Synopsis
The latest advancements in Artificial Intelligence (AI), particularly the emergence of Generative AI (GenAI) and its Large Language Models (LLM), have sparked a new wave of business opportunities across various industries. Business leaders are eager to harness this new AI wave to enhance, automate, and accelerate their operations for the benefit of their businesses. Applications driven by GenAI are helping businesses to not only monetise existing revenue streams but also to create new ones.
However, adopting new technologies always comes with challenges. A methodical and streamlined approach is essential for the successful adoption of these technologies; otherwise, there could be significant negative impacts on the technology onboarding process, the time required for adoption, learning to implement, selecting the appropriate LLM models, and integrating these technologies into the business. It also involves identifying suitable use cases, references, best practices, and frameworks, as well as considering trials of the technologies, such as running proof of concepts, building MVP (Minimum Viable Product), and productionising GenAI applications.
We have had numerous discussions with our customers about these ongoing challenges and offering guidance throughout constantly shifting AI landscape.
In this series, we will provide approach to adopt “GenAI Application Deployment Strategy” drawing from our accumulated knowledge and field expertise.
Series:
Topic 1: How to identify the most impactful GenAI use case
Topic 2: Potential Use cases for GenAI Application
Topic 3: The Evolution of GenAI Application Deployment Strategy: Building Custom Co-Pilot (PoC)
Topic 4: The Evolution of GenAI Application Deployment Strategy: From PoC to MVP
Topic 5: The Evolution of GenAI Application Deployment Strategy: From MVP to Production
Topic 6: Value Base Delivery (VBD) to accelerate GenAI use case implementation
@Paolo Colecchia @Taonga_Banda @StephenRhodes @renbafa Morgan Gladwell
