Automation risk reduction in SAP ecosystems

The “Before & After” Data Transformation Story In the lead-up to our SAP migration, we weren’t just preparing systems — we were unearthing years of neglected, inconsistent, and chaotic data. If we are honest, most of the time, it felt less like digital transformation and more like an archaeological excavation. We were buried in layers of spreadsheets, conflicting legacy reports, and systems that hadn’t seen a clean-up in over a decade. Each click revealed more clutter: customer names spelled five different ways, address fields mixing “St.” and “Street” like it was a coin toss, duplicate records stacked on top of each other, and critical fields left blank or filled with guesswork. It was more than just messy — it was risky - A complete nightmare! Data was being pulled from everywhere and nowhere. No single source of truth. No consistency. Just a patchwork of outdated inputs fuelling vital business operations. The worst part? We had to tackle it manually. A Time Sink: Highly skilled people stuck doing low-value, repetitive tasks. An Error Magnet: Fatigue set in. Errors crept through. Fix one issue, uncover two more. A Business Risk: Dirty data meant dirty output. Reports couldn’t be trusted. Customers were misbilled. Orders were sent to the wrong place. And confidence in the system? Gone. We knew we couldn’t carry that baggage into SAP. Something had to change. At this point, we built a purpose-specific solution which was created to automate and streamline data cleansing and validation, giving us the ability to: Proactively identify and rectify errors with precision. Ensure data consistency across all records. Validate information against business rules before migration. This impacts business by: 🔹Reducing Pre-Migration Data cleansing and validation Effort by Up to 75% Freeing up SMEs for strategic tasks, cutting contractor costs, and accelerating migration timelines. 🔹Delivering >99% Accuracy in Key Master Data Minimising migration errors, de-risks go-live, building trust in the new SAP system from day one. 🔹Reducing Migration Delays and Rework by 20–40% Fewer surprises in load cycles and UAT, protecting timelines, budgets, and overall project momentum. 🔹Achieving 100% Data Auditability and Compliance Ensuring full traceability, streamlining audits, and providing a defensible position on data quality from day one. 🔹Reducing Post-Go-Live Errors by 15–30% Fewer issues like misbilling and mis-shipments, leading to smoother operations, faster user adoption, and trusted SAP insights. If any of this sounds familiar, you're not alone. The good news is that we have built a solution which has already helped others through their migration journey, and we’d be happy to share it if it’s useful. Just drop us a message. Created in collaboration with Pawel Lipko ↗️

🔥 𝐎𝐧𝐞 𝐬𝐦𝐚𝐥𝐥 𝐨𝐯𝐞𝐫𝐬𝐢𝐠𝐡𝐭 𝐢𝐧 𝐐𝐀 𝐛𝐫𝐨𝐮𝐠𝐡𝐭 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐢𝐭𝐬 𝐤𝐧𝐞𝐞𝐬. 𝐸𝑣𝑒𝑟 ℎ𝑒𝑎𝑟𝑑 𝑜𝑓 𝑡ℎ𝑎𝑡 𝑠𝑡𝑜𝑟𝑦? 𝐼 𝑙𝑖𝑣𝑒𝑑 𝑖𝑡. 𝐴𝑛𝑑 𝑖𝑡 𝑐ℎ𝑎𝑛𝑔𝑒𝑑 ℎ𝑜𝑤 𝐼 𝑎𝑝𝑝𝑟𝑜𝑎𝑐ℎ 𝐫𝐢𝐬𝐤 𝐢𝐧 𝐜𝐨𝐦𝐩𝐥𝐞𝐱 𝐒𝐀𝐏 𝐥𝐚𝐧𝐝𝐬𝐜𝐚𝐩𝐞𝐬—forever. In a global SAP rollout I led across 9 regions, everything was on track—until a missed dependency in QA slipped into production. 💥 One environment change, multiple system failures, and a 48-hour war room. That day, I realized: 𝐌𝐚𝐧𝐚𝐠𝐢𝐧𝐠 𝐫𝐢𝐬𝐤𝐬 𝐢𝐧 𝐒𝐀𝐏 𝐢𝐬𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐚𝐛𝐨𝐮𝐭 𝐫𝐞𝐚𝐜𝐭𝐢𝐧𝐠—𝐢𝐭'𝐬 𝐚𝐛𝐨𝐮𝐭 𝐚𝐧𝐭𝐢𝐜𝐢𝐩𝐚𝐭𝐢𝐧𝐠. Since then, I’ve developed a playbook for mitigating risks across dev, QA, and prod: ✅ 𝐒𝐞𝐠𝐫𝐞𝐠𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐞𝐧𝐯𝐢𝐫𝐨𝐧𝐦𝐞𝐧𝐭𝐬 isn’t enough—mirror them closely to real usage. ✅ Implement 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐭𝐫𝐚𝐧𝐬𝐩𝐨𝐫𝐭 𝐯𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧 to catch misaligned configs. ✅ Build a 𝐫𝐢𝐬𝐤 𝐦𝐚𝐭𝐫𝐢𝐱 that isn’t static—update it with every sprint. ✅ Involve business users early during 𝐔𝐀𝐓 to validate not just function, but impact. ✅ Set up a 𝐜𝐡𝐚𝐧𝐠𝐞 𝐚𝐝𝐯𝐢𝐬𝐨𝐫𝐲 𝐛𝐨𝐚𝐫𝐝 (𝐂𝐀𝐁) with both IT and business stakeholders. Mitigating SAP risk isn’t about tools alone— It's about 𝒄𝒖𝒍𝒕𝒖𝒓𝒆, 𝒄𝒐𝒎𝒎𝒖𝒏𝒊𝒄𝒂𝒕𝒊𝒐𝒏, 𝒂𝒏𝒅 𝒄𝒐𝒏𝒕𝒊𝒏𝒖𝒐𝒖𝒔 𝒇𝒐𝒓𝒆𝒔𝒊𝒈𝒉𝒕. 𝐇𝐚𝐯𝐞 𝐲𝐨𝐮 𝐞𝐯𝐞𝐫 𝐡𝐚𝐝 𝐚 𝐜𝐥𝐨𝐬𝐞 𝐜𝐚𝐥𝐥 𝐰𝐢𝐭𝐡 𝐫𝐢𝐬𝐤 𝐢𝐧 𝐒𝐀𝐏? Let’s share learnings—so we can build smarter, safer systems together. #SAPRiskManagement #SAPLandscape #SAPBasis #ITGovernance #SAPProjectManagement #DigitalTransformation #EnterpriseIT #SAPSecurity #LinkedInStories

Your last regression cycle took 3 weeks. An AI agent would’ve predicted 60% of the defects on day 1. In large ERP ecosystems, most defects don’t appear out of nowhere. They repeat. They follow patterns. And they can be predicted by identifying risk-prone flows even before test execution begins. Yet in many SAP, Oracle, and Workday environments, regression planning still starts with a static checklist or last cycle’s hit list. One of our manufacturing clients running SAP S/4HANA used to spend 120+ hours each cycle chasing late-cycle defects, many of them in flows that looked “covered.” We helped shift their regression planning to include an AI-led risk predictor. This wasn’t about writing better test scripts. It was about making smarter decisions before testing even began. The agent analyzed 12 quarters of historical defect metadata, mapped it against recent transport deltas and change logs, and flagged volatile flows across finance and procurement before the test suite was even executed. • 4 of the top 6 flagged flows had confirmed defects • SME hours dropped 40% • Cycle time cut from 3 weeks to 9 working days Here’s the shift: Traditional regression asks: “What should we retest?” AI-led regression asks: “Where is risk likely to appear?” Many teams still rely on historical test sets to catch new risks. AI goes further. It surfaces emerging failure patterns regardless of past pass rates. It detects change volatility, config sensitivity, and defect-prone flows based on historical failure triggers — even before execution begins. Before you run your next 1200-script regression, ask: Which 30 flows are carrying 80% of your risk, and can your QA platform surface them on day 1? #SAP #OracleFusion #Workday #TestAutomation #ERPTesting #AIinQA #SOAIS