Aaron Johns on How Siemens Government Technologies Is Advancing AI for Federal Missions
Aaron Johns, vice president of digital solutions at Siemens Government Technologies (SGT), laid out a direct path for moving AI from experiments to mission outcomes. His message to agencies is simple: AI at scale demands a trusted ecosystem, not just models.
"As federal agencies strive to modernize critical infrastructure, improve operational agility, and manage growing volumes of data, AI has become essential. But implementing AI at scale requires more than just algorithms," Johns said. "It takes an ecosystem designed for performance and trust. That's exactly what we've built at SGT."
What this means for federal missions
- Faster, better decisions by unifying engineering, operational and sensor data.
- Lower risk through model-based systems engineering (MBSE) and AI-augmented digital twins.
- Higher availability with predictive maintenance and scenario simulation.
Inside the SGT tech stack
Siemens Xcelerator as the data backbone
SGT's offering runs on Siemens Xcelerator, which unifies engineering and operational data to eliminate silos and deliver real-time insight. Agencies gain data clarity, lineage and visibility across assets and processes.
Learn more about Siemens Xcelerator.
AI + digital twins + MBSE
SGT integrates AI with digital twins and MBSE to simulate scenarios, predict maintenance and support evidence-backed decisions. AI-augmented twins help maximize asset utilization and improve field performance, with measurable results in uptime, safety and cost.
Tools that speed adoption
RapidMiner, from Altair, and Mendix help agencies stand up use cases quickly and scale what works. RapidMiner delivers insights from unstructured data and advanced scenario modeling for predictive maintenance-already used across the Department of Defense to anticipate parts failures and cut unplanned downtime.
Mendix enables rapid prototyping and deployment of mission-specific tools while maintaining security requirements and integration with existing systems.
Where to apply first
- Maintenance and sustainment: parts failure prediction, optimized schedules, and spares planning.
- Facility operations: energy optimization, anomaly detection, and readiness monitoring.
- Test and training ranges: scenario simulation, telemetry analysis and safety assurance.
- Supply chain and logistics: demand forecasting, routing, and inventory visibility.
Trust, security and oversight
SGT's approach focuses on verifiable data sources, role-based access, model validation and traceability. This aligns with federal expectations for auditability and mission risk management.
For governance frameworks and risk controls, see the NIST AI Risk Management Framework. NIST AI RMF.
What's next
AI and emerging tech in homeland missions will be a central topic at the Potomac Officers Club's 2025 Homeland Security Summit on Nov. 12. Expect discussion on scaling proven use cases, procurement paths and secure deployment.
Practical steps for program managers
- Define mission outcomes first (availability, risk, cost, speed). Map use cases to those outcomes.
- Inventory data sources across engineering, operations and sensors. Close gaps and set data governance.
- Start with a thin-slice pilot tied to a measurable KPI. Build a path to production from day one.
- Pair MBSE and digital twins with AI to simulate scenarios and reduce test cycles.
- Plan the ATO path early: security controls, model risk documentation and monitoring.
- Upskill the workforce with role-based training and playbooks. For curated options by job role, explore Complete AI Training.
Bottom line
SGT is turning AI into a dependable part of federal workflows by unifying data, validating models and proving results in maintenance, operations and mission support. That's how you move from pilots to performance.
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