AI systems that analyze data and present recommendations, forecasts, or scenario comparisons to decision-makers. They help users make faster and more informed operational or business choices.
Use cases
9
Examples
9
Industries
7
Timeline
7 mo
Data updated 1 day ago
Adoption over time
Documented cases per month
By case publish month · completed months only
9 cases documented across 29 months (Mar 24 – Jul 26), peaking at 3 in June 2026.
AI Use Cases Hub
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.
3.6Innovativeness3.6/5Advanced3.6/5 - Advanced. Multimodal genomic exploration on Azure is more advanced than a basic chatbot, but the article mainly describes research infrastructure and a natural-language interface rather than a novel production AI architecture.
Uniklinik RWTH Aachen was working with large, complex datasets as they sought to understand genomic function.The team used Microsoft Azure to support large-scale model training, flexible storage, and development across the full MLOps lifecycle, enabling development of Genolator, an AI system for natural-language genomic exploration.Azure enabled researchers to build Genolator, making genomic data more accessible and helping scientists explore functional relationships, accelerate discovery, and lay the groundwork for future genetic disease research.
3.2Innovativeness3.2/5Differentiated3.2/5 - Differentiated. This is a targeted sports-operations deployment of Microsoft Copilot capabilities on Surface Copilot+ PCs, but the article describes a focused sideline productivity enhancement rather than a novel AI architecture; compared with recent advanced low-code and MLOps cases, it is more incremental.
The National Football League updated its Sideline Viewing System for all NFL clubs, using Surface Copilot+ PCs to give coaches and players real-time, AI-powered tools during game day.The system helps analyze plays, identify formations, and turn complex data into faster, more actionable insights on the sidelines and in the coaches' booth.
3.1Innovativeness3.1/5Differentiated3.1/5 - Differentiated. Comparable to recent applied Bedrock enterprise cases; stronger than a basic chatbot because it combines RAG plus text-to-SQL for regulated credit workflows, but it is still a practical production implementation rather than a novel architecture.
Rich Data Co (RDC) is a Sydney-based SaaS provider specializing in AI-driven credit decisioning for business and commercial lending.RDC built two assistants to support data scientists and portfolio managers with model development, data querying, and portfolio analysis for financial institutions.
3.7Innovativeness3.7/5Advanced3.7/5 - Advanced. More advanced than a basic AI assistant because it combines a centralized operational data platform with scenario optimization and controller-in-the-loop decision support, but it is not a novel model architecture; similar enterprise AI workflow cases tend to cluster around incremental-to-advanced deployment rather than breakthrough novelty.
SWISS, part of Lufthansa Group, uses Google Cloud AI and Machine Learning in the Operations Decision Support Suite (OPSD) to create a joint data repository for crew, passenger, rotation, and technical information.The modular data platform centralizes operational data above existing tools and silos, manages scenarios, and recommends optimal flight, rotation, and passenger-management actions for operations controller review.Controllers can execute recommendations in two clicks, and the first use cases focused on rotation planning and passenger management.
3.1Innovativeness3.1/5Differentiated3.1/5 - Differentiated. Above a basic natural-language data assistant because it adds a governed AI-ready data operating model with semantic layers, ground-truth exemplars, automated checks, and guardrails; compared with nearby calibration cases it is more structured, but still an applied enterprise analytics pattern rather than a novel agent architecture.
Vanguard built a Virtual Analyst for analysts and business stakeholders to query complex financial datasets through natural language.The implementation relies on AI-ready data foundations, including a metadata catalog, semantic layer, ground-truth question-to-SQL examples, automated data quality checks, and AWS services such as Amazon Bedrock, Amazon Bedrock Guardrails, Amazon ECS, Amazon S3, AWS Glue, and Amazon Redshift.
3.5Innovativeness3.5/5Advanced3.5/5 - Advanced. The prototype combines conversational text-to-SQL, RAG, and automated evaluation for a domain-specific media analytics workflow, which is more advanced than a basic chatbot but still a pragmatic applied implementation.
VideoAmp, a media measurement company, worked with the AWS Generative AI Innovation Center to develop a prototype natural-language analytics chatbot for its media analytics data.The solution is designed to let non-technical users ask questions in natural language and receive SQL-generated answers, summaries, and retrieved data from VideoAmp's analytics warehouse.
3Innovativeness3/5Differentiated3/5 - Differentiated. Geospatial AI for crop recognition and regulatory decision support is a solid domain-specific application, but the article describes a straightforward satellite-imagery workflow rather than a notably novel architecture.
Syngenta France has collaborated with xFarm Technologies, leveraging geospatial AI for crop recognition via satellite. The project identifies specific crops such as sorghum, millet, buckwheat and chia to inform farmers whether they are allowed to apply protection products in nearby fields according to local regulations.The project operates in France and its results are incorporated into Syngenta's Quali'Cible DSS.
3Innovativeness3/5Differentiated3/5 - Differentiated. The case uses Azure OpenAI generative capabilities for customer insight analysis and creative product ideation (Y3000) alongside supply chain/operations decision support, but without detailed architectural innovation evidence.
Coca-Cola is leveraging Microsoft's Azure OpenAI service to transform its supply chain and marketing processes. With advanced generative AI capabilities, it introduced innovations like Y3000, a beverage developed using customer taste data. Other implementations include enhanced decision-making tools for efficient manufacturing and cross-functional operations, positioning Coca-Cola as a digital transformation leader.
3.8Innovativeness3.8/5Advanced3.8/5 - Advanced. More advanced than a basic assistant because Falcon combines proprietary real-estate data, predictive analytics, and purpose-built agents across multiple operating workflows. It is still not a clearly breakthrough architecture, and the article does not prove the kind of rare custom model training or measured scale that would push it into the 4s.
JLL has built Falcon, an AI platform for commercial real estate that combines proprietary data and AI models to provide customizable assistants and predictive analytics for property and portfolio management.The platform uses generative AI and purpose-built agents to turn building operational data, sustainability metrics, lease transactions, and market intelligence into actionable insights for real estate professionals.
How many decision support use cases are documented?
The AI Use Case Hub documents 9 real decision support deployments across 7 industries, with 9 detailed company examples you can browse.
Which industries adopt decision support the most?
Decision support is most common in Other (22%), Finance (22%) and Consumer & Food (11%).
Which countries lead in decision support?
United States leads documented decision support deployments, followed by Switzerland and Australia.
What technologies are used for decision support?
Teams most often build decision support with Amazon Bedrock, Amazon Bedrock Guardrails and Anthropic Claude 3.
What AI capabilities power decision support?
Across the documented deployments, the most common capability patterns are RAG (22%), Sustainability (11%) and Copilot (11%).
What results do companies report from decision support?
Across the 9 deployments reporting outcomes, companies most often cite better decisions & insight (67%), customer experience & trust (56%) and new product / capability (44%).
