Category: Thought Leadership

Demand forecasting works at catalog scale, but value still leaks in the last mile, where a forecast must become a placed order. This post describes a pattern that closes that gap: Databricks Many Model Forecasting and a Genie Agent surface the forecast, and Amazon Quick reconciles it against an external supplier feed, placing routine orders automatically and escalating only the exceptions. The pattern applies well beyond supply chain.

Managing AI costs is a top challenge for organizations scaling generative AI on AWS. This post shows how AWS CUR 2.0, Amazon S3, and AWS Trusted Advisor form the data foundation that New Relic Cloud Cost Intelligence builds on for Amazon Bedrock workloads. The result is token-level cost visibility inside the observability workflows engineering teams already use, shifting AI from opaque experimentation to disciplined, ROI-driven operations.

Banking, financial services, and insurance (BFSI) institutions need AI systems that can act autonomously at market speed, but autonomy without governance is a nonstarter in an industry defined by regulatory intensity and operational risk. In this post, Apexon and AWS present an architecture-first approach to operationalizing agentic AI on AWS, with governance built in from the start across three layers: intelligence, orchestration and execution, and governance, security, and audit. Learn how governed agentic patterns apply to core BFSI workflows like customer onboarding, credit processing, and payments exception handling, enabling autonomy at scale while keeping every action traceable and auditable.

AI agents authenticate, hold permissions, and act on data, yet most organizations manage them as application components rather than governed identities. Learn how Saviynt Zuma works with Amazon Bedrock AgentCore to govern agents as first-class identities, from registration and least-privilege provisioning through runtime authorization, certification, and deprovisioning. Security teams can then answer which agents exist, who owns them, and what they can access.

The race to deploy agentic AI is outpacing the governance needed to put it into production – 74% of companies plan to deploy by 2027, yet only 21% have mature governance models. AWS launched GEAR (Governance Enabling Agentic Readiness) to close that gap, identifying partners whose governance accelerators compress months of setup into weeks and turn AI pilots into production-ready systems with measurable results.

F5 AI Guardrails and F5 AI Red Team deliver continuous adaptive security for AI applications on AWS Bedrock. This post introduces three capabilities for enterprise AI security: pre-deployment model evaluation, adversarial testing at scale, and adaptive runtime protection. Learn how these capabilities compress the gap between threat discovery and defense deployment from weeks to minutes.

AWS Marketplace has reduced the listing fee for professional services transactions. Combined with AI-assisted listing creation, conversational discovery through agent mode, branded Storefronts, and variable payment models, consulting partners now have the economics and the infrastructure to make AWS Marketplace a scalable growth channel. Learn how partners are investing in AWS Marketplace, and how to start building your presence today.

A major bottleneck for controlling Apache Spark costs or scaling analytics and AI workloads is Spark’s hard dependency on traditional CPU-only infrastructure. In this post, learn how DataPelago Accelerator for Spark closes that gap by seamlessly running your Spark workloads on heterogeneous hardware, delivering up to 10 times faster execution and up to 80% lower costs with zero code changes, no data migration, and no disruption to existing security configurations. See production results across social media, ecommerce, and financial services customers as you find out how to run a proof of concept on your own workloads.

Modern data architectures often rely on separate databases for transactions, analytics, and AI, leading to extract, transform, and load (ETL) latency; data inconsistency; and rising costs. OceanBase addresses these challenges with a unified multimodel architecture that converges online transaction processing (OLTP), online analytical processing (OLAP), and vector retrieval within a single engine on Amazon Web Services (AWS).

Grubhub and Vody are proving that the winners of AI-powered commerce won’t be defined by a single breakthrough feature, they’ll be the retailers who invest early in unified, AI-ready data foundations built on AWS. By modernizing catalog, content, and behavioral data across 400,000+ merchants, they delivered initial model results in just 6–10 weeks and demonstrated measurable improvements in search relevance, conversion, and customer experience, offering a blueprint for retailers preparing to lead the next wave of AI innovation.