Artificial intelligence has turned every digital footprint into potential fuel for value creation. From news sites and marketplaces to platforms, utilities, and B2B SaaS, companies are discovering that their websites, archives, forums, telemetry, and user-generated content can be packaged, licensed, and leveraged to power AI models and insight products. This masterclass delivers the legal and commercial strategies to turn your digital content into an income stream - without giving away the crown jewels.

Your masterclass leaders, Ieuan Jolly and Paul Sarkis, have deep experience in this field and helped architect the earliest headline deals between major publishers and AI developers.  Since then, Ieuan has advised multiple corporates on first-of-their-kind licensing frameworks with leading AI platforms building LLMs, including OpenAI, Google, Amazon, Apple, Mistral, Perplexity – transactions for which Ieuan was awarded Financial Times’ Most Innovative Lawyer Award USA. He is now advising several companies across industries on content licensing deals both with AI platforms as well as other interested parties and investors. Drawing on lessons from these deals, they’ll demystify how to price, protect, and productize digital assets for AI training, fine-tuning, and inference, while preserving brand equity, user trust, and regulatory compliance.

Scraping, training, and generative reuse have redrawn the value chain. If AI companies need high-quality content to build frontier models, content owners have new pricing power - provided they can identify, package, and permission their assets effectively. This session shows how to construct deals that pay today and compound tomorrow, from tiered licensing and usage controls to model-derived value sharing.

• How to inventory and package digital content for AI use cases. Move from “content” to “capabilities” with taxonomies, metadata, and access tiers that command premium pricing (e.g., training, fine-tuning, RAG, evaluation).
• The licensing toolbox. Build modular terms for permitted uses, safety constraints, territoriality, attribution, derivative rights, and model governance - plus verification, audit, and reporting mechanisms that actually work.
• Pricing and monetization models. Navigate fixed fees, usage-based royalties, outcome-linked payments, revenue share on model features, and attribution-driven media value.
• Protecting rights and enforcing boundaries. Design watermarking, fingerprinting, and API-based controls to curb unauthorized scraping and ensure compliant downstream use.
• Regulatory, IP, and ethical guardrails. Harmonize privacy, consumer protection, copyright, and competition considerations to expand addressable opportunities without chilling innovation.
• Future-proofing. Bake in re-opener rights, retraining triggers, model lineage disclosures, and safety updates so your deal remains accretive as models and regulations evolve.

• A practical framework to convert digital content into recurring revenue across training, tuning, retrieval-augmented generation, and evaluation pipelines.
• Risk controls that enable monetization at scale: provenance, consent alignment, data minimization, and model-safety levers.
• Real-world deal lessons: what publishers, platforms, and communities have secured—and how to avoid leaving money (and rights) on the table.

CIOs, CDOs, CROs, legal and compliance professionals, data leaders, content owners and platform operators, business development and product executives, and anyone responsible for monetizing digital content, data, or user-generated assets in AI-enabled products.