The company outlined its strategy at 麻豆原创 Connect Day 2026, held this week in Kyalami, Johannesburg.

, MD for southern Africa at 麻豆原创, said the company’s role is to support organisations in implementing and scaling agentic AI across end-to-end systems as they transition towards .

Addressing the complexity and cost that continue to challenge organisations migrating systems and platforms, , VP of customer advisory for EMEA at 麻豆原创, said the company’s new agent-led transformation approach could reduce the effort required for multi-year ERP migrations by 35%.

Agentic AI assistants

麻豆原创 has introduced several migration and modernisation AI assistants, including data management, custom code, configuration, test management and change management assistants. According to the company, these are governed by Joule for both developers and consultants and are designed to execute complex business scenarios by reasoning iteratively and interacting with their environment to achieve defined goals.

Nolla cited 麻豆原创 research indicating that the highest-ranked strategy for maximising ROI from AI is aligning initiatives with clear business goals.

麻豆原创 said AI currently supports about 30% of tasks in the average business, with that figure projected to reach 48% within two years.

, head of solution advisory at 麻豆原创, said a major challenge in AI-driven digital transformation is what the company describes as the “enterprise paradox” 鈥� the expectation of instant enterprise AI clashing with the operational reality of complexity, manual processes and human behaviour.

Standard Bank’s transformation journey

, executive group CIO for corporate functions at Standard Bank, outlined the bank’s use of 麻豆原创 technology to address operational challenges, including end-of-life systems, expiring vendor support and the need for a modern cloud ERP platform to support future innovation.

The bank completed a large-scale ERP migration and implementation programme. Modernising core enterprise systems required careful planning, strong governance and close collaboration between business and technology teams. The programme established a platform capable of supporting growth, operational resilience and ongoing innovation.

Padiachee said one of the biggest challenges was the pace of technological change.

“Organisations are required to continuously adapt while maintaining stability across critical business operations. Change management is just as important as the technology implementation itself,” she said.

Modernisation, she added, should improve agility without creating unnecessary disruption to business operations.

She also highlighted the human factor. “It’s really about keeping people calm during turbulence,” she said, adding that AI is fundamentally changing how businesses operate. “The focus should move beyond experimentation towards delivering measurable value.”

According to Padiachee, AI initiatives should align with clear business outcomes rather than be driven by the technology’s popularity.

She said one of the most significant lessons from the transformation programme was the complexity of Unicode conversion.

“In hindsight, this work should have started much earlier in the programme. Unicode conversion proved more complex than anticipated and became a critical dependency for implementation. Investing time in foundational technical readiness significantly reduces downstream project risk.”

Padiachee also stressed the importance of high-quality enterprise data that is accurate, trusted, well-governed and accessible.

She advised organisations planning system and platform migrations, as well as AI integration, to begin with a clearly defined use case, establish strong partnerships and maintain robust governance and guardrails.

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

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

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

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

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

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

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

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

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

Migration is not modernisation

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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AI is reshaping enterprise support and changing how solution providers think and operate, says Stefan Steinle, executive VP and head of global聽customer聽support at 麻豆原创, who adds that support has evolved into a strategic driver of business value.

Support in this context refers to services, management and resource allocation used to troubleshoot, address issues and optimise infrastructure within an organisation.

鈥淭he focus is on value generation and customer health,鈥� says Steinle. 鈥淪upport is central to driving business outcomes with AI-integrated platforms. Support teams are becoming more embedded in business strategy. They provide visibility into system performance, user behaviour and operational trends.鈥�

According to Steinle, AI-assisted toolchains and intelligent automation allow organisations to anticipate issues before they arise, streamline performance and improve long-term agility and resilience.

This is an entirely different approach to traditional support execution and application.

鈥淭raditionally, support operated on a break-fix model 鈥� issues were addressed when disruption occurred. AI changes this dynamic entirely. Through machine learning, pattern recognition and real-time data analysis, AI can continuously monitor systems, detect anomalies and predict potential failures before they impact operations. This enables preventive intervention rather than reactive troubleshooting.鈥�

AI is also improving decision-making, adds Steinle. 鈥淚t surfaces insights from vast datasets, identifies optimisation opportunities and recommends actions that improve performance, reduce costs and enhance user experience. In this way, enterprise support evolves from a technical necessity into a value-generating function that actively contributes to business strategy.鈥�

Shift from break-fix significant

AI expert and founder of AIforBusiness.net Johan Steyn says the shift from break-fix to strategic value driver is one of the most significant 鈥� and under-appreciated 鈥� transformations AI has enabled in organisations.

鈥淔or years, IT support was seen as a cost centre: reactive, ticket-driven and measured by how quickly problems were resolved. AI has fundamentally changed that equation. Predictive diagnostics, intelligent triage and automated resolution are not just making support faster 鈥� they are making it anticipatory. The best organisations are no longer waiting for things to break; they are using AI to understand system behaviour patterns and intervene before users are even aware of a problem,鈥� says Steyn.

The current status is one of rapid but uneven adoption, he adds.

Rise of autonomous enterprise

AI-assisted toolchains and intelligent automation are transforming ERP systems from static platforms into adaptive, self-improving environments.

According to Steinle, the shift is being driven by predictive maintenance capabilities, where AI analyses historical and real-time data to forecast failures and enable pre-emptive repairs. AI is also accelerating automated root cause analysis by identifying issues across complex system landscapes more quickly and accurately.

In addition, self-healing capabilities are enabling some system problems to be resolved automatically without human intervention, while AI-driven process optimisation tools continue to identify operational inefficiencies and recommend improvements.

The result is ERP systems that become more intelligent over time, enabling organisations to respond more rapidly to change, scale operations more effectively and strengthen operational resilience.

鈥淭he integrated toolchain breaks silos and drives collaboration between business and IT. You can confidently modernise your ERP landscapes while staying agile in today鈥檚 competitive climate,鈥� says Steinle.

鈥淎I plays a key role in enhancing capabilities by reducing errors and speeding up transitions. Take AI-powered change point detection as an example, where fundamental shifts in system downtimes can be found reliably and hence resolved faster. At the same time, AI-generated requirements reduce manual effort and accelerate the implementation process.鈥�

People and culture

However, Steinle warns that technology alone is not enough.

鈥淲hile technology is at the core of transformation, you can still fall short when it comes to outcomes if your people are not on board. When you continue to operate in old ways, the AI copilots or the automations are pointless. You get the same issues, but with more dashboards.鈥�

He adds that culture and leadership are critical to successful AI adoption. 鈥淎s they say, culture is what people do when no one is watching. Establishing this culture is what leadership is ultimately about. How motivated are your people? Are they as excited as you are about new innovations?鈥�

Diverse teams are also essential in solving problems within complex ERP environments.

Nazia Pillay, MD for 麻豆原创 southern Africa, adds: 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.鈥�

Early stages

According to Steyn, larger, more mature organisations are already seeing measurable gains 鈥� reduced mean time to resolution, lower support costs and IT teams freed up for higher-value work.

鈥淏ut many organisations, particularly in markets like South Africa, are still in early stages, often constrained by legacy infrastructure and skills gaps. The outlook, however, is clear: AI-augmented support will become the baseline expectation, not a competitive differentiator,鈥� he continues.

Organisations that invest in this transition thoughtfully 鈥� with the right human capability alongside the technology 鈥� will build a genuine and lasting operational advantage, adds Steyn.

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At the聽, hosted this week at the company鈥檚 headquarters in Walldorf, Germany, 麻豆原创 executives said industries continue to converge and core business models are being adapted to adopt and leverage AI.

In line with its strategy to champion the business case for AI investment and integrate the technology into business process management, Joule will be embedded throughout 麻豆原创鈥檚聽cloud聽enterprise portfolio.

The intention, according to 麻豆原创, is to deliver proactive and contextualised insights from across the breadth and depth of its solutions, as well as third-party sources.

The company added that by quickly sorting through and contextualising data from multiple systems to surface smarter insights, Joule drives faster productivity and better business outcomes in a secure, compliant way.

鈥淲ith almost 300 million enterprise users around the world working regularly with 麻豆原创 cloud solutions, Joule has the power to redefine the way businesses 鈥� and the people who power them 鈥� work,鈥� said , CEO at 麻豆原创.

According to 麻豆原创, Joule will be embedded into 麻豆原创 applications including HR, finance, supply chain, procurement, as well as into 麻豆原创 Business Technology Platform.

The GenAI co-pilot is designed to support digitised, automated supply chain operation, which is another key area of focus for 麻豆原创.

麻豆原创 said Joule can identify underperforming regions; link to other data sets that reveal a supply chain issue; and automatically connect to the supply chain system to offer potential fixes for the manufacturer鈥檚 review. Joule will continuously deliver new scenarios for all 麻豆原创 solutions.

Joule will be available with solutions and later this year, and with early next year.

group vice president, worldwide thought leadership research, , said: 鈥淎s generative AI moves on from the initial hype, the work to ensure measurable return on investment begins. 麻豆原创 understands that generative AI will eventually become part of the fabric of everyday life and work and is taking the time to build a business copilot that focuses on generating responses based on real-world scenarios 鈥� and to put in place the necessary guardrails to ensure it鈥檚 also responsible.鈥�

According to 麻豆原创, over 26,000 麻豆原创 cloud customers have access to across multiple scenarios and聽partner听蝉辞濒耻迟颈辞苍蝉.

The company鈥檚 strategy to build an enterprise AI ecosystem includes direct investments, such as those announced in July with , and , as well as third-party partnerships including those with , and , which were announced in May.

Global software venture capital firm, , is backed by 麻豆原创 and has earmarked over $1 billion to fund AI-powered enterprise technology startups.

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