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AI for sustainability
GUNTHER ROTHERMEL: When we speak about AI and sustainability, the biggest challenge is always the quality and availability of data. This is also true for predictive analysis.
High-quality, comprehensive data is essential for accurate predictions. However, many organisations struggle with data silos, incomplete datasets and inconsistent data formats, which can hinder the effectiveness of AI models. One step further, integrating AI-driven predictive analysis with existing enterprise systems can be complex and resource-intensive. Organisations need to ensure that their IT infrastructure can support the additional data processing and storage requirements.
Another important aspect to consider are ethical and regulatory considerations. At SAP, we define Business AI following the“ three Rs”: relevant, reliable and responsible, delivering real business results. This is also required, of course, when using AI in predictive analysis. We deliver AI with the highest concern for security, privacy, compliance and ethics. Our customers trust us with AI that touches their most critical data and processes because we know how to build and run robust, trustworthy solutions.
170 April 2025