Eight questions, about two minutes. You get a recommended migration approach for your situation, an indicative timeline, and what automation with Migration Studio changes for a landscape like yours.
Your details are only requested at the end, before your result is shown.
Enter your details to see your recommended migration approach, indicative timeline and the automation gains for a landscape like yours.
You answered not sure on part of this. That is not a gap in the assessment, it is a finding: unknowns are the single most common source of post-go-live surprises. Sizing them is the first thing an assessment does.
Migration Studio does not automate a migration evenly. It carries the volume through the middle of the programme, and deliberately steps back where judgement matters more than throughput.
Marked phases are the ones your answers point to. Automation handles what is mechanical; knowing which interfaces to redesign rather than carry over, and spotting risk in something nobody has touched in years, stays a human call.
Comparable programmes with automation applied typically run months from assessment to hypercare.
Indicative only, and not a quote. The traditional comparison assumes a manual programme of the same scope takes roughly times as long. Both ranges narrow sharply once an assessment has established real interface counts, dependencies and payload volumes.
The same five phases, run manually or run with automation. The rows most relevant to your answers are highlighted.
| Phase | Done manually | With Migration Studio |
|---|---|---|
| Assess | Interviews and hand-tracing. Findings are only as good as what people still remember. | Mapping, orchestration and connection logic extracted from the source platform, with an AI-generated flow diagram per interface. You plan against what your landscape actually does. |
| Extract & generate | Each interface rebuilt by hand, inheriting whatever shape the old one happened to have. | Target artefacts generated against your templates, with error handling, logging and governance already applied. Often minutes per interface rather than days. |
| Validate | Hand-built test cases, thin coverage, gaps that surface in production. | Automated scenarios run against real captured payloads; any mismatch in mapping, routing or structure is flagged on execution. Up to 70% less manual regression effort. |
| Cut over | Sign-off based on judgement and hope. | Interface-level pass/fail dashboards both IT and the business can read. Go-live backed by evidence. |
| After go-live | Documentation drifts. Every later change is manual rework. | Test cases and templates persist, and later changes run the same AI-assisted path. The migration becomes your operating model, not a one-off project. |
Figures from a recent large-scale migration from legacy middleware to SAP Integration Suite. In a separate programme, a Boomi landscape grown over 15 years, with nobody left who built the original logic, moved with roughly 85% of the work automated.
Prefer to read first? See our automated migration approach or 5 lessons from migrations we have run.
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Your landscape is understood and testable, and the scope is contained. That is the best case for a straight automated run: Migration Studio extracts the mapping, orchestration and connection logic from your source platform, generates the SAP Integration Suite artefacts against your standards, and automated regression testing proves the output before anything reaches production. Realistically one migration wave, or two if you want a deliberate warm-up first.
There is enough volume here that sequencing matters more than raw speed. We would assess first, order the interfaces by genuine complexity rather than by system, then migrate in waves with regression testing closing out each one. Cutover gets its own workstream, owner and rollback plan. In programmes this size, that is where timelines are won or lost, not in the build.
Your constraint is visibility rather than scale. When a landscape is largely undocumented or real payloads are hard to reach, planning without discovery is guesswork dressed up as a project plan. We would start by extracting the actual logic from your source platform and generating a flow diagram of what each interface really does, and by building a reusable payload repository from archives, traces and generated scenarios. The migration plan gets made after that, not before.
Scale plus time pressure is the combination that pushes organisations into committing to a plan before they have evidence it works. We would invert that. Take ten to fifteen of the interfaces your team is most nervous about and run them end to end through extraction, generation and automated regression testing. That turns we think this will work into a tested answer within weeks, and the wave programme then runs against a proven method. It is the standard we hold ourselves to as a partner in SAP's Migration Factory programme.
Tooling, templates and enablement so your own team runs the migration with our method embedded. The regression suite and templates stay with you afterwards, which is what keeps paying off long after go-live.
Your team and ours in one squad. We bring Migration Studio, the automation and the migration method; your people keep the landscape knowledge and the ownership. Knowledge transfer is built into the delivery rather than bolted on at the end.
We run the migration end to end, from assessment through cutover and hypercare, and report progress at interface level throughout. Rojo Managed Services is the natural option for running the landscape afterwards.