How are ERP customers faring with AI readiness? ASUG research reveals that S/4HANA migrations are accelerating, with AI in the crosshairs

What happened

New research from ASUG (Americas' SAP Users Group) shows that S/4HANA migrations among SAP customers are picking up pace, even as many organisations remain underprepared for the AI ambitions they are pinning on those migrations. According to ASUG's Lauren Dixon, almost a third of SAP customers are actively discussing artificial intelligence internally without having a meaningful strategy to back those conversations up.

The research frames this as a readiness gap rather than an appetite gap: interest in AI is high, and modernization programmes are moving forward, but the groundwork needed to make AI investments pay off — clean data, updated processes, and a clear business case — is frequently missing or incomplete at the point organisations start talking seriously about AI.

Why it matters

For enterprises running SAP, S/4HANA migration has effectively become the gatekeeper for AI adoption. The technology only delivers on its promise when the underlying ERP environment, data structures and business processes are modernized enough to support it; bolting AI ambitions onto legacy infrastructure, or discussing AI as a goal in itself without the operational foundation, risks disappointing results and wasted investment.

This matters beyond the SAP ecosystem. It is a pattern playing out across enterprise technology generally: AI strategy conversations are outpacing the digital transformation work needed to support them. Organisations that treat migration and modernization as the real enabler — rather than a back-office IT project to get through before the "real" AI work begins — are better positioned to see returns from their AI spend.

By the numbers

• Almost a third of SAP customers are discussing AI internally without a meaningful strategy underpinning those discussions, according to ASUG research.

The Renascence take

The headline risk here isn't that organisations are too slow to adopt AI — it's that many are moving straight to the AI conversation while skipping the unglamorous modernization work that determines whether AI actually functions once deployed.

Most leadership teams experience AI strategy as a talking point long before it's an operating reality, and that gap is exactly where value gets lost. The real work of AI readiness isn't the model you choose — it's whether your core systems, data and processes are coherent enough to feed it accurately. A customer-obsessed operator should treat ERP modernization as the AI strategy, not a prerequisite to one: audit the data and process foundation first, then let the AI use case follow from what the modernized system can actually support, rather than the other way round.