Our recommendations: Work with a trusted AI vendor that provides meticulous management of both AI inputs and outputs. Your data should be masked when shared with any large language models. When considering a vendor-hosted or external model, ensure that the context of inputs and prompts will not be stored and you never lose control over the use of your data. Beyond privacy and security concerns, prompts and outputs should be automatically scanned for harmful outputs. Finally, depending on the AI use case, consider keeping a human in the loop to ensure quality, accuracy, and trust.
3. Make space for education and upskilling
As AI becomes embedded in organizations, nearly half of survey afghanistan phone number list respondents agreed that continuous upskilling is necessary. This, paired with the general lack of understanding around AI concepts, highlights the opportunity for a thoughtful approach to AI education. Additionally, they noted that a lack of data skills is a primary challenge with the current use of their CRM systems.
ensuring employees have a general understanding of your AI strategy and goals, start tactically with employee training to create and refine prompts. A prompt is detailed instruction provided to a large language model to help it generate an output. A better prompt yields a better, more relevant output from the AI model. Every employee can be a prompt engineer – this training will maximize the potential benefit of AI while helping people work more efficiently. In addition, establishing corporate policies that educate employees to evaluate AI outputs for accuracy, bias, toxicity, and potential harm is essential.
See how top leaders make the most of their AI investments by creating strong data practices, a culture of continuous learning, and unwavering trust. These insights and more can be found in the full study, so your team can create a foundation of excellence with your own AI-powered CRM.