Key skills companies will need to adopt generative AI

• Technical skills like model fine-tuning, vector database administration, prompt engineering, and context engineering. These are skills existing data scientists, ML engineers, etc. can learn over 2-3 months.
• Design Thinking skills to determine where to focus Gen AI solutions.
• Contextual understanding to ensure high-quality, relevant answers from models.
• Collaboration skills to work with domain experts to test, validate, and curate model outputs.
• Forensic skills to debug issues with models – is it the data, prompts, metadata, etc.
• Anticipation skills to plan for outcomes and build appropriate tracking into code.
• Skills to build trust in models via accuracy, explainability, consistency.
• Skills to optimize data infrastructure and costs, focusing on unstructured data.
• Skills to build reusable components and standardize assets like prompts and contexts.
• Risk management skills to address expanded issues like bias, IP, job displacement.

The key is combining strong technical skills with soft skills like design, collaboration, and anticipation. Upskilling existing talent is crucial, as is establishing centralized teams to enable scaling through standards. Architecting for reuse and trust is critical.

The potential economic impact of adopting generative AI

Increased productivity and efficiency gains. By automating repetitive tasks and amplifying human capabilities, generative AI can significantly boost productivity and lower costs. This translates to improved profit margins and competitive advantage.

Full article at –> https://luigicongedo.substack.com/p/generative-ai-requires-deep-organizational