Fünf illustrative Enterprise-Entscheidungen, vollständig durchgearbeitet. Keine echten Kundendaten, aber genau dieselbe Methode.
Modernize SAP BW into SAP Business Data Cloud, Databricks, Microsoft Fabric, or a hybrid architecture?
A global manufacturer runs SAP BW on HANA alongside a patchwork of point to point extracts feeding regional reporting. Reporting is slow to change, data is duplicated across three systems, and AI initiatives have no governed foundation to build on.
Strongest SAP-native continuity, governed foundation ships largely pre-built
Strongest AI and data science flexibility, more integration work upfront
Best fit if Power BI and Microsoft 365 are already central to reporting
SAP-native core with open compute for advanced AI workloads
The governed SAP-native foundation directly resolves the duplication problem with the least new integration work, while a companion AI platform avoids boxing in future data science ambitions.
This removes a structural blocker to every AI initiative that depends on trustworthy company-wide data.
Should AI run on a single central platform, a federated model, or a hybrid?
A financial services firm has six AI pilots running on three different platforms, none of them production-grade. Leadership wants one enterprise AI strategy instead of pilots multiplying independently.
Strongest governance, slower for teams wanting to experiment quickly
Fastest for individual teams, weakest for regulated use cases
Balances regulatory need with team-level speed
Regulated use cases (credit, fraud) need centralized auditability that a fully federated model can't provide, but forcing every low-risk experiment through the same central process would slow the exact speed leadership also wants.
One strategy instead of six independent pilots is what turns AI spend into AI results.
Which platform best fits governed analytics plus growing AI ambitions?
A retailer needs one analytics and AI platform for its next five years, replacing a legacy on-premise warehouse.
Strongest for AI and machine learning maturity, more setup complexity
Strongest for governed, predictable analytics; AI capability growing but younger
Fastest time to value if Microsoft is already the default stack
AI and machine learning maturity was the deciding priority, and the retailer's roadmap leans heavily on demand forecasting and personalization use cases that benefit most from that maturity.
This platform choice determines how fast AI-driven personalization can actually ship.
Build a custom AI capability, or buy a specialized platform?
A logistics company needs a customer service AI capability and is deciding between building on foundation models directly or buying a specialized vendor platform.
Maximum differentiation and control, slowest to a production capability
Fastest to production, less differentiation versus competitors using the same platform
Customer service AI is not this company's core differentiator. Buying gets a working capability to market fastest, and the build option can be reconsidered later if the use case proves valuable enough to justify the investment.
Fastest path to a working capability, with the option to invest further once value is proven.
Should data infrastructure become fully centralized, stay federated, or move to a data mesh model?
A multi-division industrial group has five business units, each historically running its own data infrastructure with little consistency.
Strongest consistency and cost efficiency, slowest for divisions with urgent local needs
Fastest locally, weakest consistency and highest total group cost
Balances division autonomy with group-level consistency
The divisions have genuinely different data needs that a fully centralized model would slow down, but the current fully federated approach has produced real duplication and inconsistency. A shared-standards model addresses both.
This resolves years of division-level inconsistency without forcing a slow, centralized rebuild.
Stay single hyperscaler, or deliberately introduce a second cloud provider?
A pharmaceutical company runs nearly everything on one hyperscaler today. A new regulatory requirement raises the question of whether that concentration is now a liability.
Lowest operational complexity, highest concentration risk
Targeted risk reduction without full multi-cloud overhead
Lowest concentration risk, highest ongoing operational cost
A full multi-cloud architecture would address a risk that, on closer inspection, only applies to a narrow set of regulated workloads. Scoping the second provider to those workloads gets the regulatory benefit without paying for full multi-cloud complexity everywhere.
This addresses the regulatory exposure directly instead of over-rotating into full multi-cloud complexity.
Which AI use cases should be funded first?
A telecommunications company has twelve candidate AI use cases proposed across departments, and a budget that realistically covers three to start.
High value, data already largely ready, moderate effort
High value, but data readiness is the weakest of the shortlist
Fast time to value, moderate business value
High value, high effort, longer time to first result
The two funded first both combine real business value with data that's actually ready today, which is what turns a use case into a working result instead of another stalled pilot. The other two are worth doing, but need more groundwork first.
Two working AI results beat four AI pilots that stall for lack of ready data.
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