Data is the cornerstone of AI, and when combined with domain-specific industry information, it gives models power. This is according to Yasmeen Ahmad, managing director of strategy and outbound product management for data, analytics, and AI at Google Cloud. Enterprises can now tap into data they’ve never accessed before, enabling them to be more creative, efficient, and inclusive. With the ability to reach all corners of their organization and engage in new ways, companies are experiencing a transformative impact.
Training AI models on specific enterprise domains requires two techniques: fine-tuning and retrieval augmented generation (RAG). Fine-tuning teaches Language and Learning Models (LLMs) the language of a business, while RAG allows the model to have real-time connections to data in documents, databases, and other sources. This real-time capability is vital for accurate answers in financial analytics, risk analytics, and other applications.
Moreover, the true power of LLMs lies in their multimodal capabilities. Enterprises often deal with unstructured data like text documents, images, and videos, which account for 80 to 90% of their data. Having an LLM that can tap into this multimodal data is highly valuable. In fact, Google conducted a study that showed a 20 to 30% improvement in customer experience when multimodal data was used. LLMs can understand the complexity of organizations by having access to all types of data, rather than relying solely on simple pattern recognition.
Traditional organizations face challenges with their data foundations that were not designed to handle multimodal data. To meet the demands of AI and business data, a new kind of AI foundation is needed. This foundation must be conversational and act as a personal data sidekick. LLMs should be able to engage in question-answer interactions and have coherent conversations like humans do during analysis. Moving away from isolated, single-shot interactions, the industry is embracing the next generation of conversational AI. This AI acts as a tireless worker, capable of asking questions, engaging in a chain of thought, and providing query transparency.
LLMs are becoming more sophisticated and starting to mimic human brains. They can break tasks into subtasks, be strategic thinkers, understand cause and effect, and even learn honesty. The speed at which LLMs can perform these tasks is improving constantly with real-time capabilities.
In conclusion, the future of AI is here, and it is spawning new breeds of business. The possibilities are endless, and we are just scratching the surface of what this technology can enable. With data at the core and conversational AI as a personal data sidekick, enterprises can unlock new levels of creativity, efficiency, and inclusivity. The potential for AI to transform industries is immense, and we are only at the beginning of this transformative journey.
