SAP is tackling a core problem that has long frustrated enterprise leaders: the gap between wanting to personalise customer experiences and actually being able to do it at scale. The company is aligning fragmented commerce data structures to enable operational AI personalisation at the execution layer.
According to Artificial Intelligence News, enterprise leadership routinely sets objectives to anticipate customer needs and deliver relevant interactions across digital touchpoints. But the actual infrastructure running inside these businesses fails to support systematic execution at the required volume.
Why Current Personalisation Efforts Fall Short
The problem is not a lack of ambition — it is a data architecture problem. Recommendation engines display generic product listings because the underlying behavioural data remains isolated in different systems. Marketing departments send emails based on rigid calendar schedules rather than adapting to individual user habits. Corporate loyalty programs issue rewards based entirely on financial transactions while ignoring broader relationship metrics like browsing behaviour or engagement frequency.
These failures are not isolated incidents. They are symptoms of a deeper structural issue: commerce data is fragmented across silos, making it impossible for AI systems to see the full picture of a customer and act on it in real time.
What SAP Is Doing Differently
SAP is addressing this by aligning the data structures that underpin commerce operations. Instead of treating customer data as a byproduct of transactions, the company is restructuring it so that AI models can access and act on it at the execution layer — the point where decisions about recommendations, offers, and communications are actually made.
This means that when a customer visits a website, the AI can pull from a unified view of their behaviour — not just their last purchase — and deliver a recommendation that is genuinely relevant. When a marketing campaign runs, it can adapt to individual habits rather than firing off the same message to everyone on a list. When a loyalty program calculates rewards, it can factor in engagement metrics, not just spending.
Our Take: This Is the Infrastructure Problem That Matters
In our view, SAP is finally addressing the real bottleneck in enterprise AI. For years, companies have bought AI tools expecting instant personalisation, only to find that their data is too messy to use. The problem was never the AI — it was the data plumbing. By aligning commerce data structures at the execution layer, SAP is making it possible for AI to do what it was always supposed to do: deliver relevant, individualised experiences at scale. This is not a flashy announcement, but it is a practical one. And in enterprise technology, practical is often what matters most.
