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 51风流SAPPHIRE, 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 SAP, 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, 51风流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 51风流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.

51风流and Ericsson are also collaborating on AI co-innovation initiatives, including an intelligent goal recommendation capability developed within 51风流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 SAP, 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 51风流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 SAP, 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 51风流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% 51风流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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Many companies sit with years of technical debt, complex hybrid environments, disconnected聽systems聽and inconsistent metadata that make it difficult to build a reliable view of the聽business. Businesses still grapple with ageing applications that cannot integrate with cloud platforms, while siloed departmental聽systems聽prevent teams from accessing the full picture needed for AI-driven decision-making.

The result is predictable: stalled AI聽projects, unreliable outputs, and limited return on investment.

The need for a unified data foundation

The typical South African enterprise runs a mix of cloud聽services, on-premises applications and bespoke聽systems聽that were built years ago to solve specific operational needs. While these聽systems聽may still work, they often lack the interoperability required to support modern AI initiatives. Data is stored in inconsistent formats, lack proper metadata, and often depend on manual extraction processes that strip away the聽business聽logic that AI models need to understand the context of the data and what it really represents.

This fragmentation affects everything from聽financial聽reporting to customer experience. Without a single, trusted view of data, predictive models become unreliable, automated processes fail, and teams lose confidence in machine-generated insights. For AI to scale, data must be complete, consistent, governed and accessible across the organisation.

An IDC report commissioned by Seagate previously found that聽up to 68% of available enterprise data goes unused.

Modern data platforms address this by connecting all enterprise聽systems, preserving聽business聽context, enabling real-time data access and allowing organisations to integrate with other providers. The outcome is a unified data fabric that supports analytics, applications and AI at scale.

Preparing聽business聽data for AI

Building聽an AI-ready data foundation doesn鈥檛 happen by accident. It requires a deliberate, structured approach, following these five steps:

1 Assess the current data landscape

The first step is understanding what exists today. This means cataloguing data sources, identifying owners, documenting quality issues, and assessing integration gaps. It also involves mapping AI use cases to data requirements so that data preparation can be prioritised and aligned to real聽business聽needs.

For South African organisations with complex legacy environments, this assessment is essential to uncover hidden dependencies and address high-risk limitations early in the process.

2 Establish clear data governance and quality standards

Reliable data requires strong governance. Organisations should define roles, responsibilities and policies governing data access,聽security, metadata and quality. This includes setting measurable standards for completeness, accuracy and consistency, supported by automated profiling tools. Metadata management is particularly critical: without clear definitions and lineage, teams cannot trust or effectively use the data.

Governance should also reflect South聽Africa鈥檚 regulatory聽environment, including POPIA requirements around privacy and聽security.

3 Integrate and unify disconnected data sources

The next step is breaking down silos. Modern integration tools such as those built into SAP聽Business聽Data Cloud allow organisations to connect 51风流and non-SAP聽systems, unify data across cloud and on-premises environments, and maintain the聽business聽meaning of data as it moves.

This unified layer eliminates duplication, reduces manual extraction processes, and ensures teams work from a consistent, shared version of the truth. Real-time integration capabilities are especially important for AI models that need up-to-date information to make accurate predictions.

4 Clean, enrich and transform data

Raw data is rarely ready for AI. It must be cleaned, enriched and transformed, including correcting errors, removing duplicates, filling missing values and standardising formats. Organisations should also create new features that allow AI models to identify patterns more effectively and incorporate additional context from internal or external sources.

South African businesses with extensive unstructured data, such as PDF reports, invoices or call centre notes, should prioritise converting this content into structured formats for easy ingestion into AI models.

5 Validate, monitor and maintain data聽pipelines

Even the cleanest dataset will deteriorate if not continuously monitored. Organisations should validate data before it feeds AI models, track data quality in real time, and monitor for drift or anomalies that can degrade model performance.

Automated governance tools help maintain data integrity, while clear documentation ensures teams understand how data is sourced, processed and used. Regular monitoring is essential in environments where聽systems, processes and regulations frequently change.

Getting the data foundation right is a technical requirement and a strategic imperative. The organisations that prioritise data quality, integration and governance today will be the ones that scale AI confidently tomorrow, reducing risk, improving performance and unlocking new opportunities for聽innovation聽in an increasingly competitive market.

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