By: Wayne Meisel - Chief of Staff & Senior Business Manager at SAP Africa

As more South African organisations accelerate their adoption of artificial intelligence, they are confronted by a familiar obstacle: the data simply isn’t ready. AI can only perform as well as the information that powers it, and in many businesses that information remains fragmented, incomplete or locked away in legacy systems. No algorithm, however advanced, can overcome poor data foundations.

Many companies sit with years of technical debt, complex hybrid environments, disconnected systems and inconsistent metadata that make it difficult to build a reliable view of the business. Businesses still grapple with ageing applications that cannot integrate with cloud platforms, while siloed departmental systems prevent teams from accessing the full picture needed for AI-driven decision-making. 

The result is predictable: stalled AI 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 services, on-premises applications and bespoke systems that were built years ago to solve specific operational needs. While these systems 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 business logic that AI models need to understand the context of the data and what it really represents.

This fragmentation affects everything from financial 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 up to 68% of available enterprise data goes unused.

Modern data platforms address this by connecting all enterprise systems, preserving business 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 business data for AI

Building an AI-ready data foundation doesn’t 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 business 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, 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 Africa’s regulatory environment, including POPIA requirements around privacy and security.

3 Integrate and unify disconnected data sources

The next step is breaking down silos. Modern integration tools such as those built into SAP Business Data Cloud allow organisations to connect SAP and non-SAP systems, unify data across cloud and on-premises environments, and maintain the business 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 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 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 innovation in an increasingly competitive market.