CEO QuickStart: bringing AI-enabled search to the enterprise
Summary
Across industries, organizations are integrating AI solutions, whether through partnerships with consulting firms or technology vendors. A select few have discovered unique applications of AI, providing them with a competitive edge they prefer to keep under wraps. Meanwhile, others are still in the exploratory phase, seeking their unique applications. A common concern is the potential compromise of intellectual property and institutional knowledge. This guide provides a roadmap for leveraging AI effectively and securely.
The need is known, but the how is confusing.
When speaking with companies about AI and associated use cases, my first question is about how they are currently getting internal data to the AI, or how they plan to do so. Simple fact, your IT team has done an amazing job of building firewalls and instituting security protocols. Consumer AI solutions like OpenAI ChatGPT (the AI interface) and the corresponding Large Language Model (the AI brain) do not know anything about your business. Keep it that way.
It starts with data.
What is the cornerstone of success in Generative AI? The answer lies in data, specifically, your proprietary data. This data likely resides with a reputable cloud service provider such as Microsoft Azure, Google Cloud, Amazon AWS, or within applications like Salesforce, Office 365, ServiceNow, and Slack, among others. Blending this disparate data together is the foundation on which AI applications for the enterprise are built. These AI applications, or ‘systems of intelligence’, harness AI to empower users and decision-makers with sustained awareness of what the business “knows” and how to leverage it.
AI in layman’s terms with none of that inside jargon
To demystify AI, let’s start with the simple concept of a book index, which is a list of words or phrases and their corresponding page numbers. A search index operates on a similar principle, compiling a list of words and their locations, but on a vastly larger scale. Instead of providing access to the knowledge contained in a single book, it offers access to the global knowledge available on the internet.
Due to the immense scale of the search index, patterns begin to emerge in the words, such as context, common associations, and meaning, irrespective of the language. Given this large scale and the associated language patterns or relationships, they can be modeled to understand their usage. In AI speak, this is known as a Large Language Model (LLM), the ‘brain’ of the AI. When you interact with an AI interface, such as ChatGPT, and pose a question (or ‘prompt’), it generates a response based on the patterns it learned during the model’s creation. If your company hasn’t already done so, it will eventually develop its own LLM that will understand your business in your language.
AI du jour is French for vendor confusion.
AI can be a complex domain to comprehend and even more challenging to implement within a business context. This complexity is often exacerbated by vendors offering unnecessarily intricate and costly solutions. As previously mentioned, AI is inherently oblivious to your business’s internal operations. Top-tier organizations are employing Retrieval Augmented Generation to educate AI on your proprietary data. This methodology is recommended for most organizations looking to integrate AI into their enterprise.
Learning from other mistakes
The two predominant methodologies for implementing Retrieval Augmented Generation in the enterprise are predictive and real-time. Predictive models necessitate the migration of substantial data volumes from various silos, typically into a vector database. This approach is particularly effective for call centers and the development of customer service chatbots, given the relatively static nature of the data and the lower risk associated with information security. However, organizations that have tried vector database AI models have discovered that updating information and upholding security protocols can be complex and expensive. This was particularly true for companies where information is continuously updated.
Conversely, real-time models offer a competitive edge by preventing the need to create multiple copies of the same data, allowing the host application to manage security and access while ensuring the most current information is available for Generative AI.
The need is understood, and the how is easy.
To seamlessly integrate internal and external repositories for creating intelligence systems, you should consider Swirl, a solution designed for your Azure private cloud. Swirl empowers enterprise users to swiftly access and leverage data, applications, and insights via a user-friendly search interface. Swirl ensures secure deployment in your Azure private cloud environment and qualifies for Microsoft Azure Consumption Commitment (MACC).