Implementing generative AI comes with a multitude of challenges for enterprises. They include identifying suitable use cases, vendor selection, and addressing ethical considerations. The concerns regarding privacy, content generation, copyright infringement, and data leakage add to the complexity. As a result, enterprises need multidisciplinary AI and Ethics teams to navigate these challenges and ensure responsible and ethical implementation of generative AI technologies, says GlobalData, a leading data and analytics company.
Rena Bhattacharyya, Chief Analyst and Practice Lead, Enterprise Technology & Services at GlobalData, says: “The mass appeal of gen AI lies in its potential to perform tasks that existing AI applications have not yet been able to master. The ability to write code, generate training data, or create natural sounding text opens the door to a range of potential horizontal and industry-specific applications yet to be discovered.”
GlobalData’s latest report, “Generative AI Watch: Gen AI’s Sweeping Impact on Enterprise Technology,” points out that enterprises will need to evaluate the pros and cons of the multitude of the large language models (LLMs) available today. They will need to decide which model will work best with their specific use case, whether they will need to use multiple LLMs depending on their applications, and how much customization will be required to make the model work for their use case(s).
Charlotte Dunlap, Research Director, Application Platforms at GlobalData, observes: “Increasingly, cloud giants are looking to woo low-coders and non-coders to amass a legion of developers devoted to building on their respective platforms including Azure, AWS, and GCP. Advanced AI services represent the newest weapon in the cloud wars for eliminating complex baseline coding requirements and helping abstract underlying configuration necessary to deploy new app architectures into production.”
Adding to implementation challenges is the fact that Gen AI has its own unique set of hurdles related to ethics and responsible AI, beyond those encountered with previous AI deployments.
Bhattacharyya concludes: “The technology can be prone to “hallucinations,” in which it provides incorrect or misleading information. Unfortunately, the results are presented so authoritatively that it results in unearned confidence. Gen AI has also been under scrutiny because of allegations that it does not appropriately safeguard individual privacy, may generate inappropriate or malicious content, or inadvertently use copyrighted content illegally.
“Furthermore, enterprises are concerned about data leakage. Organizations looking to scale their use of AI to include Gen AI should implement multi-disciplinary AI and Ethics teams that evaluate new AI use cases and ensure they adhere to corporate ethical standards.”
