For the vast majority of use cases, yes — SAC stories and the Data Analyzer cover interactive dashboards, formatted reporting and ad-hoc analysis. SAC is SAP's strategic analytics platform and receives quarterly feature releases; BEx and BusinessObjects are in maintenance mode. That said, a complete migration is a programme, not a project: organisations typically have hundreds of BEx queries and WebI documents, of which 30–50% are unused or duplicated. Our migration approach starts with an inventory and usage analysis — retiring dead content, consolidating duplicates and migrating what remains in priority order. Highly formatted operational reports (pixel-perfect invoices, statutory formats) may warrant Crystal Reports retention or an alternative approach, which we flag during the assessment rather than discovering mid-migration.

SAC planning is designed for exactly this. Planning models support versions (Plan, Budget, Forecast, Actual), driver-based calculations, allocations, currency translation and calendar-driven approval workflow — the full structure of an enterprise budgeting cycle, with the collaboration and audit trail Excel cannot provide. For SAP BPC customers: SAC planning is the strategic successor, and SAP provides integration paths for coexistence during transition. Data can retract from SAC plans back into S/4HANA for actual-vs-plan reporting. The realistic migration path from Excel is phased: start with one planning process (typically opex or headcount planning), prove the workflow, then extend to revenue and full P&L planning. We structure planning engagements this way deliberately — big-bang planning replacements have a poor industry success record.

No — that is the point of Smart Predict. It automates the machine learning pipeline (variable selection, model training, validation) behind three business-oriented scenario types: classification (will this customer churn?), regression (what will this deal close at?) and time-series forecasting (what will demand be next quarter?). A business analyst defines the question and the historical data; Smart Predict builds and explains the model, including which variables drive the prediction. Automatic forecasting is even simpler — a toggle on any time-series chart that projects forward with confidence intervals. What organisations do need is good historical data and someone who understands the business question — which is where our enablement approach focuses: we configure the first scenarios with your team, so your analysts learn the discipline of framing predictive questions correctly.

SAC is a core component of SAP Business Data Cloud — BDC bundles Datasphere, SAC, Databricks and Object Store into a single subscription, and going forward SAP will not sell SAC and Datasphere separately. If your organisation adopts BDC, SAC is included and your existing SAC content carries forward. BDC's Insight Apps are themselves pre-built SAC applications running on BDC data products. For organisations not yet on BDC, SAC continues to work exactly as before with existing subscriptions and connections. Practically: nothing about a SAC investment today is wasted in a future BDC adoption — the stories, models and skills all transfer. Our SAC engagements are designed BDC-forward: we architect connections and models in the pattern that maps cleanly onto BDC data products, so a future transition is a licensing change, not a rebuild.

In our experience, the overwhelming majority of SAC performance problems are design problems, not platform limitations — and they are fixable. The common causes: stories with too many widgets querying on open, BW queries returning excessive cell counts to live connections, missing filters forcing full data scans, over-complex calculated measures evaluated at render time and un-optimised CDS views behind S/4HANA live connections. Our SAC performance assessment profiles the story, the connection and the underlying query to identify exactly where time is spent — then remediates in order of impact: query redesign, story restructuring (splitting overloaded stories, lazy-loading tabs), filter strategy and, where relevant, data model changes. Typical outcomes bring multi-minute story loads down to seconds. This is offered as a standalone optimisation engagement for organisations with existing SAC deployments.