The Risk of Overreliance on AI in SAP: Why Data Quality Has to Come First

Most people have had the experience of asking an AI tool a confident, polished question and getting back a confident, polished answer that turns out to be wrong. Usually that’s a minor inconvenience. Inside SAP, at scale, with AI agents acting directly on your business data, it’s a different order of problem entirely.

A 2026 survey of 1,000 C-level executives by Semarchy found that 51% of organisations are implementing AI initiatives without master data management foundations in place, and 38% without enforced data quality standards. As Joule and SAP’s wider agentic AI capabilities get built more deeply into day-to-day SAP processes, that gap between AI ambition and data readiness stops being a background risk and starts being an active one, because these agents don’t just suggest answers anymore. Increasingly, they act.

Why this is different in SAP specifically

Generic AI overreliance is a known problem: people trusting outputs they haven’t checked, treating a confident answer as a correct one. Inside SAP, the stakes are higher for a specific reason. Joule agents and SAP’s AI capabilities don’t operate on general knowledge, they operate directly on your master data: your customer records, product hierarchies, supplier relationships, plant assignments. If that data is duplicated, mismatched, or simply wrong, the AI doesn’t cautiously flag the uncertainty. It acts on the data it has, confidently, and at a speed and scale no human team could match.

That’s the core risk. Overreliance on AI in a general context means trusting a wrong answer. Overreliance on AI in SAP means an agent executing a wrong action, an order shipped to the wrong address, a payment run against a duplicate supplier, a compliance report built on a broken customer hierarchy, before anyone has a chance to notice.

What Overreliance on AI Looks Like in SAP

This rarely arrives as a dramatic failure. It creeps in, one small trust decision at a time:

• A Joule-driven process auto-approves a transaction because the underlying classification “has been right before”
• A team stops spot-checking AI-suggested master data merges because the tool is usually accurate
• An agent resolves a duplicate customer record automatically, picking the wrong one to keep, and nobody notices until an order goes to the wrong address
• A material gets misclassified by an AI-driven data enrichment process, and that error propagates into procurement, finance, and reporting before a human ever reviews it

None of these require the AI to be broken. They require the AI to be roughly right most of the time, and for the organisation to stop checking the times it isn’t. AI models are inherently probabilistic: they select the most likely answer, not the verified one. In a chatbot, that distinction is mostly academic. In an SAP process acting on live master data, it’s the entire risk.

How MDM Is the Foundation for Safe AI Adoption

This is the part that gets skipped in most AI adoption conversations, and it’s the actual fix. AI agents amplify whatever they’re given. Clean, governed master data makes Joule and SAP’s AI capabilities more accurate and more trustworthy. Messy, ungoverned master data makes them unpredictable, confidently so, which is worse than an AI that’s obviously unreliable.

Practically, that means:

1. Know your data quality before you scale AI, not after. If duplicate rates, missing attributes, or classification errors are already a known issue in a domain, that’s exactly the domain where autonomous AI action should wait.
2. Set clear boundaries on where AI can act without review. Not every process carries the same risk if it’s wrong. A Joule agent drafting a report is a different risk profile to one auto-approving a payment.
3. Keep a human data steward in the loop for exceptions. AI should surface uncertainty, not quietly resolve it. If a classification or a merge decision isn’t clear-cut, that’s a human decision, not an automation opportunity.
4. Make governance a prerequisite for automation, not a parallel project. The 51% of organisations building AI without MDM foundations aren’t necessarily making a conscious choice, they’re often just moving faster than their data governance has caught up. Sequencing matters: get the data foundation in first.
5. Review AI outputs on a schedule, not just at go-live. Data changes. New products, new customers, new suppliers. An AI process that was accurate against last year’s master data can quietly drift as this year’s data changes underneath it.

Use AI as an Accelerator, Not an Autopilot

None of this is an argument against Joule or SAP’s wider AI direction. Used well, AI genuinely solves problems that have frustrated data teams for years, working through the sheer volume of records at a scale no human team could match, spotting patterns and anomalies that would otherwise go unnoticed. That value is real. It just depends entirely on what it’s built on top of.

The organisations getting this right aren’t the ones avoiding AI. They’re the ones treating master data quality as the prerequisite, not an afterthought, so that when Joule does act autonomously, it’s acting on a foundation that’s actually been checked.