Digital transformation encompasses two main aspects. The first involves digitizing operations; transitioning from traditional paper-based and manual, local, human-dependent processes. The second aspect involves optimizing your digital infrastructure to establish intelligent workflows greatly improving overall organizational performance and scalability. By incorporating AI into both the beginning and end of their workflow, businesses can experience a significant advantage, accelerating propelling growth tenfold. As a key part of this strategy, Large Language Models (LLM) can play a pivotal role in bridging the gap between human input and machine comprehension, paving the way for more meaningful integration of AI into business workflows than was previously available to most, just a year ago.
Before the emergence of OpenAI's advanced Large Language Models (LLMs), the field of AI was fragmented into narrow areas of research, such as image creation, OCR, spell checkers, AlphaGo, and others. Despite some enthusiasts showing interest, the general public largely ignored it or simply found it intriguing. In essence, AI lacked the reliability needed to be integrated into everyday developer platforms. Whenever AI was mentioned, it often sparked discussions about doomsday scenarios, like the end of the world, Terminator, and Skynet. However, the focus of conversations has now shifted toward the impact of AI on jobs and the vast possibilities it presents. The real discourse revolves around how AI will revolutionize the user experience.
Consider a website the simplest form of a web application. It provides familiar navigation patterns that users have become accustomed to over the past 40 years. Users can easily understand how to navigate through the site, scroll down to skim articles, and move between different pages of interest. Although we track metrics like bounce rates and conversions, we often overlook the potential for enhancing this process. It is crucial to question why users should be limited to merely searching for content and evidence while navigating the website.
A large website for a school campus as an example. On such a website, there are distinct sections for a brochure and a web application. The brochure serves as a showcase for prospective students, parents, and teachers. In contrast, the web application focuses on utility, providing features such as event calendars, class schedules, policy documents, and various other essential information that users need to access. Certain information may require authentication to access, while other content is publicly available.
By utilizing a real-time training process to train an LLM using your own private organizational data, the university is able to develop a model capable of identifying a given user's intent and needs, whether through their written or spoken input or through other determinative analysis using implicit and explicit data about the user. Once the user's requirements are identified, the LLM is leveraged to deliver the relevant data or execute the required function back to satisfy the end user’s request. The LLM becomes the UI and user experience and information is more readily accessible than traditional website navigation methods.
In the proposed solution, by utilizing a standardized text and voice prompt, a LLM powered “chatbot” becomes the interface to the universe of your company or organization's data (or more likely a subset thereof). In this scenario, user inquiries are matched with relevant vectors within your dataset. The returned vector is then processed through the AI-powered LLM, enabling accurate answers with contextual understanding. The LLM can simplify, summarize, and even expand upon the provided text. Additionally, the LLM offers valuable functionalities such as text translation and transposition into different tones and languages, which further enhances its capabilities. This versatility is an added advantage of the LLM that traditional “AI-powered” chatbots have lacked.
In a standard web application workflow, AI can be utilized to leverage the reasoning capabilities of the LLM in order to determine the appropriate function or code to execute. When the user progresses through their workflow steps, an AI system can select a procedurally coded function that is expected to yield the desired results. The solution lies in mapping abstract concepts to specific functions, enabling the production of contextually rendered results based on the overall context and flow of the user's steps.
A website chatbot powered by AI revolutionizes the user experience by efficiently addressing inquiries that were traditionally deeply embedded within your website or knowledge-stack. It transforms the game by providing tailored responses to your users, utilizing your data effectively, and prompting them with appropriate calls to action, as well as engaging users in meaningful conversation. Calculations can be run from natural language requests, including transactional queries such as “what time does Chemistry 101 meet next Spring semester?” or “how much will it cost me to take twelve credits this year?”. LLMs can be trained to request more information from the user before returning a result to ensure the correct answer or provide the best answer to the given query and then ask for more detail to provide a more refined response, at the user’s choice.
Other notable results are enhancements to your internal application by incorporating an Extract, Transform, Load (ETL) system that enables effortless querying. For instance, you can request a table displaying the daily total profits and count of sold items from the vehicles department, grouped by item. The LLM will automatically interpret your query and generate the corresponding SQL code, rendering the desired table, without you ever writing a single query or knowing any commands. This empowers your analytics and development resources to focus on new insights and features by providing your entire organization with the capability to explore and analyze your growing universe of data.
ChatGPT / the SaaS-ification of LLMs has made AI’s role in business mainstream in a way like never before, providing a massive opportunity for the market to accelerate digital transformation, resulting in hyper-competitive and highly efficient organizations. In this world, information is more accessible, automation is taking the legwork out of knowledge work, and businesses stand to reap the benefits if they incorporate this technology.
One major obstacle is compiling, collating, filtering, and preparing an organization’s data for training LLMs, including classifying the data as public, private and accessible at which authorization level. Now is the time to incorporate LLMs, but first, compile and manicure your data, digitize your workflows and processes and make sure you are capturing all of the key data that your organization relies on to operate.
If data lives on local drives and files, with specific users within your organization, that needs to be fixed now. Develop a data warehouse or data lake, re-architect your workflows and get ready for mass adoption of AI or be disrupted. AI technology is being commoditized and now accessible to all, thus your differentiator is how you leverage this new technology. This is a function of the available data (to train/utilize in LLM models), which is tightly related to your current workflows and workflow design (including your underlying data models), including how you are utilizing your human resources within those workflows and capturing the resulting data they create.
The final determinant is your own creativity and that of those you can tap, in terms of devising all of the practical applications at the intersection of LLMs, AI (in general) and your best data. Talent and skills in these areas are in high demand and short supply, thus any and all means of acquiring talent and the creativity needed to harness AI within your organization should be a priority for virtually every business, industry and vertical. Start planning and executing now to avoid being too far behind the curve to effectively compete.