Media and Entertainment GenAI Modernization Assessment on AWS

Media and entertainment organizations are rapidly adopting generative AI across content production, post-production, personalization, and audience engagement. However, these initiatives often evolve in silos, leading to fragmented architectures, inconsistent data pipelines, and inefficient scaling of AI workloads. Without a unified strategy, organizations face rising operational costs, duplicated tooling, inconsistent content quality, and limited ability to scale GenAI across channels such as streaming, advertising, and digital platforms. Compass UOL helps media companies assess and modernize their GenAI landscape on AWS by identifying fragmentation, defining scalable architectures, and aligning AI initiatives with business outcomes such as faster content delivery and improved audience engagement. This assessment evaluates current GenAI usage, content workflows, and data platforms, and defines an AWS-native modernization roadmap leveraging services such as Amazon Bedrock, data and analytics platforms, and scalable media pipelines. Customers leave with a clear path to scale GenAI across content workflows, reduce manual effort, and improve audience engagement while increasing AWS consumption in a structured and controlled way.

Buyer Problem / Business Trigger

GenAI initiatives fragmented across multiple content and engagement workflows High manual effort in content production, tagging, localization, or personalization Difficulty scaling GenAI across streaming, advertising, and digital platforms Increasing infrastructure cost without clear architecture standardization

Delivery Model

Discovery of current GenAI use cases and media workflows Assessment of data, content pipelines, and AI architecture Definition of AWS-native GenAI reference architecture Roadmap for modernization and scaling

Assessment / Engagement Scope

Evaluation of content production workflows (creation, editing, distribution) Mapping of GenAI use cases (content generation, metadata enrichment, personalization) Assessment of data pipelines and analytics platforms Review of scalability, cost efficiency, and performance of AI workloads Design of AWS-native architecture (Bedrock, data lake, media services) Identification of gaps and inefficiencies in current environment

Expected Output / Deliverables

GenAI modernization assessment report AWS reference architecture for media GenAI workloads Use case prioritization aligned to business impact Cost and efficiency optimization recommendations Implementation roadmap for scaling GenAI

Customer Decision Questions This offer helps the customer answer:

How do we scale GenAI across content workflows without fragmentation? Which AWS architecture supports media GenAI at scale? Where are the biggest efficiency gains in content production and engagement?