Concept card

AI use-case discovery and prioritisation for SAP clients

As of 2026-11-07

What is AI use-case discovery and prioritisation for SAP clients?

SAP already catalogues and pre-values over 240 AI use cases on Discovery Center, organized by business area with benchmarked value ranges — a discovery workshop that starts from a blank whiteboard is re-deriving work SAP has already done. The consultant's job is to filter that catalog against the client's actual data readiness and risk classification, not to invent a use-case list from scratch.

Start from the catalog, not the whiteboard

The most common mistake in an AI use-case discovery workshop is treating it as a blank-page brainstorm. SAP has already done a large part of that work publicly: the SAP Business AI catalog on Discovery Center documents descriptions for more than 240 AI use cases, categorized by business area, including Joule capabilities and Joule Agents, each with business benefits and an estimated business value. The catalog is a living resource, not a one-time PDF — SAP reports it has been visited heavily by customers and partners since its launch, with users returning regularly to research features, agents and related assets. For a consultant, this changes the shape of a discovery workshop: instead of asking "what could AI do for you" as an open question, the opening move is to filter the catalog against the client's own line-of-business priorities and bring 15-20 pre-qualified candidates into the room, each already carrying a documented value range and a link to the underlying SAP feature or agent.

Why it matters

SAP's own catalog already values 240+ use cases with a documented methodology (efficiency, direct impact, effectiveness) — skipping it and starting from a blank whiteboard wastes workshop time re-deriving what SAP has already benchmarked.
The catalog's value figures are benchmark ranges, not client-specific commitments — quoting them unadjusted in a proposal is the same error “The SAP AI business case and value tracking” flags for business cases that never get client-specific validation.
Data readiness and risk classification are the two axes the catalog cannot supply — without them, prioritization by value alone produces a backlog of strategic bets disguised as quick wins.

Key points

SAP Business AI catalog on Discovery Center: 240+ documented use cases (count published August 2025), categorized by business area, including Joule capabilities and Joule Agents.
Value methodology: industry expertise, benchmarking data, third-party research and early customer feedback, viewed through efficiency, direct impact and effectiveness.
The catalog is a living, updated resource — not a fixed one-time document; SAP reports heavy repeat use by customers and partners since launch.
Catalog value = benchmark range, not a client-specific number — must be validated against the client's own data volume and process maturity.
Two client-specific prioritization axes the catalog does not supply: data readiness and risk classification (the same red-line/high-risk/standard triage as “SAP's AI ethics policy in delivery”).
Four honest backlog categories: quick wins, strategic bets (fund data work first), governance-gated (route through Ideation classification), deprioritize.
Two-session workshop design: session one filters the catalog for breadth with business stakeholders; session two scores readiness and risk with data/compliance owners.
Fewer than five quick wins after both sessions is usually a signal to widen the catalog filtering, not a verdict that the client lacks opportunities.

Terms used on this page

SAP Business AI catalog: SAP's Discovery Center resource documenting 240+ AI use cases by business area, each with a benchmarked value estimate.
Value estimator: SAP's tool, alongside the catalog, for producing business-value statements per use case for customers and partners.
Quick win: A prioritized use case combining high catalog value, high data readiness and low risk classification — build first.
Strategic bet: A high-value use case blocked by low data readiness — needs a funded data-preparation phase before it can be built.
Governance-gated use case: A high-value candidate that scores high risk or red line on the Ideation classification — routed to review before commitment.

Sources

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Edited by Cédric Mary · How this page is made

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