Introducing AI in the Workplace — Employees and Operations
Explore how employee acceptance and competence drive successful AI transformation, with practical strategies for building AI readiness through targeted training and cultural change.
Introducing AI in the Workplace — Software, Technology, and IT Implementation
An excerpt from the research work by Axel Wiemann (kiucon), Patrick Helmig (Omnifact), Sebastian Hennerici (Omnifact Academy), and Sven-Erik Holm (GvW Graf von Westphalen).
Self-hosting LLMs empowers businesses with enhanced data privacy, security, and regulatory compliance by maintaining complete control over their data and infrastructure. This approach also allows for customization and optimization of AI models to meet specific business needs, offering greater flexibility and potential long-term cost benefits compared to relying on external AI providers.
Main Points
- Maintain complete data control and privacy by self-hosting LLMs, eliminating reliance on external AI providers and mitigating data exposure risks.
- Benefit from accessible and powerful AI with recent advancements in open-source models and efficient hardware, making on-premise solutions feasible for businesses of all sizes.
- Maximize AI's potential with customized, self-hosted LLMs, optimizing performance for specific needs and achieving greater efficiency and cost savings over time
- Enhance your data security and regulatory compliance with self-hosted LLMs, reducing external dependencies and ensuring adherence to evolving legal requirements. Seamlessly integrate AI into your operations by incorporating self-hosted LLMs into existing workflows and systems, unlocking a wide range of AI-driven use cases.
Technical Architecture
Generative AI
Since 2021, generative AI models have seen unprecedented growth. Trained on massive datasets, these models can now understand natural language, generate contextually relevant responses, and handle complex tasks. Two key benchmarks illustrate this progress:
Solving World Problems (SWE): This benchmark measures AI's ability to solve complex programming tasks. Success rates jumped from 1% in early 2023 to over 40% by the end of 2024. Massive Multitask Language Understanding (MMLU): This benchmark tests AI language models across 57 academic and professional domains using university-level multiple-choice questions. Success rates climbed from 60% to over 90% in just 18 months.
This rapid advancement directly impacts practical applications. Use cases deemed impossible six months ago are now readily achievable. Companies must regularly reassess potential AI applications. The question is no longer if AI can support or automate processes, but when.
About the Author
Patrick Helmig, CEO of Omnifact, brings over ten years of experience in developing AI solutions for regulated industries and leads Omnifact with the goal of enabling secure and user-friendly AI integration for organizations.