systems is also evolving. Every intelligent application depends on trusted data, and 51ˇçÁ÷is extending its reach through 51ˇçÁ÷Business Data Cloud. A new 51ˇçÁ÷Snowflake solution extension brings Snowflake’s managed data and AI capabilities directly to 51ˇçÁ÷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”.

The announcement was reinforced by a new 51ˇçÁ÷Business Data Cloud Connect partnership with Snowflake, adding to existing integrations with Databricks and Google Cloud. The result is a more open, federated data ecosystem that lets developers work with 51ˇçÁ÷data wherever it resides.

A new data product studio capability in 51ˇçÁ÷Business Data Cloud now allows developers to turn raw data into ready-to-use assets known as data products, designed for analytics, AI and application development. At the same time, the 51ˇçÁ÷HANA Cloud knowledge graph engine can automatically generate knowledge graphs. This means it maps relationships across 51ˇçÁ÷database tables, columns, and data models to reveal how data fits together. For developers, it is a new way of seeing how information connects across systems, turning structure into insight.

51ˇçÁ÷is also extending its Joule AI portfolio, which now includes new assistants that can coordinate multiple agents across workflows, departments and applications. These assistants are designed to “plan, initiate, and complete complex tasks spanning finance, supply chain, HR, and beyond.”

Among the new offerings is an agent for business process analysis, helping teams understand how processes actually run, identify inefficiencies, and uncover opportunities to optimise workflows and achieve measurable improvements.

51ˇçÁ÷pledged to equip 12-million people worldwide with AI-ready skills by 2030, through a partnership with Coursera that expands hands-on training and certification in practical AI tools. The goal, 51ˇçÁ÷says, is to make AI accessible to “people everywhere”.

*ĚýArthur Goldstuck is CEO of World Wide Worx, editor-in-chief ofĚý, and author ofĚýThe Hitchhiker’s Guide to AI – The African Edge.

This article first appeared in .

The post 51ˇçÁ÷Teaches AI to Predict Business Outcomes appeared first on 51ˇçÁ÷Africa News Center.

BERLINĚý— At 51ˇçÁ÷TechEd in 2025,Ěý (NYSE: SAP) brings AI deep into the development process to level up how developers build.

New AI-driven capabilities in the 51ˇçÁ÷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, 51ˇçÁ÷also pledges to equip 12 million people worldwide with AI-ready skills by 2030.

“SAP’s announcements today give developers the tools they need to deliver at the speed of AI,” said Muhammad Alam, member of the Executive Board of 51ˇçÁ÷SE. “Innovations across SAP’s unique flywheel of applications, data and AI put developers in the driver’s seat — where they belong.”

Opening the Developer Ecosystem

51ˇçÁ÷Build, the company’s 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 51ˇçÁ÷development frameworks with new 51ˇçÁ÷Build local Model Context Protocol Servers. Visual Studio Code users will be able to access 51ˇçÁ÷Build capabilities directly in their development environment with a new 51ˇçÁ÷Build extension. This extension will also be made available later on Open VSX Registry for other development environments. 51ˇçÁ÷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 SAP’s ready-to-use agents and build new agents grounded in 51ˇçÁ÷business data and context that can act autonomously based on changing business conditions.

Putting Data to Work

Every intelligent application starts with trusted data. 51ˇçÁ÷is giving developers more ways to put that data to work through 51ˇçÁ÷Business Data Cloud.

The solution now connects with more of the data and AI platforms developers use every day. A new 51ˇçÁ÷Snowflake solution extension for 51ˇçÁ÷Business Data Cloud brings Snowflake’s fully managed data and AI capabilities directly to 51ˇçÁ÷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. 51ˇçÁ÷also announced a new 51ˇçÁ÷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 51ˇçÁ÷data.

With a new data product studio capability in 51ˇçÁ÷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 51ˇçÁ÷HANA Cloud knowledge graph engine can automatically generate knowledge graphs. This capability maps relationships across 51ˇçÁ÷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

51ˇçÁ÷is evolving its AI portfolio to give developers the intelligence and orchestration power they need to takeĚýAI from insight to action.

51ˇçÁ÷introduced its first enterprise relational foundation model, a new class of AI that predicts business outcomes rather than the next word in a sentence. SAP-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. 51ˇçÁ÷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, 51ˇçÁ÷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, 51ˇçÁ÷is pledging to equip 12 million people worldwide with AI-ready skills by 2030. 51ˇçÁ÷will expand hands-on training and certification programs that integrate practical AI-ready tools, including through its partnership with online learning platform Coursera.

Visit theĚý. Get 51ˇçÁ÷news viaĚýĚýandĚý.

About SAP

As a global leader in enterprise applications and business AI, 51ˇçÁ÷(NYSE:SAP) stands at the nexus of business and technology. For over 50 years, organizations have trusted 51ˇçÁ÷to bring out their best by uniting business-critical operations spanning finance, procurement, HR, supply chain, and customer experience. For more information, visit .

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