AI Enablement · Enterprise Content

Transforming Enterprise Content Discovery with AI

Building a practical, governed enterprise AI capability that helped creative and business teams discover valuable content faster across Paramount’s large media ecosystem.

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Paramount

Executive Challenge

As Paramount’s content ecosystem expanded across brands, productions, and business units, finding the right media became increasingly difficult.

Traditional discovery depended heavily on manually created metadata, inconsistent tagging practices, individual knowledge, and searches across fragmented repositories. Valuable moments could be buried within hundreds of thousands of hours of content, slowing production, marketing, licensing, legal research, and creative decision-making.

The challenge was larger than implementing a search tool.

Paramount needed a scalable way to unlock more value from its media library while addressing concerns around accuracy, security, governance, workflow integration, and enterprise adoption.

Organizational Context

The initiative grew from an earlier AI tagging workflow I introduced using AWS Rekognition to identify and tag photography during live events.

That early work demonstrated the potential for AI to reduce manual effort and improve content discoverability. As image recognition, semantic search, transcription, and video-indexing capabilities advanced, I continued tracking the market and identifying practical use cases for broader media discovery.

I later joined efforts with internal Archives, Engineering, Product, Legal, Security, and operational teams to develop what became Paramount AI Search.

My role was to serve as a key business and operational sponsor representing more than twenty entertainment brands, creative marketing teams, internal production groups, Clip Licensing, Legal, and other organizations that depended on faster access to media.

Strategic Leadership

Championed a long-term enterprise vision for AI-powered content discovery grounded in practical business workflows rather than experimental technology.
Connected lessons from the earlier AWS Rekognition initiative to a broader image and video search strategy.
Represented creative, marketing, production, licensing, legal, archive, and business-operations requirements throughout planning and implementation.
Built alignment across Archives, Engineering, Product, Legal, Security, Operations, and external technology partners.
Led evaluation of multiple AI indexing services, comparing search accuracy, semantic understanding, scalability, security, operational complexity, and model-maintenance requirements.
Supported the selection of Canto AI because it delivered the strongest results and required less ongoing model maintenance at the time of evaluation.
Established approved use cases, governance requirements, rollout priorities, adoption planning, KPI reporting, and operational feedback processes.
Designed workflows that sent selected assets from Paramount’s Media Asset Management environment to the AI platform for indexing.
Expanded adoption through real creative and operational use cases rather than relying on demonstrations alone.

Key Executive Decisions

Decision 1

Prioritize operational value over novelty

Focused the initiative on content discovery problems affecting production, marketing, licensing, and legal teams rather than pursuing AI capabilities simply because they were new.

Decision 2

Prove accuracy before scaling

Addressed skepticism about AI search quality by evaluating multiple services against real Paramount content and real business use cases.

Decision 3

Build security and governance into the operating model

Partnered with Legal, Security, Engineering, Archives, and business stakeholders to define appropriate use, access, and operational controls before broad adoption.

Decision 4

Integrate AI with the existing media ecosystem

Built workflows connecting the enterprise Media Asset Management environment to the AI indexing service so users could benefit from discovery without replacing established source systems.

Business Impact

Minutes of enterprise content indexed for AI-powered discovery.: 570,000+
Reported time savings when locating specific audio and video moments and determining where source media could be retrieved.: 70%+
Larger enterprise digital-library ecosystem connected through the Media Asset Management workflows supporting the initiative.: 100+ PB

Organizational Impact

Adoption expanded quickly as teams experienced measurable value in real production and promotional workflows.

The platform supported work across:

MTV Video Music Awards promotional campaigns
Paramount+ series launches
Nickelodeon YouTube compilations
Creative marketing organizations
Internal production teams
Clip Licensing
Legal content research
Archive and media-operations teams

The initiative demonstrated that a carefully governed AI capability could improve productivity across creative and operational organizations while creating a foundation for broader enterprise AI adoption.

Leadership Insight

The success of enterprise AI is not determined by the sophistication of the model alone.

It depends on whether the technology solves a meaningful business problem, earns user confidence, fits existing workflows, and operates within clear security and governance standards.

By addressing accuracy and security concerns early, involving the right stakeholders, and proving value through real campaigns and production use cases, the platform evolved from an emerging idea into a trusted enterprise capability.

Executive Capabilities Demonstrated

Enterprise AI StrategyExecutive SponsorshipProduct StrategyEnterprise TransformationCross-Functional LeadershipAI GovernanceProduct & Vendor EvaluationWorkflow ArchitectureChange ManagementOrganizational AdoptionOperational ExcellenceStrategic Technology Leadership

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