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World Congress 2024 β€’ Aug 20, 2024 β€’ Session details

Bringing AI Everywhere

Stephan Gillich

Why are enterprises struggling to operationalize generative AI? Discover how hybrid architectures and automated workflows overcome infrastructure bottlenecks without compromising your private data.

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#1 about 2 min

Comparing the influence of AI to the internet

Artificial intelligence will reshape software and applications much faster than the internet did due to its software-first nature.

#2 about 2 min

Identifying barriers to enterprise generative AI production environments

Limited early production deployments highlight how infrastructure challenges and an absence of open choices hinder generative AI adoption.

#3 about 2 min

Three phases of artificial intelligence adoption within modern enterprises

Organizations scale internal intelligence by transitioning from isolated personal copilots to automating workflows and driving complex functions using domain data.

#4 about 2 min

Differences between secure enterprise environments and public AI models

Enterprise solutions require mature models, stringent security, and strict data locality compared to rapidly changing public implementations.

#5 about 4 min

Strategies for accelerating innovation and maximizing AI value

Overcoming varied computational demands requires a platform strategy that balances infrastructure total cost of ownership with long-term sustainability.

#6 about 4 min

Hardware architectures tailored for specific artificial intelligence computations

Handling diverse processing needs involves distributing operations across specialized machine learning accelerators and processors optimized for local inference.

#7 about 2 min

Processing workloads efficiently across hybrid artificial intelligence deployments

Distributing intensive computations between local client devices and cloud servers improves network latency while maintaining user data privacy.

#8 about 2 min

Building an open collaborative software stack for AI workloads

Preventing vendor lock-in involves adopting versatile middleware layers and open-source tools that optimize machine learning across diverse hardware ecosystems.

#9 about 5 min

Utilizing AI and hardware acceleration for application code optimization

Integrating specialized acceleration hardware with generative technologies empowers developers to automate tedious software refactoring and optimize mathematical systems efficiently.

#10 about 2 min

Safeguarding enterprise intelligence securely with retrieval-augmented generation

Protecting sensitive proprietary intelligence from external exposure requires seamlessly connecting large language models to securely hosted local vector databases.

#11 about 2 min

Simplifying AI deployments using architectural blueprints and reference implementations

Addressing fragmented infrastructure tooling involves leveraging standardized architectural blueprints and collaborative reference implementations for comprehensive enterprise deployments.

#12 about 2 min

Hardware security features necessary for compliance and responsible AI

Meeting strict regional regulatory mandates requires establishing foundational hardware trust services that continuously protect sensitive data throughout processing lifecycles.

Matching moments

3:13 min

Embedding generative AI in enterprise software platforms

Mike Butcher Mike Butcher +3 Β· WWC 2024

3:33 min

Crucial lessons for deploying generative AI in enterprises

Alexander Trusheim Alexander Trusheim +1 Β· WWC 2025

3:52 min

Establishing a structured framework for enterprise AI

Julian Joseph Β· LIVE

6:00 min

Navigating competition and infrastructure in enterprise AI

Ash Ryan Arnwine Ash Ryan Arnwine +3 Β· WWC 2024

54 sec

Overview of enterprise Java and generative AI

Timo Salm Timo Salm Β· WWC 2025

2:06 min

Financial impacts of generative AI across the enterprise

Thomas Schmidt Thomas Schmidt Β· WWC 2024

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