[{"data":1,"prerenderedAt":139},["ShallowReactive",2],{"uc-org-jetblue":3},{"organization":4,"includeUnpublished":13,"evidence":14},{"slug":5,"name":6,"country":7,"region":8,"industry":9,"records":10,"useCases":11,"indexable":12},"jetblue","JetBlue","US","north-america","travel-and-hospitality",3,4,true,false,[15,60,92],{"title":16,"useCases":17,"organization":19,"vendors":20,"summary":24,"stage":25,"year":26,"channels":27,"languages":29,"metrics":31,"outcomeDisclosed":12,"sources":42,"verification":52,"grade":55,"id":56,"useCaseTitles":57},"JetBlue: engine out taxi out compliance from 19% to 45% with SkyBreathe",[18],"airline-fuel-efficiency-optimization",{"name":6,"anonymized":13,"country":7,"region":8,"industry":9},[21],{"name":22,"role":23},"OpenAirlines","platform","JetBlue, which operates 300 aircraft, set up a dedicated fuel optimization team and deployed SkyBreathe Analytics and the SkyBreathe MyFuelCoach pilot engagement app from OpenAirlines to turn flight data into fuel saving actions. OpenAirlines' case study headline claims a positive return on investment within three months; in the case study itself, Christopher Lum, JetBlue's Director and System Chief Pilot, reports that engine out taxi out compliance rose and fuel was saved over six months. Lum is quoted saying the airline is \"not asking the pilots to be perfect\" and wants to show them \"where the opportunity may have been\", which the case study frames as a deliberately non punitive approach.","scaled",2025,[28],"internal-tools",[30],"en",[32],{"kpi":33,"value":34,"unit":35,"currency":36,"qualifier":37,"period":38,"claimant":39,"quote":40,"sourceUrl":41},"cost-savings",300000,"currency","USD","exact","in one month","vendor","$300,000 in savings just from engineout taxi in in one month.","https://blog.openairlines.com/fuel-efficiency-journey-jetblue",[43,46,49],{"url":41,"title":44,"publisher":22,"date":45},"JetBlue's fuel savings: positive ROI in 3 months","2025-12-15",{"url":47,"title":48,"publisher":22},"https://www.openairlines.com/fuel-management-software/","SkyBreathe Fuel Management Software",{"url":50,"title":51,"publisher":22},"https://www.openairlines.com/pilot-engagement-app/","SkyBreathe MyFuelCoach, Pilot Engagement App",{"level":53,"checkedAt":54},"source-verified","2026-09-30","C","jetblue-skybreathe-fuel-efficiency",[58],{"slug":18,"title":59},"AI for airline fuel efficiency optimization",{"title":61,"useCases":62,"organization":64,"vendors":65,"summary":68,"stage":69,"year":70,"channels":71,"languages":72,"metrics":73,"outcomeDisclosed":12,"sources":82,"verification":86,"grade":55,"id":88,"useCaseTitles":89},"JetBlue: data observability and an internal Data NPS",[63],"data-quality-monitoring-agent",{"name":6,"anonymized":13,"country":7,"region":8,"industry":9},[66],{"name":67,"role":23},"Monte Carlo","JetBlue's data team needed to catch freshness, volume and schema problems in its Snowflake tables before they reached the business, and to prove that data reliability work was paying off. It deployed Monte Carlo's data observability platform for automated alerting, field level lineage and pipeline ownership documentation, and started tracking an internal \"Data NPS\" score from its own data consumers to measure trust in the data over time. Monte Carlo reports that a year after the JetBlue implementation, that Data NPS score had risen 16 points.","production",2024,[28],[],[74],{"kpi":75,"value":76,"unit":77,"qualifier":37,"period":78,"baseline":79,"claimant":39,"quote":80,"sourceUrl":81},"nps-change",16,"points","year over year","JetBlue's internal Data NPS score from its data consumers before the increase","To that end, when JetBlue recently surveyed its data consumers more than a year after its Monte Carlo implementation, the airline saw its Data NPS score increase 16 points year over year.","https://montecarlo.ai/blog-jetblue-monte-carlo-data-observability",[83],{"url":81,"title":84,"publisher":67,"date":85},"How JetBlue Used Data Observability To Help Improve Internal “Data NPS” By 16 Points Year Over Year","2024-01-30",{"level":53,"checkedAt":87},"2026-09-29","jetblue-data-observability",[90],{"slug":63,"title":91},"AI agent for data quality monitoring and observability",{"title":93,"useCases":94,"organization":97,"vendors":98,"summary":101,"stage":25,"year":102,"channels":103,"languages":108,"metrics":110,"outcomeDisclosed":12,"sources":124,"verification":131,"grade":55,"id":133,"useCaseTitles":134},"JetBlue: AI virtual agent and digital messaging support",[95,96],"flight-disruption-and-rebooking-agent","first-line-contact-centre-agent",{"name":6,"anonymized":13,"country":7,"region":8,"industry":9},[99],{"name":100,"role":23},"ASAPP","JetBlue moved its customer support to an AI platform from late 2019, opening messaging channels (Apple Messages for Business, Google Business Messaging, web and app chat, WhatsApp) with Spanish language support, a virtual agent that resolves routine requests and AI assistance for the crewmembers who handle the rest. In a January 2026 conference session published by the vendor, a JetBlue customer support leader described weather disruptions, when passengers ask for their options, and said the conversations crewmembers now handle (rebooking, refunds, alternatives weeks away) are multifaceted, which is why the airline has looked at AI that orchestrates several workflows. The ASAPP speaker in the same session warned against reading containment gains without checking whether customers still have the option to escalate.",2019,[104,105,106,107],"web-chat","mobile-app","whatsapp","social-messaging",[30,109],"es",[111,118],{"kpi":112,"value":113,"unit":114,"qualifier":37,"period":115,"claimant":39,"quote":116,"sourceUrl":117},"containment-rate",45,"percent","May 2023, virtual agent","The integration of virtual agent experiences facilitated streamlined interactions and contributed to a remarkable 36% year-over-year growth in containment, with a 45% containment rate achieved in May 2023.","https://www.asapp.com/case-studies/jetblue",{"kpi":119,"value":120,"unit":121,"qualifier":37,"period":122,"claimant":39,"quote":123,"sourceUrl":117},"hours-saved",73000,"hours","Q1 2023 only (one quarter, not annualized)","In Q1 2023 alone, this AI-driven efficiency translated into significant savings of 73,000 workforce hours.",[125,127],{"url":117,"title":126,"publisher":100},"ASAPP X JetBlue | ASAPP",{"url":128,"title":129,"publisher":100,"date":130},"https://www.asapp.com/blog/what-airlines-like-jetblue-teach-us-about-building-agentic-customer-experience","What airlines like JetBlue teach us about building agentic customer experience","2026-03-10",{"level":53,"checkedAt":132},"2026-09-26","jetblue-asapp-digital-customer-support",[135,137],{"slug":95,"title":136},"AI agent for flight disruption and rebooking",{"slug":96,"title":138},"AI agent for first line contact centre service",1790783101800]