There’s a reason why the ‘testing pyramid’ works well, in the context of checking software for correctness. The more tests you conduct early in the software development lifecycle, it’s easier to eliminate most bugs from the outset with little or no testing necessary as you get closer to the final build.

However, the benefit of testing early goes beyond just finding bugs: you save a lot of time, money and effort by sorting out issues well before the application has been built in its entirety. Once you reach that phase and discover issues, finding a lasting resolution increases in complexity.

Point being a simple bug fix can set off unintended behavior elsewhere that requires more work to be done, with respect to making architectural and integration changes. As you can imagine, it’s the very reason why regression testing plays a key role, whenever updates or new features are added.

Negotiating the complexity of an S/4HANA migration: Early validation is key

When it comes to an S/4HANA implementation, these issues can get even more complicated as these implementations are rarely greenfield in nature. Apart from the build-config activity that goes into creating functionality for S/4HANA users, a complex web of legacy systems, third-party integrations and custom developments are necessary to keep up with evolving business processes.

Even if human error is inevitable, it’s vital that every “i” must be dotted while every “t” has to be crossed, right from start to finish. Or else, the S/4HANA implementation in question will have architecture gaps, integration failures, data quality issues and security risks that remain undetected just before go-live.

So, how does one ensure that their S/4HANA implementation negotiates such complexities?

For this, early validation, which audits the S/4HANA implementation for functionality and performance long before the first ‘increment’ is built, can play a key role. In addition, AI-powered automation can further speed up test generation and execution while also detecting key issues plaguing the implementation right from the build itself. With such strategies in place, one can ascertain whether the project is moving in the right direction throughout, towards a successful go-live. 

But before we explore what AI-led early validation has on offer, it’s well worth examining how S/4HANA implementations are carried out in the real world. 

The costs associated with performing manual testing late

If this isn’t obvious already, S/4HANA implementations follow the SAP Activate methodology, which has been conceptualized to aid such transformations. Quite interestingly, at the program level, it adopts the Waterfall approach while at the project level, the iterative style of development takes over.

So, it’s no surprise that even though testing is carried out iteratively, it begins during the Realize phase. This is almost halfway through the S/4HANA implementation itself, and which, for all practical purposes, begins after decisions related to the architecture and integrations have already been made and built. Testing your implementation this late can lead to complications for reasons mentioned earlier, which is why it makes sense to shift left. Yes, testing ideally needs to get involved as early as the requirements gathering and design phases. Which, in the case of an S/4HANA implementation, translates to the discovery and blueprinting phases.

As for performing the testing itself, manual testing fails to deliver quality at speed, and which is why the inclusion of both AI and conventional test automation to plan, generate and execute tests is a good move. While there are third-party automation tools available to execute tests, the use of AI can perform test scenario, case and script generation and even deliver these assets to the CI/CD pipeline. Most importantly, the ability to capture real user interactions at a granular level, map business-critical workflows, transactions, reports, and objects can lead to immense savings with respect to costs and time spent on seeing your S/4HANA implementation through to go-live.

In stark contrast, in following the SAP Activate methodology as is, there are costs associated with manual testing carried out during the Realize phase:

  • Rework is more expensive and time-consuming
  • Go-live windows become compressed and chaotic
  • Post-go-live hyper care becomes firefighting instead of fine-tuning
  • Business users lose confidence in the system
  • Regulatory and investment misalignment

To be honest, manual testing performed late doesn’t just cost you more – it amplifies risk across the board. But don’t take our word for it. Let’s look at a few real-world examples that highlight these very costs and how AI-led validation when performed earlier could have saved the day.

The impact of manual testing performed late: Three real-world case studies

As mentioned earlier, S/4HANA implementations are already complex, where there’s every chance of delays and budget overruns. With almost 40% of such implementations finding themselves in such a predicament, it’s fair to say that taking a late, manual testing approach doesn’t do very much. Here are three real-world case studies that support such an argument:

Case 1: The Forgotten Interface

A global manufacturing firm delayed integration testing until the final weeks of their S/4HANA migration. Only then did they discover that a critical interface to their logistics partner was incompatible with the new system. The fix required a custom middleware patch and delayed go-live by six weeks—costing millions in lost productivity. AI-driven interface mapping tools could have flagged the incompatibility during early validation.

Case 2: The Data Migration Surprise

A retail company assumed their legacy data was clean and skipped early data validation. During UAT, they discovered that over 30% of customer records were incomplete or duplicated. The remediation effort pushed their timeline back by two months and required emergency staffing. AI-based data profiling could have identified incomplete or duplicated records before UAT.

Case 3: The Role Explosion

An energy provider underestimated the complexity of role-based access in their new S/4HANA environment. Late-stage testing revealed that key users couldn’t access critical transactions. The resulting scramble to redesign roles delayed training and go-live readiness. AI could have simulated user access scenarios to ensure role configurations that meet business needs.

As you can tell, harnessing the intelligence, safety and speed that AI-powered validation offers as early as possible in the development lifecycle amounts to a lot.

Four unmistakable benefits associated with early testing

Now, it’s not enough to consider AI-powered validation for your S/4HANA implementation. Working it in right from the beginning of the SAP Activate lifecycle matters just much.  

So, here are four reasons why you should take early testing seriously:

1. Architecture Gaps 

Misaligned system landscapes, missing components, or incompatible versions can be identified before they derail the build. AI tools can proactively analyze system landscapes to detect misalignments and suggest optimal configurations. 

 2. Integration Failures 

APIs, middleware, and data flows can be validated early to ensure seamless communication between systems. AI-powered integration testing can simulate data flows and identify potential API mismatches early. 

3. Data Quality Issues 

Early test cycles can expose inconsistencies, duplicates, or missing data that would otherwise corrupt downstream processes. Machine learning algorithms can automatically detect anomalies, duplicates, and missing data in large datasets. 

4. Security & Access Risks 

Role-based access and authorizations can be tested before users encounter roadblocks in UAT or production. AI can evaluate role-based access patterns and flag unusual permission configurations that may pose risks.

AI-driven early testing for your S/4HANA implementation: Final thoughts

Resilience in S/4HANA migrations isn’t about avoiding failure. Instead, it’s about failing early, learning fast, and recovering cheaply. By embedding AI-powered testing from the start, organizations can uncover hidden risks, improve data quality, avoid costly surprises, and strengthen system resilience. Most of all, you get to build a transformation program that’s not only successful but sustainable.

So, what would you prefer? Your teams failing fast in a safe environment? Or during critical go-live windows?