From Experimentation to Execution: Platform Engineering for Scalable Generative AI

In our latest thought leadership report, From Experimentation to Execution: Platform Engineering for GenAI, completed in partnership with Red Hat, Futurum Research covers why enterprise GenAI initiatives stall before reaching production and outlines the platform engineering practices organizations need to convert AI investment into repeatable business outcomes.
Charting the Rise of the Revenue-Driven Data Team: ‘New Business Opportunities’ Become the Fastest-Growing Priority for 2026

Futurum’s Brad Shimmin, VP of Data Intelligence, Analytics & Infrastructure, notes a shift in 1H 2026: building AI capabilities (-5.6 pts) and trust in data (-6.5 pts) fell as top data team objectives, while execution goals surged.
The End of Token Maxing: Why Pragmatic AI Engineering is Replacing Frontier Models

Futurum analysts Brad Shimmin and Guy Currier discuss how Tokenomics and AI FinOps are forcing enterprises to abandon massive frontier models in favor of smaller, domain-specific AI endpoints governed by strict agent control planes. Utilizing AI – Ep. 37, “The AI Market is Turning Away from Frontier Models”
Active Storage Takes Over: AWS DynamoDB Adds Native Vector Search for Agentic AI

Brad Shimmin, VP at Futurum, analyzes the launch of native vector search in AWS DynamoDB. By embedding semantic retrieval directly into its serverless operational database, AWS eliminates fragile AI data pipelines and challenges standalone vector DBs.
Can Legacy Data Security Survive the Velocity of Autonomous AI Agents?

Brad Shimmin, VP and Practice Lead at Futurum, shares his insights on Bedrock Data’s launch of Agent DLP, a runtime data loss prevention tool designed to secure AI agents and MCP tool calls at machine speed.
The Software-Defined Vehicle is Winning the Compute War and Losing the Owner

Futurum analysts Olivier Blanchard and Brad Shimmin on the moment the car stopped being a car, and why the sharpest pushback isn’t about EVs, autonomy, or subscriptions at all. From Utilizing AI – Ep. 35, “Software-Defined Vehicles: Do You Really Own Your AI Car?”
WEKA Engineers the AI Chassis to Conquer the Inference Power Paradox

Brad Shimmin, VP and Practice Lead at Futurum, shares his insights on WEKA’s launch of the WEKApod 3 appliances and NeuralMesh 6 software. By taking total control of its hardware engineering, WEKA aims to solve the severe power and density constraints currently choking enterprise AI inference deployments.
Solving the Distributed AI Dilemma: Oracle Base Database Cloud@Customer Brings OCI Automation to Local Workloads

Brad Shimmin at Futurum analyzes Oracle’s launch of Base Database Cloud@Customer X11, exploring how converged application VMs and local AI Database 26ai deployments solve data gravity and latency issues.
RegattaDB Arrives: A Unified Engine Built for the Era of Read-Write AI

Brad Shimmin, VP of Data Intelligence, Analytics, and Infrastructure at Futurum, explores Regatta’s launch of RegattaDB. By unifying OLTP, OLAP, and vector workloads, this new architecture provides the low-latency core required to power read-write autonomous AI agents.
The Active Storage Revolution: VAST and Cloudera Team Up to Cure Enterprise GPU Starvation

Brad Shimmin, VP and Practice Lead at Futurum, explores the new strategic partnership between VAST Data and Cloudera. By integrating the VAST AI OS with Cloudera data services, the vendors aim to eradicate ETL complexity and eliminate GPU starvation.
AWS Looks to Collapse the Search-Analytics Divide: How Its New OpenSearch Engine Fuels Agentic AI

Brad Shimmin, VP at Futurum, explores how AWS is re-architecting Amazon OpenSearch Service. By fusing search and analytics and integrating native MCP support, AWS aims to slash log storage costs by 70% while fueling autonomous AI agents.
Databricks Data + AI Summit: Looking Beyond the Database Through Unified Transactions, Analytics, and Agentic AI

Brad Shimmin, Chief Analyst at Futurum, shares his insights on Databricks’ 2026 Summit announcements, detailing how the unification of transactional and analytical data via LTAP lays the groundwork for truly autonomous agentic AI.
