Building the Business Case for S/4HANA Migration Before the 2027 Deadline

Building the Business Case for S/4HANA
Migration Before the 2027 Deadline

SAP's mainstream maintenance for ECC ends December 31, 2027, and the business
case that justifies an S/4HANA migration needs to hold up long after the budget
meeting that approved it. Most get built early, on thin information, and never revisited,
a habit that gets riskier with every month left before the deadline closes off options,
AI-assisted modeling included.

SAP's mainstream maintenance for ECC ends December 31, 2027. Past that date, organizations that
haven't moved lose more than a support contract: the third-party consultants, staffing pools, and
specialist vendors built around ECC-era SAP also thin out, as the installed base shifts toward S/4HANA
and the people who know ECC best move with it. That combination, shrinking support and a shrinking
bench of people who can help, is what turns migration from a project some organizations might
eventually get to into one with a real deadline attached.

Under that deadline, the business case that justifies a migration deserves more scrutiny than it usually
gets. Most are built before anyone has a precise view of what the project involves: the data profiling
hasn't run yet, the custom code assessment hasn't scoped the technical debt, and fit-gap analysis hasn't
confirmed which simplification items even apply. Instead the business case gets assembled from
industry benchmarks and comparisons to similar projects, submitted for approval, and then left alone
until the project wraps and someone checks whether it landed where it was supposed to. That's a
reasonable way to get budget approved. It's a poor way to manage a decision with a hard deadline
behind it, and AI-assisted modeling is one of the more useful tools for closing that gap, letting the
business case update as real project data comes in instead of standing on the original guess.

Reported S/4HANA migration costs span an enormous range. CIO.com puts it at roughly $2 million on
the low end to more than $1 billion for the largest, most complex global enterprises. That spread doesn't
mean reliable estimation is impossible. It reflects how much migration cost is actually driven by variables
specific to each organization: how much custom code exists, how clean the underlying data is, how
many simplification items apply, and which transformation approach (greenfield, brownfield, or selective)
the organization ends up choosing.

The traditional business case handles this variability with a single-point estimate, derived from an
industry benchmark or a peer comparison and adjusted by a contingency percentage that's usually more
a matter of negotiation than analysis. S/4HANA transformations exceed budget in practice 65% of the
time, according to Horváth's 2025 study. An approach to contingency planning that ignores odds like
that deserves more scrutiny than it usually gets.

A single-point estimate, set once at the outset and never revisited, is the weakest part of most migration
business cases. It gets treated as fact by the time it reaches a steering committee, when it was really a
rough calibration against benchmarks and comparable projects, adjusted by a contingency percentage
that owes more to negotiation than analysis. The number doesn't get worse as the project moves
forward. What gets worse is the gap between that number and what discovery actually turns up, a gap
nobody is tracking because nobody planned to revisit the estimate in the first place.

The fix is procedural, not technological: build the estimate to update on a cadence, as data profiling,
code assessment, and fit-gap results come in during discovery, rather than defending the original figure
until the project closes. AI-assisted modeling is one practical way to do that. Models trained on
comparable prior migrations can generate a probabilistic range instead of a single number, and re-run
that range against real findings as they arrive, giving a steering committee an early, evidence-based
signal when the business case that justified the investment is drifting away from what's actually being
found on the ground. The discipline of revisiting the estimate matters more than which tool does the
recalculating.

A complete migration business case should also name AI-assisted tooling as its own line item, cost and
benefit both, rather than letting it hide inside broader delivery estimates. That means licensing for
AI-assisted tooling and copilots, benchmarked productivity gains such as the sprint acceleration KPMG
has measured and the delivery-timeline reduction EY has reported from Joule-assisted development,
and the ongoing cost of the governance function needed to keep that tooling delivering safely.

That governance line matters because the gains aren't automatic. Gartner's research on agentic AI
projects is a useful caution here: a large share are projected to be cancelled well before 2027 over
unclear ROI and weak governance. A business case that counts on AI-driven productivity without
budgeting for the governance that makes those gains reliable is making the same mistake as an
unrevised single-point estimate, just with newer numbers attached to it.

is the realistic range for S/4HANA migration cost, driven almost entirely
by organization-specific variables. That's exactly why a probabilistic,
evidence-updated model outperforms a single industry benchmark.
(CIO.com)

Getting the business case right, and keeping it right as real project data comes in, is the difference
between a migration that clears budget approval and one that actually lands before December 31, 2027.
Orpington Technologies brings deep S/4HANA migration experience to both delivery engagements and
independent business case review, using data profiling, custom code assessment, and fit-gap analysis
to replace guesswork with evidence. AI-assisted delivery tooling is one of several capabilities Orpington
brings to a migration, alongside functional and technical expertise built across ECC-era SAP
landscapes. Learn more about Orpington's approach at orpingtontech.com.