Shared infrastructure for running, governing, and scaling AI models and applications across an organization.
Use cases
150
Examples
60
Industries
12
Timeline
9 mo
Data updated 1 day ago
Adoption over time
Documented cases per month
By case publish month · completed months only
125 cases documented across 37 months (Jul 23 – Jul 26), peaking at 25 in May 2026.
AI Use Cases Hub
6 earlier cases 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.
3.8Innovativeness3.8/5Advanced3.8/5 - Advanced. More differentiated than a basic chatbot or simple copilot because it combines a no-code AI agent platform with a Marketplace managed-application deployment model that runs in customer tenants. Similar recent AI platform cases calibrate around the low-to-mid 3s, so this lands slightly higher due to the go-to-market/deployment architecture and enterprise tenancy isolation.
Zammo.ai is an AI platform company that helps organizations launch commercial-ready AI agents quickly.The company moved from a traditional SaaS model hosted in its own Azure tenant to a Microsoft Marketplace managed application that deploys into customer tenants.The approach addresses security, data residency, and slow procurement barriers while enabling business users to create agents using natural language.
3.6Innovativeness3.6/5Advanced3.6/5 - Advanced. Advanced applied data-platform work rather than a basic AI app: TRACT combines BigQuery, GKE, Cloud Run, Earth Engine, and Gemini exploration to operationalize sustainability data at scale. It is similar in pattern to other recent Google Cloud platform cases, but the geospatial normalization and industry-wide traceability scope justify a solidly above-average score.
TRACT (founded by ADM, Cargill, Louis Dreyfus, Olam Food Ingredients) built a cloud-native platform to collect, structure, validate, normalize, analyze, and share sustainability and supply-chain data at scale.The platform uses BigQuery for massive data volumes and rapid queries, Google Kubernetes Engine for workflow orchestration, Cloud Run and Cloud Run functions for processing and API delivery, and Google Earth Engine for geospatial analytics.TRACT is exploring Gemini models to automate ingestion and validation workflows, including an internally developed GeoFixer tool to normalize heterogeneous geospatial data.
3.6Innovativeness3.6/5Advanced3.6/5 - Advanced. This is a strong applied cloud modernization case with planned Gemini conversational access, but the core implementation is a managed microservices and automation redesign rather than a novel AI architecture. It is broadly comparable to recent Google Cloud modernization cases such as GKE-based HPC or enterprise app modernization, so it sits in the mid-3 range rather than the 4s.
The IGAD Climate Prediction and Applications Centre (ICPAC) serves 11 countries in the Greater Horn of Africa with climate forecasting and early warning services.To improve reliability after on-premises disruptions and a monolithic setup, ICPAC moved to Google Cloud and adopted a microservices architecture on Google Kubernetes Engine.ICPAC uses Cloud Run to process geospatial data, automated deployments with Cloud Build and Artifact Registry, and secured data and access with Cloud Storage and IAM.The organization reports that climate data processing dropped from up to 8 hours to 30 minutes, cloud services costs fell by more than 40%, and the platform now runs 24/7.ICPAC also plans to use Gemini for conversational interfaces so users can ask questions in local languages.
IGAD Climate Prediction and Applications CentrePublic Sector
3.7Innovativeness3.7/5Advanced3.7/5 - Advanced. This is more advanced than a standard copilot rollout because it is a multi-platform, auto-scaling enterprise AI platform deployed across global business units. However, the source still describes a managed AWS-based platform rather than a novel agent architecture, so it sits below highly unusual multi-agent or custom-model cases.
Sony Group built a generative AI platform on AWS to scale AI access across global business units.The platform uses Amazon Bedrock and Amazon Bedrock AgentCore, with Amazon SageMaker Unified Studio and Amazon Nova Forge also referenced in the source.Sony says the multi-platform solution democratizes AI for internal users while maintaining zero downtime at large scale.
2.8Innovativeness2.8/5Differentiated2.8/5 - Differentiated. This is a strong but fairly common enterprise modernization pattern: migration, containerization, database modernization, and Bedrock adoption. Compared with recent AWS modernization/agentic-AI cases, it is less novel than agentic or specialized workflow redesign and is closer to practical infrastructure enablement.
Verint Systems modernized its infrastructure on AWS to remove legacy bottlenecks created by acquisitions, including siloed data, monolithic databases, and high licensing costs.The company containerized workloads, moved to Amazon EKS, migrated Microsoft SQL Server databases to Amazon Aurora, and shifted most large-language-model traffic to Amazon Bedrock.AWS modernization and migration programs supported the multiyear transformation, helping Verint improve scalability, reliability, and the speed of AI feature delivery.
3.1Innovativeness3.1/5Differentiated3.1/5 - Differentiated. More than a standard copilot because it combines agentic task decomposition, external tool use, and scalable deployment; however, the core stack is still managed AWS services and is closer to recent differentiated AWS AI platform cases than to frontier multi-agent systems.
SEELE is an AI-native game creation platform that combines a multimodal large language model with a gamified IDE so creators can build playable 3D worlds from prompts without coding.The platform generates code, models, textures, and audio to deliver fully playable games and hosts more than 366,100 active creators and over 200,000 games.For overseas business scenarios, SEELE uses Amazon Bedrock and Amazon EKS to automate workflows, optimize token and text-processing costs, and improve deployment scalability and operational efficiency.
3.1Innovativeness3.1/5Differentiated3.1/5 - Differentiated. Differentiated but not frontier: it combines managed AWS media services with generative AI for multilingual subtitle generation and creative asset automation, similar in novelty to recent mid-3 AWS applied AI platform cases.
Malaysia-based Netmedias built a cloud-native, serverless streaming platform for short dramas.The solution automates video transcoding, multilingual subtitle generation and episode poster creation using AWS services.
4.3Innovativeness4.3/5Advanced4.3/5 - Advanced. This is more advanced than a standard analytics+chatbot pattern because it combines governed semantic grounding, an event-bus workflow, and autonomous creative generation/optimization loops. Compared with similar recent applied-AI cases, the multi-step agentic feedback loop is notably more novel.
Overdose Digital, an AI-first consultancy and digital commerce agency, centralized ecommerce data in BigQuery and used Looker as a governed semantic layer to ground generative AI and reduce hallucinations.The company built an agentic workflow where Looker-defined metrics trigger Vertex AI and Gemini-based agents to generate hyper-localized creative briefs and assets with human oversight.It also used BigQuery AI to detect creative fatigue and automatically trigger a synthesis loop that refreshes creative strategy near real time for nearly 300 clients.
3.7Innovativeness3.7/5Advanced3.7/5 - Advanced. More advanced than a basic educational chatbot because it combines a multi-agent simulation, real-time safety gating, and concurrent orchestration for hundreds of student teams. Compared with recent Gemini/Vertex AI customer stories, it is differentiated but not frontier-level.
Day of AI Australia and partner UNSW Sydney built an immersive educational simulation called Win the Farm to teach AI literacy through a fictional misinformation and narrative game.Students configure AI-powered bots to generate social media posts that influence an election story, while the platform enforces safety and age-appropriate controls at scale.
3.7Innovativeness3.7/5Advanced3.7/5 - Advanced. More than a basic chatbot because it combines multimodal generation, image creation, multilingual chat, and automated content moderation in one product stack. Still, it is a fairly common applied GenAI pattern compared with recent Vertex AI/Gemini cases, not a frontier architecture.
VideoShow uses Google Cloud's generative AI to enhance video creation and provide emotionally nuanced AI conversation support.Since 2022, VideoShow has focused on incorporating AI into product design, with Gemini multimodal capabilities used for text-to-story and text-to-video generation, Imagen 3 on Vertex AI for image generation, Gemini 1.5 Flash and Natural Language AI in its AI chat assistant, Translation AI for multilingual prompts, and Cloud Vision API for content review.
The AI Use Case Hub documents 150 real ai platform deployments across 12 industries, with 60 detailed company examples you can browse.
Which industries adopt ai platform the most?
AI platform is most common in Tech & Comms (24%), Manufacturing (22%) and Consumer & Food (8%).
Which countries lead in ai platform?
United States leads documented ai platform deployments, followed by Germany and Global.
What technologies are used for ai platform?
Teams most often build ai platform with Amazon Bedrock, Vertex AI and Azure AI.
What AI capabilities power ai platform?
Across the documented deployments, the most common capability patterns are Agent (37%), Copilot (26%) and Vision (15%).
What results do companies report from ai platform?
Across the 150 deployments reporting outcomes, companies most often cite new product / capability (83%), speed & agility (63%) and customer experience & trust (45%). Where impact is quantified, the strongest evidence is in time & speed: a median +65% across 15 reported metrics.
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