You might ask: How big is our data set? Is it mostly structured or unstructured? How are we managing access to data in real time so we can contextualize insights for language learning models (LLMs)? place to maintain data privacy, accuracy, and governance? How organized is our data, and do we have a clear understanding – and offer an equally clear explanation –of our data pipelines? Is it easy to connect our GenAI tools with other platforms and plug-ins? Can our GenAI solution run denmark whatsapp number data anywhere (e.g., on-premises, hybrid, and cloud)?
Answering these practical questions reveals immediate areas of improvement because security and access are core tenets of effective GenAI models. This line of thinking also assesses whether your GenAI efforts are intentional and structured, or overly freeform and too risky. If you find your answers to these questions are less than satisfactory or even nonexistent, this is a strong indication that your organization needs greater AI strategy and oversight.
Has GenAI led to additional revenue or cost savings?
Next, explore GenAI’s impact on your organization’s bottom line. This is an area in which you may want to coordinate with leaders across departments to more fully understand how GenAI is helping to accelerate specific business goals.