Global law firm modernizing legal ops with Microsoft Fabric and AI
K&L Gates is a global corporate, regulatory and litigation law firm whose non-legal hiring skews unusually toward data roles, backed by a Microsoft-centric analytics stack of Fabric, Power BI, DAX and Purview. Work on semantic models, Direct Lake datasets and AI use cases in litigation and finance points to a firm building an internal data platform, while ServiceNow and ITIL 4 change governance show maturing IT operations.
K&L Gates is a law firm partnership headquartered in Pittsburgh, Pennsylvania, with 1,001–5,000 employees. It advises global corporations, capital markets participants, middle-market and emerging growth companies, public sector entities, educational institutions, nonprofits and individuals. Practice areas include corporate, finance, asset management and investment funds, intellectual property, energy, labor and employment, litigation and policy work. Lawyers and policy professionals operate on an integrated global platform, supported by business teams in data, marketing, finance, IT and operations.
K&L Gates runs a Microsoft-heavy stack including Azure, Microsoft Fabric, Power BI, Purview and Power Automate, alongside iManage, RelativityOne, ServiceNow, Box, Terraform, Kubernetes and GitLab.
Yes. Beyond legal roles, data is the firm's largest business hiring area, tied to projects like Microsoft Fabric semantic models, Direct Lake dataset management and finance AI use cases.
Current initiatives include technology modernization, a litigation AI solution rollout, finance AI use cases, knowledge management programs, Microsoft Fabric semantic models and ITIL 4 change enablement.
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K&L Gates's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.