The rise of generative AI (GenAI) felt like a watershed moment for enterprises looking to drive exponential growth with its transformative potential. However, this enthusiasm may be tempered by a host of challenges and risks stemming from scaling GenAI. As the technology subsists on data, customer trust and their confidential information are at stake—and enterprises cannot afford to overlook its pitfalls. Yet, it is the quality of the data that will determine how efficient and valuable GenAI initiatives will be for organizations. For these data to be utilized effectively, the right mix of skills, budget, and resources is necessary to derive the best outcomes. Such data also has to be placed in environments, be it private or public clouds, that can meet both business requirements and technical needs. In light of these considerations, it has become a growing imperative for business and IT teams to collaborate and align their business priorities for AI use. How will organizations wield AI to seize greater opportunities, engage employees, and drive secure access without compromising data integrity and compliance? These are vital concerns that companies must address and communicate across every level of the business. While it may sound simplistic, the first step towards managing high-quality data and right-sizing AI is defining the GenAI use cases for your business. Depending on your needs, large language models (LLMs) may not be necessary for your operations, since they are trained on massive amounts of text and are largely for general use. As a result, they may not be the most cost-efficient AI model to adopt, as they can be extremely compute-intensive.

Full opinion : The success of GenAI models lies in your data management strategy.