AI remains at the forefront of the minds of business and industry leaders, and the sector continues to witness significant investment and funding. An interesting piece of information is that recently Amazon has revamped Alexa using generative AI Large Language Model (LLM) methods to substantially enhance its understanding of conversational context and improve responses.
For enterprises, encompassing large businesses and organizations, AI advancements are laying the foundation for an extensive and intricate period of "AI business transformation". This transformation involves updating technology stacks, introducing new processes, modernizing policies, and more. While this shift will lead to the creation of new jobs, it will also render many existing roles redundant. In essence, similar to transformations in the past, such as e-commerce or social media, every company is about to become an AI company.
Nonetheless, the AI business transformation could surpass some of its predecessors in significance. Consider the modern bank ATM, which automated various bank services and notably reduced the requirement for human bank tellers. AI has the potential to automate even more aspects of the customer experience, particularly through the utilization of advanced large language model technologies. These technologies could replace current automated phone services, creating an experience that feels like interacting with a live human, despite being an automated process.
The Enterprise LLM
The Enterprise LLM, or a company's customized ChatGPT, is emerging as organizations develop large language models. Unlike ChatGPT, which uses deep learning on web content, an enterprise LLM is based on a company's data. This means that when interacting through various channels like phone, website, chat, or app, you engage with the company's large language model. It might already know your identity, history, purchases, and potential preferences. Building an enterprise LLM involves investing in new tech infrastructure and reprioritizing initiatives, including a secure cloud service, a deep learning model, data sources, and an experiential layer for user interaction.
Hyper-personalized experiences, driven by generative AI, are increasingly integrated into the consumer journey. Auto retailer Carvana, under the initiative "Joyride," explored the fusion of key customer data like the buyer's name, purchased car details, and transaction specifics. Leveraging this information, they crafted thousands of unique content pieces, narrating individual stories derived from the provided data.
Some of the world's leading consultancies have successfully developed and are further enhancing their proprietary enterprise LLMs. McKinsey, for instance, has introduced its proprietary LLM called "Lilli", currently undergoing testing by employees and expected to be more widely available this yearl. The user interface of Lilli resembles other public-facing text-to-text-based generative AI tools like OpenAI's ChatGPT and Anthropic's Claude 2, featuring a text entry box at the bottom for user queries and responses generated in a chronological chat format above.
What sets proprietary LLMs apart from "public" LLM products is their ability to access specific data not publicly available to AI web crawlers, such as those used by OpenAI. Companies are actively deploying measures, like coding on their websites, to block these crawlers, designed exclusively for gathering publicly accessible information. Many companies already possess valuable customer data integrated into the customer experience but not utilized within an LLM. Nonetheless, any existing interface providing customer data holds the potential to be incorporated into an organization's custom-built large language model. Similar to the ubiquity of apps, a company can utilize an LLM to interface with customers as AI assistants, guides, customer service agents, and more.
Transforming Customer Experiences with AI Conversations
We're familiar with Graphic User Interfaces (GUI) used on phones and kiosks. LLMs introduce another prominent interface, the Conversational User Interface (CUI). Presto, collaborating with fast-food chains, automates the drive-through using AI. Customers interact with an AI worker via voice while monitoring orders on a screen, showcasing the synergy between GUI and CUI. Or Kiki, a LLM developed by Zalo AI, integrated with Zing MP3, revolutionizes your music experience. You can easily play your favorite songs by requesting them or adding them to your collection. In the driving seat, Kiki ensures safe travels by assisting with restaurant searches, traffic updates, news, and texts, all without distraction. As AI evolves, future conversational experiences will feel more natural.
Inevitably, most companies will adopt their proprietary LLM, whether complemented, co-built with a public LLM, or existing independently. Proprietary enterprise LLMs will serve as the cornerstone for companies to elevate customer and brand experiences into the conversational and hyper-personalized domain. Engaging with a company will resemble a friendly conversation with someone well-versed in your details from the company's data. Alternatively, for those seeking respite from screen fatigue, a company's LLM could transform the customer experience into a pleasant chat.
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