Three significant limitations exist for enterprises at the functional level:
Timeliness Deficiency: Generative AI tools, being trained on outdated data, often fail to capture recent company news or the latest regulations. Consequently, they prove inadequate for tasks necessitating in-depth expertise or a nuanced understanding of business context.
Transparency Shortcomings: Tools like ChatGPT&Co. lack transparency as they do not furnish sources or citations for their responses, making it impossible to verify the accuracy of the information provided.
Accuracy Challenges: Generative AI tools are susceptible to hallucinations, where they may produce responses containing plausible yet inaccurate assertions. Relying on such responses for decision-making can potentially lead to severe consequences for your organization.
Moreover, enterprises encounter three data governance challenges:
Privacy and Security Risks: There is a heightened risk of leaks, unauthorized access, or misuse of sensitive information when employing Generative AI tools.
Regulatory Compliance: Ensuring compliance with regulations such as GDPR is essential when utilizing Generative AI within enterprises.
Integration with Existing Systems: The integration of Generative AI tools within corporate environments often necessitates customization to seamlessly align with existing systems and workflows.
In conclusion, selecting the appropriate AI framework will be pivotal in unlocking its full potential for your enterprise.
This is how Symantra resolves the 6 challenges on GenAI Trust & Accuracy.
Check Symantra's AI offering - Secure Artificial Intelligence for Enterprises. We create custom AI Chatbots for Enterprises trained on internal business knowledge and data.
