Are you a consultant helping clients adopt Generative AI use cases?
Below are the million $ (dollar) insight that helps bring clarity where to focus your efforts and time to seek value creation through GenAI use cases.
- Don't ideate for GenAI use cases where being accurate or full correctness (or sometimes even partial correctness) matters. For example, In healthcare diagnosis or investment decisions where human stakes are high and can potentially lead to catastrophic effect in the long term.
- Shape your use cases where GenAI will augment and improve human productivity and enhance creativity that can be interpreted and validated with domain experts faster and backed by true facts. For example, Large Language Models powered Chatbots when custom tuned for FAQ response generation or search based on internal documents representing enterprise information. Other example could be generating a product description from a list of large product catalog/inventory in an automated fashion etc.
- Strictly avoid use cases where Data and AI literacy of the end users can't be managed in a progressive and transparent manner. For example, A semiconductor engineer leveraging GenAI for programmable chips without having understanding and know-how of Electronic vehicles (EV components) data. Moreover, semiconductor as an industry needs more accurate and precision response where AI use cases seems limited in the current state of "GenAI" as a technology. For more insights, checkout this post: https://www.dhirubhai.net/pulse/sluggish-adoption-ai-semiconductors-inzone-ai/GenAI holds immense promise, but its adoption must align with context, expertise, and transparency. Let’s build a future where GenAI enhances human capabilities, backed by true facts and domain expertise.Feel free to chime in and share your insights in identifying and building generative ai use cases.
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