How Resolve Tech Solutions’ Juno Labs Builds Production AI Extensions on SAP BTP

Resolve Tech Solutions, the Addison, Texas-based SAP services firm, recently published a technical explainer detailing how its AI division, Juno Labs, builds production-ready AI extensions on SAP Business Technology Platform (BTP), sitting atop SAP S/4HANA. The article arrives as the SAP ecosystem accelerates AI investment but struggles to move from limited deployments into core workflows. For enterprise teams stuck in that gap, the Juno Labs approach offers a concrete model for what production AI on SAP actually requires.

The explainer builds on a Resolve Tech Solutions blog post that focused on SAP BTP readiness and SAP’s AI copilot, Joule, ahead of SAP Sapphire 2026. Taken together, both articles make the same argument: before Joule or any custom AI extension delivers value, the SAP BTP foundation must be correctly configured. Building and managing that foundation is exactly what Juno Labs does.

What Juno Labs Ships on SAP BTP

Juno Labs emerged from Resolve Tech Solutions’ work managing one of the largest SAP environments on AWS, over 6,000 virtual machines serving Fortune 500 clients in energy, manufacturing, and industrial sectors. That scale means its AI extensions run against real production constraints.

The explainer identifies four extension categories in production on SAP BTP:

• ML-powered vendor scoring analyzes SAP MM purchasing history and delivery performance to recommend preferred suppliers for MRP-triggered requisitions.
• Predictive maintenance extensions consume sensor data from SAP IoT or third-party OT systems and write condition-based notifications into SAP S/4HANA Plant Maintenance, reducing unplanned downtime by 15% to 30%.
• LLM-powered document processing handles invoice classification through Integration Suite, cutting manual keying in accounts payable by 60% to 80%.
• Natural language ERP query extensions, built on SAP’s Generative AI Hub and connected to SAP S/4HANA CDS views, let users query operational data without knowing table structures.

Juno Labs distinguishes between SAP’s embedded AI (on SAP BTP’s AI Foundation layer, included in many SAP S/4HANA licenses) and custom models on SAP AI Core. The embedded layer covers intelligent invoice matching and demand forecasting. AI Core handles proprietary use cases such as a refinery’s specific equipment failure mode, or a procurement team’s negotiated supplier terms that override standard MRP logic.

Why The SAP BTP Foundation Is The Bottleneck

Resolve Tech’s explainer lands at a precise inflection point. According to SAPinsider’s Technology Leaders’ Strategic Agenda 2026, SAP BTP leads planned investment at 48%, with embedded AI and ML at 33%. Yet only 16% of those organizations use AI beyond limited deployments. SAPinsider’s AI Adoption and Maturity 2025 confirms the pattern: 91% of SAP organizations use AI at some level, but only 17% have embedded it in core workflows.

The disconnect traces back to infrastructure. Resolve Tech notes that Joule requires an SAP BTP subaccount with Cloud Foundry runtime, a consolidated SAP Cloud Identity Services tenant, and SAP Build Work Zone. Identity consolidation alone takes three to six months when multiple SAP instances are involved. The SAPinsider ERP Migration and Transformation 2026 report shows that 40% of organizations plan to build an AI Foundation via SAP BTP, with Joule adoption plans up more than 40% year over year. Organizations moving fastest treat SAP BTP configuration as an infrastructure investment rather than a project overhead.

What This Means for SAPinsiders

Sequence the migration before the AI, and use Juno Aura to do it cleanly. CIOs and ERP program managers running parallel ECC-to-SAP S/4HANA and AI workstreams risk compounding the issues in both. Juno Aura, Resolve Tech’s migration product, is designed to bridge that sequencing gap. With 40% of organizations planning an AI Foundation via SAP BTP, the migration must exist on an SAP BTP-ready SAP S/4HANA platform, or the AI investment has no foundation to run on.

Treat SAP BTP readiness as the first AI project. Enterprise architects evaluating Joule or custom SAP BTP extensions in 2026 need to resolve the infrastructure stack before committing to agent timelines. With only 16% of technology leaders deploying AI beyond limited use, the organizations pulling ahead treated SAP BTP subaccount configuration, identity consolidation, and Work Zone setup as distinct, time-boxed projects. Activating embedded AI takes four to eight weeks; a custom AI Core extension takes 12 to 20 weeks. Neither starts without the foundation.

Use Juno Halo to run agentic AI inside AMS, where operational data already exists. SAP development leads and AI leaders managing Application Management Services have direct access to the production signals that drive the most valuable AI use cases. Juno Halo, Resolve Tech’s agentic AI offering within SAP AMS, converts those signals into automated decisions. The documented outcomes from Juno Labs, 15% to 30% downtime reduction and 60% to 80% reduction in manual document processing, set the benchmark for what agentic AI in AMS should deliver in 2026.