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GenAI Driven Workflow Optimization: From Concept to Execution | Dr. Oliver Iff, Applied AI Stage
Discover the power of GenAI-driven workflow optimization, increasing productivity and efficiency, and learn how to implement agents, large language models, and foundation models to automate tasks and improve decision-making.
- GenAI-driven workflow optimization can increase productivity and efficiency up to 80%.
- Implementing agents for specific domains reduces the burden of knowledge on employees and automates tasks where possible.
- Combining a multi-agent framework with a conversational large language model enables the architecture to answer complex questions accurately.
- Foundation models are flexible and can be swapped out depending on the use case, allowing for easy adaptation to changing governance and compliance requirements.
- The agent framework can be extended to incorporate multiple LLMs, enabling the architecture to handle complex queries.
- By using a conversational LLM, clients can have a conversation with the system to retrieve the processes they need.
- The system can generate dashboards and graphs to visualize KPIs and provide insights to stakeholders.
- The architecture is scalable and can be adapted to small or large organizations.
- The agent framework can be extended to incorporate different LLMs, allowing for easy adaptation to changing requirements.
- GenAI-driven workflow optimization can help reduce the need for manual processing and improve decision-making.
- Using Watson XAI, clients can access a model catalog and analyze the behavior of their models during deployment.
- Agents can be trained to generate specific code based on user input.
- Foundation models can be used to generate specific data formats, such as JSON.