How SAP BW Migration Prepares Enterprises for AI

Companies pouring money into AI are seeing only incremental gains, and One Data Chief Executive Officer Dr Andreas Böhm says poor data is a major reason. In a recent episode of the Don't Panic. It's Just Data podcast, Böhm joined the data whisperer Scott Taylor to discuss why SAP Business Warehouse migrations can give enterprises a valuable opportunity to improve the quality and usability of their data.

Böhm, who started his first data company 20 years ago, told host Taylor that the underlying task has not changed through successive waves of big data, data science and machine learning. Organisations still need to get the right data to the algorithms. The algorithms are what have improved. 

Why AI Projects Stall Without Trusted Data

Böhm said enterprises are spending heavily on AI but are not yet seeing the return on investment (ROI). This is especially true when it comes to large jumps in efficiency, cost savings or product development. In his view, the cause is that data is not being used properly. He identified three factors that determine whether AI is adopted in practice.

• The first is trust. If finance controllers do not believe the data a system produces, for example because it does not match their own spreadsheets, they will not act on it. Böhm questioned how an AI agent could be expected to act on data that people themselves distrust.
• The second is consistency over time. Getting data right once is relatively easy, he said, but keeping quality high day to day is a continuing challenge. He compared it to asking his daughter to tidy her room: it looks fine for a day.
• The third is context. Data accurate enough for a forecast, where a five per cent swing may be acceptable, may not be good enough for a profit and loss statement or regulatory reporting to the US Securities and Exchange Commission.

Taylor noted that AI amplifies the consequences of poor-quality data. The familiar principle of “garbage in, garbage out” becomes a much larger enterprise risk when flawed data is processed and scaled across AI systems. As Böhm put it, the result is “scaled garbage.”

Why BW Migration Is an Opening for AI Readiness

Böhm said SAP BW systems hold decades of financial and operational data, along with business logic buried in ABAP code, mappings and calculation views. That logic covers how sales forecasts are calculated and how controlling processes work, and the people who built it have often left the company. Simply lifting and shifting that content to a new platform carries the old problems with it, the two agreed. Taylor said it amounts to building a faster way to repeat the same mistakes.

Böhm recommended instead starting with a landscape analysis and end-to-end lineage mapping to see what is actually used. Typically, he said, about 50 per cent of legacy objects can be discarded, and reducing scope raises quality on its own. His team then uses an explainer agent to document what past logic did, so new staff and AI agents can understand it.

He described a client with 32 SAP systems, a mix of BW and ERP, that wanted to free up capital by understanding how cash flowed across the group. BW was trusted at the level of individual organisations but not company-wide. One Data translated the BW logic into a modern lakehouse architecture, then built a group-wide model on top of it. The migration took months rather than years, Böhm said, and the improvement in managed cash was visible in the company's profit and loss account.

Böhm cautioned that AI is not a shortcut around the work. Pasting ABAP code into a large language model does not capture logic spread across classes, reusable objects, hidden calls and customer exits. What has changed, he said, is that AI agents can now handle tasks such as migrating code, quality checks and fixing syntax errors, which would have been far harder four years ago. He dismissed the idea that LLMs remove the need to clean data. The funding angle matters too. Migration spending is already required, Böhm noted, so it can double as the investment in data management that organisations often struggle to secure.

How to Keep Migrated Data Trusted After Go-Live

Böhm said governance after migration should follow a data product approach, built on several practices:

• Ownership: Someone must understand and be responsible for the data, or it will not stay clean.
• Readability: Business users need understandable column and table names, not cryptic technical labels.
• Business validation: Checks should go beyond technical rules such as "revenue must be a number" to business rules, such as whether negative revenue is valid because of returns.
• Focus: Not everything needs governing. Priorities should be core entities such as customers, revenues, purchase orders and product lifecycles.

Taylor described governance as a lifestyle rather than a project. Böhm added that it remains a people business, in which data owners take pride in their work and promote it internally.

His closing advice to organisations preparing SAP BW data for AI was to use the migration as a chance to clean up the data landscape and to apply the budget to making at least one or two areas AI-ready. If you would like to find out more, visit onedata.ai or follow Dr Andreas Böhm on LinkedIn.

Takeaways

• The recurring hype cycles in AI and the importance of data quality.
• The critical role of trust, context, and governance in AI adoption.
• Transforming legacy SAP BW systems into modern data architectures.
• Strategies for cleaning up and understanding enterprise data landscapes.
• The significance of data ownership, understanding, and management in data governance.