Inside SAP Discovery Center’s Quiet Role in Enterprise Adoption

Most of the AI conversation in the SAP ecosystem happens at the loud end of the spectrum, such as 200-plus agents unveiled at Sapphire, Joule promoted from assistant to orchestrator, and an Anthropic partnership wiring Claude into the Business AI platform. SAP’s latest customer story points to something quieter and arguably more consequential. Where do enterprises go to figure out what to build first?

That place is the SAP Discovery Center, a free self-service portal where customers explore SAP Business AI use cases, reference architectures, cost and ROI estimators, maturity assessments, and step-by-step missions. There are nearly 400 features and agents in the SAP Business AI Catalog alone. At SAP Sapphire Orlando, two customers, Agilent and Sutherland, explained how the site shapes their approach to transformation. Their stories matter because they expose how AI adoption really begins, and why a starting point is a strategic asset, not a convenience.

Agilent: Borrowing a Pattern

Agilent, a global leader in life sciences, diagnostics, and applied chemical markets, faced the familiar enterprise problem of manual analysis and late detection of tariff and compliance changes. Its chief enterprise architect, Manthan Peshne, framed the team’s operating philosophy bluntly. “We have one core principle: do not solve the problem that has already been solved.” That single sentence reframes the Discovery Center from a catalog into a reuse engine.

What Agilent did next is the part that SAP architects should study. The team found a mission built for the oil and gas industry, an entirely unrelated vertical, and used it to explore how an AI agent could interpret unstructured regulatory signals, extract tariff context, judge relevance, and convert fragmented information into actionable alerts. The mission accelerated design thinking and development. More importantly, Agilent walked away with what Peshne called “an enterprise pattern,” a reusable platform for any scenario where external signals must be captured and placed in the context of Agilent’s own data. That is the difference between buying a feature and building a capability.

Sutherland: Closing The Cold-Start Gap

If Agilent shows the architecture value, Sutherland, an AI-driven business transformation company, shows the economics. SAP enterprise architect Amar Busireddy described the Discovery Center as a way to skip the painful early phase of a project. “Instead of starting an MVP to see what the problem is and where to start, we have a ready-made solution from which we can pick up,” he said. “It gives us a start somewhere around 20%-30% depending on the scenario.”

This is not trivial in consulting, where time-to-value is the product. Sutherland uses missions to educate consultants and to implement for clients, leaning heavily on Joule missions that integrate with SAP SuccessFactors, SAP Ariba, and SAP S/4HANA. Busireddy was candid about the limits, too. He noted that reference architectures “might not fit our requirements 100%,” but they give direction on what to use and what not to use. This is enough to plan the budget and time.

Why This Matters Now

The honesty in both accounts is what makes them useful. SAP Discovery Center isn’t sold as a finished answer; it is a way to compress the riskiest, most expensive part of any AI program, the part before anyone knows whether the idea is even viable. That lands directly on the ecosystem’s central tension. SAPinsider’s own coverage shows AI is everywhere and scaled almost nowhere. The Stanford AI Index 2026 analysis found 88% of organizations now use AI in at least one function, while agentic deployment sits in the single digits, and SAP CIOs continue to describe AI as pilot-stage, gated by data quality, governance, and cost. Tools that de-risk the first step are exactly what move organizations from attention to execution.

This is also consistent with SAP’s broader Sapphire posture, in which the Autonomous Enterprise depends on customers finding and adopting agents, not just on SAP shipping them. Discovery Center is the on-ramp. SAPinsider’s research on AI adoption and maturity in the SAP ecosystem and its long-running coverage of Discovery Center missions and reference architectures both point to the same conclusion: the differentiator is no longer access to AI but the disciplined selection of where to apply it.

What This Means for SAPinsiders

Treat the Discovery Center as a reuse engine, not a brochure. Agilent’s win came from lifting a mission out of oil and gas and bending it to a tariff-compliance problem. Enterprise architects should audit the Business AI Catalog and mission library for patterns, not just products, and explicitly ask “has this class of problem already been solved somewhere in the catalog?” before commissioning net-new development. The payoff is an enterprise pattern you can replicate across use cases, not a one-off feature.

Use the 20-to-30% head start to fund governance. Sutherland’s advantage is a faster start, but the recurring barrier across SAPinsider’s CIO and Stanford coverage is governance, validation, and trust, not raw capability. Reinvest the time the SAP Discovery Center saves into harder work, clean-core data readiness, human-in-the-loop controls, and ROI measurement, so pilots can graduate to production rather than stalling.

Make “fit, not 100%” an explicit design decision. Busireddy’s framing, that reference architectures guide what to use and what to avoid, is the right mental model. Practitioners should treat SAP Discovery Center outputs as directional blueprints and deliberately document where they diverge and why. That discipline turns the cost and ROI estimators into real budget inputs and gives program managers a defensible plan rather than an optimistic guess.