7 cases documented across 37 months (Jul 23 – Jul 26), peaking at 3 in May 2026.
AI Use Cases Hub
1 earlier case before Jul 23 not shown
Each column counts every documented case of this type by its publish month, across the full corpus. The in-progress current month is excluded from columns and surfaced separately, and cases published before the charted window are summarized as earlier cases instead of plotted.
3Innovativeness3/5Differentiated3/5 - Differentiated. The implementation is a differentiated BI and self-service analytics deployment using governed semantic modeling, dashboards, alerting, and embedded analytics, but it is a practical analytics modernization rather than a novel AI architecture.
Assurance IQ, a direct-to-consumer insurance and financial wellness platform, built a trusted business intelligence environment with Looker on Google Cloud to replace scattered reporting in Excel, SQL, and other in-house tools.The company created dashboards and self-service analytics so leadership, marketing, sales managers, and partners could work from a single source of truth without increasing analyst headcount.
3Innovativeness3/5Differentiated3/5 - Differentiated. The article shows a practical but not highly novel retail implementation combining real-time analytics, forecasting, and a customer-facing product locator using Google Cloud AI services.
Morrisons, a UK supermarket chain, moved data from an on-premise stack to Google Cloud so its teams could access business data in near real time instead of waiting for daily exports.The company also built a customer-facing Product Finder in its app to help shoppers locate products in store by aisle and shelf position, using cloud data and generative AI.
4Innovativeness4/5Advanced4/5 - Advanced. Advanced use of multi-modal Google Cloud AI including BigQuery ML, Gemini, and Vertex AI integrated into a real-time omnichannel customer data platform with AI assistants in retail marketing.
ContactPigeon developed a customer data platform for digital retailers to unify siloed customer data and enable real-time insights.The platform uses Google BigQuery for scalable data warehousing and real-time analysis, integrated with Vertex AI, Gemini, and Looker for machine learning and visualization.Retailers now generate customized reports in minutes versus days and use AI models for customer segmentation, churn prediction, and personalized marketing workflows.AI-powered alerts notify retailers of customer engagement issues and opportunities to enable immediate action and improve conversion rates.ContactPigeon is expanding to generative AI assistants using Gemini for automated campaign messaging, customer service chatbots, and AI shopping assistants.
2Innovativeness2/5Incremental2/5 - Incremental. It primarily describes cloud migration and advanced analytics (Azure + Cortana Intelligence Suite) enabling faster deployment and insights, without unusual AI architecture details.
InSpark, a full-service Microsoft Solutions Partner, implemented Microsoft Azure and Cortana Intelligence Suite for a Dutch pharmaceutical company with global operations. This solution provided valuable data-driven insights, dramatically speeding up the development and production of lifesaving medication, particularly for rare diseases.New features can now be deployed globally within 24 hours, supporting 200,000 pharmacists across multiple continents, and driving significant expansion including to South America. The scalable Azure cloud foundation and advanced analytics enabled rapid decision-making and operational flexibility.InSpark’s approach involved supporting the client through strategic consultation, cloud migration (CSP), and continuous managed services. Their repeatable solutions and full-service support contributed to rapid growth for both the pharma client and InSpark.The pharmaceutical enterprise can now deliver medication faster and more cost-effectively, thanks to the integration of transformational cloud and AI technologies. These digital advancements, supported by Microsoft’s platform, have been integrated into production, workplace optimization, and rapid scaling for new locations.
2Innovativeness2/5Incremental2/5 - Incremental. A PoC centralized factory data pipelines and visualization/reporting using Azure Data Factory and Power BI for analytics, without evidence of novel AI/architecture.
A 3-month PoC implemented visualization and reporting of factory-level analytics using Microsoft Power BI and Azure foundational tools. It provided plant-wide insights for decision-making and highlighted performance improvement.
3Innovativeness3/5Differentiated3/5 - Differentiated. Connects industrial telemetry to Azure Data Explorer and uses Kusto Query Language analytics to deliver tailored manufacturing process optimizations with reported downtime/energy reductions.
Bühler Group leverages Microsoft Azure Data Explorer to connect industrial equipment and automation software to its Bühler Insights platform. Using Azure, Bühler transmits millions of telemetry messages for customers to analyze, resulting in significant optimizations in manufacturing processes. By using tools like the Kusto Query Language for data analytics, Bühler Insights offers tailored manufacturing insights for varying business roles.
2Innovativeness2/5Incremental2/5 - Incremental. Improves store performance primarily via real-time data modernization and Power BI analytics refreshed every 15 minutes, with no strong evidence of AI-specific novelty.
A global footwear company, serving customers in over 1,000 branded retail stores and online, struggled with legacy data systems that delayed critical business decisions. The company aimed to modernize operations, improve store performance, enhance staff efficiency, and support community programs. Partnering with Protiviti, they implemented a real-time analytics solution using Microsoft Power BI, Azure Synapse, and Azure Data Lake. The new solution delivers actionable data every 15 minutes to store managers, enabling rapid business adjustments, optimizing staffing, refining sales strategies, and deepening community impact. Automation streamlined reporting and replaced slow, manual systems. With improved internal controls and data governance, leadership and store managers can make immediate, insight-driven decisions. The project led to significant cost savings, efficiency gains, and improved decision speed across the business.Data modernization drives retail innovation across the organization.Actionable insights now delivered to store associates within 15 minutes rather than days or weeks.
2.8Innovativeness2.8/5Differentiated2.8/5 - Differentiated. Compared with recent Google Cloud location-and-analytics cases, this is a practical scaling and mapping integration with modest novelty; it uses standard Maps APIs, BigQuery, and Compute Engine rather than a new AI architecture.
inDriver is a ride-hailing app that promotes mobility in emerging markets and remote regions by helping customers and drivers negotiate a fair price.The company uses Google Maps Platform, including Maps Static API, Google Places API, and Maps JavaScript API, plus Google Compute Engine and Google BigQuery, to support route calculation, journey-time estimates, pickup guidance, and scaling across 31 countries and 50 million users.
How many operational analytics use cases are documented?
The AI Use Case Hub documents 8 real operational analytics deployments across 5 industries, with 8 detailed company examples you can browse.
Which industries adopt operational analytics the most?
Operational analytics is most common in Retail (38%), Manufacturing (25%) and Pharma (13%).
Which countries lead in operational analytics?
Switzerland leads documented operational analytics deployments, followed by Germany and Global.
What technologies are used for operational analytics?
Teams most often build operational analytics with Looker, BigQuery and Vertex AI.
What AI capabilities power operational analytics?
Across the documented deployments, the most common capability patterns are Agent (13%).
What results do companies report from operational analytics?
Across the 8 deployments reporting outcomes, companies most often cite better decisions & insight (75%), speed & agility (63%) and new product / capability (50%). Where impact is quantified, the strongest evidence is in time & speed: a median −48% across 1 reported metric.
