{"resourceId":"syracuse-pwc-5g-occupancy-fabric-2026","versions":[{"version":"external-a01afccbc88eff0da41d5194d7de5f4475ff07ebc722a13fc863222957c61bb9","resource":{"id":"syracuse-pwc-5g-occupancy-fabric-2026","title":"Syracuse-PwC case study claims occupancy accuracy gains and expected 10M dollar 5G return","organization":"PwC / Syracuse University","sector":"Higher education / facilities and campus operations","geography":"United States (New York)","publishedAt":"undated PwC case study page (retrieved 2026-09-15)","publicationDate":null,"eventDate":null,"sourceName":"PwC","sourceLabel":"PwC Syracuse AI-enabled campus case study","sourceUrl":"https://www.pwc.com/us/en/library/case-studies/syracuse-ai-enabled-campus.html","evidenceClass":"vendor-claim","outcomeClass":"emerging","topics":["infrastructure","data-security","operating-model","governance-procurement"],"finding":"PwC describes Syracuse private 5G campus, Microsoft Fabric unification of 10+ sources, and IoT thermal/camera pilots that raised occupancy accuracy from about 80% to 95%, unlocking 40+ insights for space, custodial, and energy decisions. The page states Syracuse expects about a $10 million return on the 5G investment from avoided construction and reduced energy/maintenance; Fabric ownership transferred to university staff. Privacy is noted as key for heat-signature analysis.","sledRelevance":"Facilities and space-management AI/analytics for campus operations: sensor plus Wi-Fi occupancy, unified data platform, and capital avoidance framing.","evidence":"Consulting vendor case study with quantified accuracy and expected financial return; includes named Syracuse CDO quote. No independent evaluation, baseline methodology detail, or audited savings.","architectureImplications":"Interpretation: private 5G/IoT signals into Azure/Fabric with Power BI/Copilot query layer; start with constrained pilots before scale. Accuracy claims apply to occupancy sensing, not dollars realized.","governanceImplications":"Interpretation: data-sharing across departments is presented as a governance win; camera/thermal sensing needs explicit privacy review beyond the case-study sentence.","securityPrivacyImplications":"Thermal/camera presence analytics and Wi-Fi occupancy create surveillance risk; page asserts privacy focus but does not publish DPIA-like controls.","caveats":"Vendor-authored; $10M is expected not realized; publication date unknown; occupancy accuracy method not independently verified; Copilot benefits anecdotal.","streamIds":["campus-operations"],"roles":{"sales":"Facilities and digital officers chasing space utilization and capital avoidance will recognize Syracuse occupancy-accuracy and expected-return framing. PwC reports IoT sensors raising occupancy accuracy from about 80% to 95% and an expected $10 million return on 5G from avoided construction and energy or maintenance. Treat dollars as hypothesis and accuracy as the nearer proof point. Discovery should separate sensor accuracy from realized savings, name who operates Fabric after integrator exit, and require a privacy review for thermal and camera analytics before scaling beyond two buildings.","engineering":"Fit is IoT and Wi-Fi occupancy into a governed Microsoft Fabric lakehouse with Power BI and Copilot queries on a private 5G campus network. Start with constrained pilots, unify 10-plus departmental sources against a room master, and design minimization for thermal and camera presence data. Prerequisites include network coverage, data-sharing agreements, and privacy design. Constraints are a vendor narrative without a methods appendix, expected rather than audited ROI, and an initial two-building sensor pilot. Presence analytics need retention limits and access control that this case study does not specify.","delivery":"Work is standing up Fabric, integrating sensors and Wi-Fi occupancy, mapping room-level models, publishing dashboards, and transferring operations to university staff. Ownership should rest with campus digital and facilities teams after integrator knowledge transfer. Acceptance is measured occupancy accuracy versus the prior baseline plus documented privacy controls, not claimed dollar returns. Governance checkpoints include privacy review before camera expansion and a go/no-go for additional buildings. Risks include privacy backlash, integrator dependency, and insight volume without decision uptake."},"roleDetails":{"sales":{"customerProblem":"Hybrid work broke space assumptions; campuses risk overbuilding while energy and custodial costs rise.","stakeholders":["Chief digital officer","Facilities/space management","Finance/capital planning","Privacy office"],"discoveryQuestions":["What is current occupancy sensor accuracy by room?","Which savings are realized vs expected?","What privacy review covers thermal/camera analytics?","Who operates the platform after integrator exit?"],"valueHypothesis":"Unified occupancy analytics can improve space decisions; Syracuse reports accuracy gains, while dollar returns remain expected.","potentialEngagement":"Two-building occupancy accuracy pilot with privacy review and capital-avoidance decision log.","unsupportedClaims":["Guaranteed multimillion-dollar savings","Campus-wide energy reductions already realized","Surveillance-free camera analytics"]},"engineering":{"fit":"Smart-campus occupancy and operations analytics on private 5G/IoT with Fabric/Power BI/Copilot.","architectureIntegration":"Azure ingestion of sensor/camera/Wi-Fi into Microsoft Fabric unified model; Power BI views; Copilot natural-language queries.","prerequisites":["Network/IoT coverage","Room inventory master data","Cross-department data sharing agreements","Privacy design"],"constraints":["Case study lacks methods appendix","Expected ROI","Initial pilot limited to two buildings"],"security":"Presence analytics need minimization, retention limits, and access control; not detailed here.","proofOfValue":["Replicate occupancy accuracy measurement","Track decisions avoided with dates","Privacy sign-off artifacts"]},"delivery":{"work":"Pilot sensors, unify data model, publish dashboards, train campus operators, plan scale-out.","dependencies":["5G and IoT vendors","Microsoft Fabric","Facilities systems of record"],"ownership":"University digital/facilities teams after knowledge transfer from integrator.","skillsAdoption":"Analysts and facilities planners learn Fabric/Power BI; leaders use Copilot queries with verification.","governanceCheckpoints":["Privacy review before camera/thermal expansion","Data-sharing agreements","Post-pilot go/no-go for additional buildings"],"acceptanceCriteria":["Documented accuracy vs prior baseline","Operator runbooks owned by university staff","No reliance on unrealized dollar claims in acceptance"],"risks":["Privacy backlash","Integrator dependency","Insight volume without decision uptake"]}},"retrievedAt":"2026-09-15T18:48:30Z","enrichedAt":"2026-09-15T18:57:40Z","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"May change custodial routing and space allocation work; no labor-impact analysis. Accessibility of spaces for disabled users is not evaluated.","procurementImplications":"Integrator-plus-hyperscaler stack; require knowledge transfer, data ownership, and realized-vs-expected savings clauses.","operatingModelImplications":"Central digital office drives shared data; facilities acts on occupancy insights; success depends on departmental data sharing.","sourceVerification":{"openedUrl":"https://www.pwc.com/us/en/library/case-studies/syracuse-ai-enabled-campus.html","referenceExcerpt":"The new IoT sensors increased occupancy accuracy from 80% to 95%.","promptVersion":"sled-research-v4.0","model":null,"basis":"agent-reported inspection"}}}]}