Meet the organizations using data and AI to drive breakthrough results across financial services, healthcare, energy, retail and more.
Every industry has its own unique challenges. The Databricks Customer Awards Industry winners are the organizations that didn't just face theirs — they used data and AI to solve them.
The Industry Awards recognize one standout organization from each sector: the company whose work best demonstrates how data intelligence can drive breakthrough results, reshape operations and create new possibilities within their field. From a global bank unifying risk and finance data across regions to a hospital transforming how clinical data is ingested and reused, this year's winners show just how broad and deep that impact can be.
This year's program recognizes 10 winners spanning financial services, communications, health and life sciences, manufacturing, retail and CPG, energy and utilities, enterprise technology, public sector, digital-native businesses and excellence in cybersecurity. Together, they represent some of the most inventive and impactful uses of Databricks we've seen — and we're thrilled to celebrate each of them.
Without further ado, meet the 2026 Databricks Customer Awards Industry winners.
SMBC Group is a top-tier global financial group. Headquartered in Tokyo and with a 400-year history, SMBC Group offers a diverse range of financial services, including banking, leasing, securities, credit cards and consumer finance. The bank is using data and AI to integrate risk, treasury and finance; harden cybersecurity; and modernize the digital experience for clients.
At the center of that transformation is a single Databricks lakehouse that brings SMBC's data together across regions — on one governed platform. Unity Catalog provides the bank with cross-region data sharing, lineage and access controls. Lakehouse runs analytics and AI at the scale a global bank requires, while holding the line on cost and complexity.
On that foundation, SMBC is using Genie and Agent Bricks to put trustworthy, data-driven intelligence directly in the hands of the business. The platform powers a chatbot for the Cash Management System, generates Early Warning Indicators for portfolio risk and is being extended into front-office credit memo workflows — turning analyst hours into minutes.
The results are measurable:
These outcomes show how SMBC treats data as a strategic asset — operationalized every day across global teams to drive better, faster and more defensible decisions.
Lumen Technologies is a digital networking services company that delivers the secure, high‑performance, intelligent connectivity enterprises require to achieve their AI ambitions. The company uses data and AI to give finance and operations teams faster, more consistent access to information.
In finance, Lumen brings together data from domains such as revenue, billing and network expense so teams can work from a shared view of the numbers. The company consolidates this data on the Databricks Platform. It introduces natural‑language agents that let employees ask questions in their daily tools and get governed, explainable answers without specialist skills.
On this foundation, Lumen:
In Service Assurance, Lumen is making it easier to investigate and resolve network incidents that can affect critical customer workloads. A Lakehouse architecture, foundation models and multi-agent workflows provide a single conversational interface. In which teams can review service status and operational context, analyze tickets, review case history, run diagnostics and explore time to resolution in natural language. This approach is helping scale automation across service operations, with internal metrics showing 3 million+ AI-powered diagnostics, 35% ticket deflection and more than 100,000 AI-generated actions.
Hospital for Special Surgery (HSS) is the world’s leading academic medical center focused on musculoskeletal health. For 16 consecutive years, it has been ranked #1 in orthopedics in the U.S. by U.S. News & World Report — a distinction that reflects sustained excellence across clinical care, research and innovation.
Building on this foundation of leadership and impact, in June 2025, HSS launched an ambitious enterprise data transformation journey: to design and implement a modern data lakehouse on Databricks within an 18-month timeframe. The objective was not incremental improvement, but structural change, unifying a highly fragmented data ecosystem, strengthening governance at scale and enabling faster, more reliable decision-making across the enterprise.
Rather than following a traditional use case-driven data ingestion model, HSS adopted a fundamentally different strategy — full system ingestion. In practice, this required ingesting source systems in their entirety, rather than selectively extracting subsets of data tied to individual use cases. ERP, HRIS, EHR, PACS and other source systems were brought in as complete datasets, preserving fidelity, context and enabling reuse across multiple downstream applications without re-ingestion or re-engineering.
This approach introduced meaningful upfront complexity. Requiring deeper coordination with system owners, rigorous data modeling and stronger governance controls from day one. However, it established a scalable foundation. New analytics use cases can now be delivered rapidly without revisiting ingestion patterns or re-modeling data, significantly reducing time-to-insight and technical redundancy.
In just 10 months, HSS had ingested 40+ source systems, established 14,500+ production tables and 130+ schemas, and delivered a growing set of data products and applications.
More importantly, the transformation has shifted the operating model for how data is delivered across the enterprise. Teams can now move from question to insight using reliable, governed datasets, enabling not just faster delivery, but more sophisticated analytics, functionality and deeper insight generation. It is this step-change in both capability and execution that distinguishes this transformation and underpins the program's recognition as an industry-leading data initiative.
TrinityRail® is a North American railcar lessor, manufacturer and logistics provider that is adopting more digital services into its platform to enhance its suite of customer offerings. As part of its 2026 strategy, the company is focused on operationalizing intelligence, embedding data and implementing AI in its day-to-day decisions and workflows.
TrinityRail brings data together from legacy systems that previously limited visibility across manufacturing, leasing and services and made it harder to improve margins, working capital and workforce productivity. The company is re-platforming core business systems on the Databricks Platform and converging its data estate to support “Systems of Action” — moving from static dashboards to transactional applications and agents that act on insights in near real time.
On this foundation, TrinityRail:
Through the acquisition of RSI Logistics, TrinityRail also integrates telemetry data from railcar sensors to support data-as-a-service offerings for customers. Together, these efforts target measurable outcomes such as inventory reduction, procurement automation, per diem and supply chain savings and more responsive digital services across its fleet.
PepsiCo is a global food and beverage leader — and data powers its operations at scale. Partnering with Databricks, PepsiCo built its Enterprise Data Foundation (EDF) to enable every function across the company to build and scale data and AI products on a shared, modern, scalable and performant platform.
EDF unifies enterprise data and powers reusable data products used by supply chain, commercial, consumer, finance, procurement, R&D, HR and sustainability teams globally. The foundation supports priority business programs with strong operating metrics, including onboarding 85%+ of enterprise data, exceeding 90% data quality and catalog coverage, and delivering 200+ enterprise data and AI products to production.
On this Databricks foundation, PepsiCo has driven reporting and data simplification by 60% to date and plans to move to AI for BI consoles across lines of business and functions.
Built on Databricks, EDF is AI‑ready by design — supporting advanced analytics, machine learning and governed AI at scale. With embedded governance and semantic context, PepsiCo turns clean, connected data into insights and actions that drive real business impact across the enterprise.
Ausgrid is a major electricity distributor in Australia serving homes and businesses across its network. The company is modernizing how it manages and uses data so a relatively small team can support analytics and AI across the enterprise.
Ausgrid brings data from parallel legacy analytics systems that increased platform costs and made it harder to deliver trusted, reusable data to business users. A lean eight‑person data team planned and executed a full migration from Azure Synapse to a lakehouse architecture on the Databricks Platform, decommissioning the old environment and standing up an Enterprise Data Platform with certified “gold” data products across domains such as customer, finance and weather. This work removes duplicate platform spend and reduces operational overhead from maintaining two analytics stacks.
On this foundation, Ausgrid:
Looking ahead, Ausgrid focuses on scaling to an AI‑driven utility by releasing more domain‑aligned data products and enabling tools such as Databricks Lakehouse, Genie and apps for democratized analytics. The goal is to onboard thousands of employees onto a single lakehouse and use data and AI to support smarter decisions, modern BI and embedded use cases while improving total cost of ownership through consolidation and elastic compute.
Intel is a global technology company that designs and manufactures semiconductors and computing technologies powering devices, data centers and intelligent systems worldwide. The company is modernizing its data and AI foundation to improve collaboration, governance and speed of innovation across the organization.
As Intel’s data estate grew in scale and complexity, teams faced challenges with consistency, governance and fragmented workflows across business units. To address this, Intel IT aligned cross-functional teams around a unified data strategy and standardized analytics and ML consumption on the Databricks Platform, helping bring greater consistency and control across the enterprise.
On this foundation, Intel IT:
Intel IT also strengthened its internal data community by aligning operations, analytics and governance teams around shared standards and operating models. This has helped improve data stewardship while making it easier for teams to discover, access and reuse data across the organization securely.
Together, these efforts have made Intel’s data ecosystem more connected, governed and scalable, supporting faster innovation and broader adoption of data and AI across the business.
IDB Invest is the private sector arm of the Inter-American Development Bank, financing companies and mobilizing private investment to advance development across Latin America and the Caribbean. In 2025, it reported a record $13.1 billion in total activity as it advanced a broader institutional transformation designed to increase scale, quality and development impact.
As IDB Invest’s efforts with Databricks have expanded, the organization has modernized how teams’ access and act on information across treasury, analytics and risk workflows. A core objective has been to reduce fragmentation in the analytical environment and establish a stronger foundation for faster, more consistent and more AI-enabled decision-making across the business.
This transformation includes applying AI-based document analysis to support the Risk and Sustainability Management team as deal flow grows and financial and non-financial risk reviews become more complex. Using natural language processing and large language models, IDB Invest can analyze and summarize investment proposals, aide-memoires and annual supervision reports while creating a centralized repository of institutional knowledge and key financial information for faster retrieval.
IDB Invest also implemented an enterprise-wide AI virtual assistant powered by Databricks Genie to provide conversational access to data and analytics. By combining Genie with governed access through Unity Catalog, the organization is giving users a secure way to query enterprise data, explore scenarios and generate summaries in natural language. This helps extend analytics beyond specialist teams and broadens access to AI-driven insights across treasury, risk and impact workflows. This further shortens time to insight, reduces reliance on manual reporting and accelerates adoption of AI across risk, treasury and development impact workflows.
Early outcomes from this work include:
Together, these efforts show how IDB Invest is using data and AI to streamline operations, strengthen decision-making and support its broader growth and development mandate.
Superhuman, the productivity platform that includes Grammarly, Coda, Superhuman Mail and Superhuman Go, serves over 40 million daily users across dozens of languages and relies on the Databricks Platform to deliver suggestions that are fast, reliable and cost-efficient at a global scale.
In close partnership with Databricks, Superhuman migrated a majority of the inference traffic powering Grammarly’s flagship grammatical error-correction model from a DIY vLLM stack to Databricks High Throughput Model Serving.
On this foundation, Superhuman:
Beyond inference, Superhuman has adopted Lakebase, Databricks’ fully managed PostgreSQL service natively integrated with the lakehouse, as a transactional backbone for internal applications and production services, syncing Delta tables into a low-latency store and writing application state back without custom pipelines. This Lakebase and Databricks Apps foundation has turned multi-month feature onboarding and reverse-ETL projects into work measured in weeks or even hours, while dramatically reducing on-call load for engineering teams.
Together, these investments demonstrate how a digital-native company like Superhuman can run high-volume LLM inference and data-driven applications side by side, tightly managing latency, reliability and cost at consumer scale.
Adobe empowers everyone, everywhere to imagine, create and bring any digital experience to life. From creators and students to small businesses, global enterprises and nonprofit organizations, customers choose Adobe products to ideate, collaborate and be more productive, drive business growth and build remarkable experiences.
But in today’s digital world, trust is what makes bold ideas possible. Trust empowers creativity — and it starts with security, by protecting our customers and communities who use Adobe’s products every day.
As the volume and complexity of security data have grown, Adobe has continued to evolve its data-driven cybersecurity approach. Adobe’s security team leverages data and AI tooling, including Databricks, to help enable more scalable analysis and improve the speed and accuracy of threat detection.
By applying software engineering practices to its detection workflows, Adobe has strengthened the consistency, governance and efficiency of its security operations. The result has been a measurable reduction in false positives, faster detection development and improved analytical performance against large-scale data.
These efforts reinforce Adobe’s ability to identify and respond to ever-evolving threats — and underpin the secure, reliable digital experiences for Adobe customers worldwide.
Sumitomo Mitsui Banking Corporation, Lumen Technologies, Hospital for Special Surgery, TrinityRail, PepsiCo, Ausgrid, Intel, IBD Invest, Superhuman and Adobe.
Ten winners. One throughline: organizations that saw what data and AI could do in their field and built something worth celebrating. Their work pushes the whole community forward — and that's exactly the point.
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