World Congress 2026 Europe - Virtual Stage • Jul 2, 2026 • Session details

From Training to Adoption: Engineering a GenAI Learning Product That Sticks

Daniel Ambach

When GenAI frameworks update, static tutorials instantly break. Discover how Learning Ops uses CI/CD pipelines to treat curriculum like code, engineering dynamic training products that test themselves.

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#1 about 4 min

Overcoming outdated training materials with learning operations

Traditional onboarding documents and workshop materials quickly become obsolete without a lifecycle management strategy.

#2 about 3 min

Adapting generative AI content for industry relevance

Highly capable models enable scalable content creation but require ongoing updates to match specific roles, languages, and use cases.

#3 about 5 min

Treating learning curriculums as tested software artifacts

Applying principles from software engineering and machine learning operations creates versioned, tested, and deployable educational content.

#4 about 5 min

Handling knowledge drift and broken code examples

Rapid advancements in frameworks and model versions cause technical tutorials to break and require systematic patch releases.

#5 about 6 min

Automating the deployment of educational templates

A structured pipeline integrates markdown files, template logic, and rendering engines to deploy consistent formats across learning management systems.

#6 about 8 min

Customizing curriculums for diverse corporate profiles

Context libraries and structured inputs allow a single source of truth to generate specialized training paths for different sectors and roles.

#7 about 4 min

Migrating file systems to version-controlled content pipelines

Adopting a single source of truth with automated linting and validation ensures resilient and auditable training materials.

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Adapting content delivery for artificial intelligence consumption

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The generative AI application lifecycle

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Balancing AI regulation with technological innovation in human resources

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Retraining workforce staff for the artificial intelligence era

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Scaling generative AI use cases across large enterprises

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