This partner insight was authored by Brian Rogan, Growth Leader, U.S. at LG CNS PerfecTwin.

ECC mainstream maintenance ends in 2027, and the customers running it can’t address that deadline in isolation. Migrating to S/4HANA is the obvious move, but two other shifts come with it: quarterly upgrades become a permanent feature of life under Cloud ERP, and Clean Core requires that customizations move off the core system and onto SAP BTP. Each of those changes invalidates something testing organizations spent the last ten years building around.

Most of the testing playbooks in use today were designed for an environment SAP is leaving behind. UI-based replay tools, which carried the previous wave of SAP test automation, work by recording what a user does on screen and replaying it later. They depend on the screen looking the same the next time. Fiori doesn’t cooperate; quarterly updates routinely shift things around, and scripts that worked last quarter need to be repaired this one.

When the team’s time is going into fixing what used to work, new coverage just doesn’t get built. The data side of the problem is different, but adds up to the same thing. If your test data is something the QA team designed rather than something pulled out of the production system, you’re testing what people thought customers do, not what customers actually do.

The first month-end close after go-live is usually where this catches up with you. Special discounts that nobody flagged, foreign-currency revaluations on transactions the test scripts never saw, tax rules from a country that wasn’t in the test scope. These show up the same week, and most of them weren’t in any pre-go-live regression run.

These two issues compound each other. Time spent repairing scripts reduces resources for new coverage. Missed exceptions extend Hypercare beyond its planned duration, redirecting budgets meant for future automation. By the next quarterly release, teams often stop using testing tools, dashboards become outdated, and regression is tracked manually in spreadsheets.

What AI Doesn’t Fix

The vendors haven’t ignored these problems, and most of the major testing platforms have added some kind of AI feature aimed at one or another of them. Self-healing is the most visible: when a UI element moves, the tool tries to figure out where it went and rewires the broken script accordingly.

There’s also a category of features around generation—describe a scenario in plain language, get back something resembling a test—that vendors are pitching under the agentic-AI banner. And on the analysis side, some tools now use AI to predict which parts of a system a change will touch, which lets teams scope regression more narrowly than running everything every time.

The features are useful in their own right, but they all run into the same wall when the underlying tool is UI-based. SAP’s UI is also its most volatile layer. Anchoring automation to a layer that changes constantly is a structural problem no AI feature can solve, because the AI is repairing damage the architecture didn’t have to produce in the first place.

A Different Architecture

PerfecTwin, the SAP testing solution from LG CNS, takes the opposite approach. Rather than recording user interactions and replaying them through a browser, it sends test inputs straight to the SAP application layer, where calculations, data flows, and business rules are decided. From that architectural choice, four reinforcing capabilities follow.

1. Native SAP design. PerfecTwin treats table structures, SAP system messages, business objects, and T-Codes as first-class elements of the testing model rather than as fields and buttons to be located on a screen. Scenarios assemble along the contours of how SAP actually works, not along the contours of how SAP is rendered.
2. Backend execution is what produces the speed advantage. LG CNS reports execution speeds up to 50 times faster than UI-based alternatives, which matters when thousands of regression tests need to finish inside an upgrade window.
3. Real production data, drawn straight from the operational database through a Data Extractor. Users specify the business area and time window they want covered and receive the matching set of live transactions back as test inputs. The edge cases that simplified test data never produce—the multi-currency settlements, the unusual discount structures, the regulatory variations of a global footprint—show up because they’re already in the data. The distance between test and production environments narrows because the test data is the production data.
4. No-code scenario assembly. PerfecTwin’s flow-diagram interface lets functional consultants and business users build scenarios by snapping reusable units together rather than writing scripts, drawing on a library of pre-built templates for standard SAP processes. Onboarding takes days rather than months, which means automation stops being the dominion of a single specialist whose departure leaves the entire program stranded.

These four capabilities reinforce each other in a way that the individual features don’t fully convey. Backend execution is what keeps the no-code library from decaying through Fiori updates, and real production data is what makes the speed advantage actionable.

Three Outcomes Across the Lifecycle

Three customer cases show the system at work across the three lifecycle stages where SAP testing has historically broken down.

The migration case is the most demanding of the three, and it’s where LG CNS has its strongest reference: a global manufacturer that ran end-to-end validation against more than 10,000 scenarios across over 50 transaction codes during its ECC-to-S/4HANA conversion, including full coverage of cross-entity transactions and global system integration touchpoints. That customer went live with zero day-one defects and cut its verification time by 40%, an outcome that wasn’t going to come out of sample-data testing because the relevant edge cases weren’t in the samples.

The other two stages of the lifecycle look different in their particulars. A financial institution running quarterly releases used the tool to consolidate its Groupware-to-SAP workflow into one end-to-end test, then ran that test against actual ECC data ahead of cutover. The team caught RFC integration errors before they reached production, and the institution’s upgrade verification time came down 70%. A separate engagement, this one with a global chemical company, replaced an entrenched manual regression routine with an automated regression program weighted by process frequency, business risk, and ROI. Operational incidents were down 80%.

LG CNS is bringing the AI-enabled cloud version of PerfecTwin to SAP Sapphire 2026, and the framing matters as much as the features. AI is what every testing vendor is racing to add right now, but its leverage depends on what it’s applied to. Layered onto UI replay, it’s a sophisticated patch on a fragile foundation. Built into a backend-direct, SAP-native, production-data-driven architecture, it extends a structure that already addressed the underlying problem.

The 2027 deadline is what’s forcing the question. The customers who answer it by looking at where their testing actually runs, not just which tool runs it, give themselves an architecture that holds up after migration ends.

Brian Rogan is Growth Leader, U.S. at LG CNS PerfecTwin.