Data & Analytics professionals are on the cutting edge of fine-tuning and applying foundation models for #GenerativeAI use cases to drive business impact within our companies. Recent advances include self-service bots with better resolution and containment rates, and marketing content creation with lower costs, higher performance and more scalable personalization.

But have we turned the power of Generative AI on our own Data & Analytics organizations?

3 near-term use cases demand our attention if we are to eat our own GenAI dog food:

1. Building New Master Datasets. Generative AI models can help analyze, clean and label data from disparate sources in consistent, scalable ways – especially when the sources mix structured and unstructured data (e.g., customer interaction logs, employee performance management systems).

2. Reducing Code Development Time. Generative AI models can draft initial code libraries, help de-bug code, and establish common frameworks and syntax.

3. Accelerating Self-Service. With some prep work (see Building New Master Datasets above), Generative AI models can classify, codify, access and return the right assets and instructions to enable faster and broader self-service adoption within our client organizations.

We’ll have an easier time evangelizing the potential of Generative AI to transform our businesses more broadly when we demonstrate the impact it’s had within our own organizations.

#AI, #CDO, #AnalyticsForImpact