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<\/p>\n\u2018We rolled out Gemini Enterprise across our 500-person company in 27 countries and used real work\u2014not demos\u2014to test what scales,\u2019 says Pythian CTO Paul Lewis.<\/p>\n
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Google Cloud<\/a> all-star partner Pythian reinvented itself by rolling out Gemini Enterprise across its 500-person company, which led to the creation of a new AI operating model<\/a> that is driving \u201cmillion-dollar outcomes\u201d for customers.<\/p>\n \u201cWe rolled out Gemini Enterprise<\/a> across our 500-person company in 27 countries and used real work\u2014not demos\u2014to test what scales,\u201d Pythian\u2019s CTO Paul Lewis told CRN.<\/p>\n \u201cThe results were a 3X increase in active user engagement and an 80 percent reduction in mean time to resolution for database incidents across about 15,000 monthly database tickets,\u201d he said.<\/p>\n [Related: VMware Adds Google, Nvidia AI Models To VCF; Boosts Tanzu Security To Drive AI Adoption<\/a>]<\/p>\n Leveraging Google Gemini Enterprise, the Ottawa, Ontario-based company engineered the Pythian AI Operating Model\u2014a new end-to-end framework designed to take enterprise AI from high-level strategy all the way into sustained production.<\/p>\n \u201cEnterprise AI value does not come from making a tool broadly available. It comes from building the operating model around it: a measurable business agenda, a production-grade platform connected to the enterprise data estate, separate ownership for adoption and transformation,\u201d Lewis said. \u201cAlso, an entirely new skill set to manage observability, drift, and the lifecycle after launch.\u201d<\/p>\n How Pythian Is Driving AI ROI And Outcomes For Customers<\/p>\n Pythian, a Google Cloud premier partner<\/a>, said its AI operating model \u201cconsistently unlocks million-dollar outcomes\u201d for customers.<\/p>\n For example, one customer who specializes in knowledge management leveraged Pythian\u2019s AI operating model, which was able to automate 10 percent of the client\u2019s 20,000 annual IT tickets into \u201cno-touch\u201d resolutions.<\/p>\n This saved the client over a million operational hours, Lewis said.<\/p>\n Another use case was for a customer that specializes in supply chains. Pythian built custom agentic supply chain tools on Google Gemini Enterprise,<\/a> which compressed forecast-matching cycles from several weeks to two or three weeks across 70 global manufacturing sites.<\/p>\n \u201cThe workflow behind that result is deliberately practical: an agent reads and enriches tickets, searches the relevant knowledge, and generates a mini runbook before an engineer starts work,\u201d Lewis said.<\/p>\n \u201cThat changes the economics of operations because it improves the workflow end to end, rather than saving a few minutes in isolated tasks,\u201d he said.<\/p>\n Common AI Use Cases In 2026<\/p>\n