Joule Meets Data Migration: Where Most Enterprises Are Falling Behind

A recent article on CIO.com reports that roughly 6 in 10 companies migrating to SAP S/4HANA are choosing not to adopt SAP Joule (AI copilot/agent) alongside the migration, even when there’s no “significant additional resource expenditure” required. This is a striking disconnect. On paper, S/4HANA should be the ideal foundation for AI agents; in practice, many firms feel “not agile, efficient or flexible enough” to combine a major ERP migration with AI enablement.

From our vantage - as consultants who live and breathe data readiness and migration discipline, this reluctance reveals a meaningful, rational hesitation rather than just resistance to innovation.

Reality check: ERP migration - heavy lifting first

The same article on CIO.com candidly points out that even without AI, an S/4HANA migration typically demands more time and budget than originally planned: 46% of companies surveyed admitted they had to allocate additional resources after encountering unforeseen complexities.

That mirrors what we observe when working with clients: legacy data issues, custom code, master-data misalignment, master-data cleanup, cutover & testing cycles, business-process redesigns, these consume the lion’s share of effort. Under those conditions, adding an AI copilot integration introduces additional variables and risk, which many project sponsors deem unnecessary until the core ERP is stable and optimized.

Joule remains promising - but timing and scope matter

The CIO.com article highlights that when used strategically in standardized processes, Joule can deliver early productivity gains: e.g. in procurement — auto-fetching supplier quotes, comparing them, generating draft purchase orders.

Also, the lack of incremental license or interface-integration cost (in many public-cloud / RISE-with-SAP scenarios) makes Joule a “low-hanging fruit” from a cost/effort standpoint.

But the article - and we as seasoned consultants - caution against a “big bang” approach for AI adoption. Instead, we see value in a phased AI adoption strategy: first stabilize S/4HANA, then pick a few high-ROI, standardized business processes for AI - test, adapt, then scale.

As one of the cited experts puts it: “it’s better to select specific business processes and automate them properly and completely than to do everything a little bit.”

What this means for clients - and for us at DataLink Dynamics

For companies planning S/4HANA migration: don’t treat AI as an afterthought - but don’t force it into Day-1 either. Instead:

• Prioritize data quality, master-data alignment, custom-code remediation, and process readiness above all.

• Once the ERP core stabilizes, identify a few high-impact, standardized business processes (e.g. procurement, invoice processing, reporting) for AI enablement and evaluate payback -that’s where tools like Joule shine.

• Adopt a phased, controlled rollout for AI capabilities (rather than “all-in at once”) to avoid disruption, allow user adoption, and maximize success.

For us at DataLink Dynamics, this reinforces our core value proposition: we help clients not just migrate data, but also prepare the data foundation and processes so they’re ready for future advances - whether that’s AI agents, automation, or next-gen analytics.