A place for enterprise, solution, data, AI and cloud architects to structure the trade offs behind a decision, and explain them in language the business already speaks.
How architects turn a platform decision into something a CFO can read in one page.
A few illustrative patterns, each answering the questions that actually matter for the decision.
SAP data stays governed and trustworthy while AI teams keep the flexibility a purely SAP-native platform doesn't offer.
Two platforms means two commercial relationships to manage, but each is scoped to what it's actually good at, rather than paying for capability you don't use.
If AI ambitions stay modest, a single SAP-native platform may cover the need without Databricks at all.
Many enterprises have significant non-SAP data that still needs the same governance discipline as the SAP core.
Avoids paying twice for governance capability that both platforms already provide, if the data sharing boundary is designed well.
If most valuable data is SAP-native, the case for a second governed platform weakens considerably.
Years of custom ABAP code inside the SAP core make every upgrade slower and riskier, and block a path to SAP's cloud roadmap.
Slower, more expensive upgrades are a real recurring cost this pattern reduces, but the migration of existing customizations is itself a project to budget for.
If an upcoming SAP upgrade or cloud migration isn't on the roadmap, the urgency of adopting this pattern now is lower.
AI pilots multiply across departments on inconsistent platforms, making governance, reuse and scaling nearly impossible.
The real cost comparison isn't this architecture against nothing, it's this architecture against N independent AI stacks that don't share investment.
If AI activity is genuinely limited to one team with no near-term plan to expand, a shared foundation may be premature.
What actually changes when a modernization like this happens, in architecture and in business terms.
Trades a web of point-to-point extracts for a smaller number of governed data products with clear ownership — fewer integration points to maintain, but each one now carries more responsibility.
Executive reporting stops requiring manual reconciliation between teams' numbers, and new analytics use cases (including AI) can build on data that is already governed instead of starting from scratch.
Illustrative Architecture Decision Records, each translated into plain business language.
This decision trades near-term simplicity against how fast the company can act on AI in two to three years.
This is a trade between negotiating leverage and operating cost, not a technology preference.
This decision is about paying down complexity now versus paying it back later, with interest.
The real question for the business is whether this capability is where the company wins, or where it just needs to keep up.
Every Architecture Decision Record here runs on the same Donna engine your team would use.
Start with Donna