Matt Sheehan

About Matt Sheehan

AI is brilliant with text and pixels, but enterprises still struggle to turn that into reliable decisions in the physical world. My domain is giving AI a grounded sense of “where” so it can actually act.With 25 years in geospatial, I focus on the evolution from traditional GIS and remote sensing to world models, reasoning engines, and the Decision Layer—the place where spatial understanding turns into operational decisions. I work at the intersection of Spatial AI, Generative AI, and agentic systems, and I care as much about how they reason as what they generate.I have two areas of focus:1. Run Spatial-Next (spatialnext.io), a research lab exploring how Spatial AI, world models, and decision intelligence reshape real-world operations and human–machine teaming.2. Lead commercial Spatial AI strategy for an innovation group, with a simple mandate: make sure what we build is exactly what we can sell, scale, and renew. That means- BD → I translate ambiguous, real-world problems into deployable Spatial AI workflows so sales can sell transformation, not features- Product → I feed domain logic and constraints into the roadmap so our spatial reasoning stack tracks to what the market will pay for- Customer → I own the lifecycle from signature to renewal—driving adoption, value realization, and NRR, and building the feedback loops that fund the next generation of Spatial AI capabilities.In short, I operate as a commercial Decision Architect for Spatial AI, designing the operating model between geospatial data, decision engines, and revenue so the next phase of geospatial moves from lab concept to enterprise-ready decision systems.Clearance: Active DoD Top Secret (TS)Specialties: Decision Velocity | World Models | Spatial Intelligence | AI Decision Layer

Experience

1. Director, Commercial Strategy for Spatial Ai

   Versar Global Solutions

   Mar 2025 — Present

   I don’t write the code; I design the engine that makes AI commercially viable by building the connective tissue between engineering, BD, and the customer. My focus is ensuring enterprise AI moves beyond being \"just a tool\" to become a scalable revenue engine and a reliable \"Decision Layer\" for complex physical and digital environments- Designed the \"Commercial Blueprint\" for AI Delivery: I co-led the creation of an 8-phase lifecycle that integrates Data, Engineering, and Security lanes into a unified flow—ensuring the commercial thread remains intact from initial discovery through to sustained adoption- Created a Diagnostic Lens for Enterprise AI Maturity: I authored a \"Five-Stage\" model used to identify specific \"decision latency\" bottlenecks—moving clients from basic data visibility to advanced simulation and anticipatory intelligence- Established Governance and \"Guardrails\" for Innovation: I introduced \"Solution Resets\" and \"Security Gates\" into the production engine to ensure technical builds (like RIPE) remain anchored to customer needs and measurable \"decision velocity\".

Education

• University of London

  BSc, Physical Geography

  1982 — 1985

• University of Utah

  MSc, Geographic Information Systems

  1996 — 1999

Skills

• Databases
• Esri
• Android
• Gis
• Cloud Computing
• Arcgis
• Geography
• Cartography
• Geographic Information Systems (Gis)
• Arcgis Server
• Arcgis, Geographic Informatio Systems, Gis, Spatial Analysis