Please note all these have different levels of complexity for implementation. of current infrastructure and teams comes into consideration.
Infrastructure
Even if organizations want to leverage generative AI for insights, they might not have the right data infrastructure and business processes in place when implementing AI for practical use. Data lebanon whatsapp number data quality issues might impact the output, or your organization’s systems and platforms might have unique needs and capabilities. What’s more, there might be specific training practices needed for success. As everyone knows, GenAI is prone to bias and hallucinations; if there are data quality or training issues, the output would be manifested incorrectly, to put it lightly.
Because of this, your organization should organize a team with some related expertise. A team consisting of business experts, engineers, and AI specialists would work. Obviously, you can’t expect people to have 10 years’ experience with generative AI, but something close to that – such as people on your data science team, with LLM or NLP or prompt engineering experience – would be beneficial. Such expertise will be needed while transitioning from individual inquiries to production-level applications. Accuracy will be critical, and training on extensive data sets will become foundational.
One of the main reasons AI projects don’t take off is the lack of leadership, so you should also ensure someone can pilot the program.