March 2024
Artificial intelligence (AI) has long been at the forefront of cutting-edge technology. Since the creation of the transformer architecture in 2017, there was little attention paid to generative AI (Gen AI), with the exception of bleeding-edge ML/AI technologists, until consumer-facing ChatGPT burst onto the scene in November 2022. Since then, there has been a Cambrian explosion of generative AI (Gen AI) startups launching, trying to vie for pole position in the AI arms race. One subfield that is gaining some attention is Vertical AI (vAI).
To start, it is important to highlight what vAI is within the vast AI taxonomy. AI is a broad term that is often used to encompass many techniques for simulating intelligence: machine learning (ML), natural language processing (NLP), machine perception (i.e., computer vision (CV)), etc. Within the ML spectrum, we can broadly think about two types of AI: Generative and Predictive. I’ll let Dr. Ali Arsanjani of Google AI explain:
Predictive AI and Generative AI
Generative AI and predictive AI are two different types of artificial intelligence (AI) that are used for different purposes. Generative AI is used to create new content, such as music, images, and texts, while predictive AI is used for clustering, classification and regression that often relies on supervised learning and historical training sets that create models for predictions about future states or events. For example, a predictive AI model that is trained on a dataset of historical data about the stock market may be able to make predictions about the future prices of stocks.
Generative AI models are built by training on a large dataset of general examples (such as wikipedia, commoncrawl, etc) and then using that knowledge to generate new examples that are similar to the training data. So the idea is to generate new data. A generative AI model that is trained on a dataset of images of cats may be able to generate new images of cats that are similar to the training data.
Vertical AI is not a separate classification - it can be both predictive or generative. vAI takes a more specialized approach, focusing on specific sectors or use cases, developing and applying AI to solve problems within a specific industry vertical, such as healthcare, finance, or retail, where a specific set of challenges and problems exist.
The utility of Vertical AI arises from its focus on specificity — there are unique workflows and pain points that users face in those industries, and by leveraging industry-specific data, vAI can provide solutions that are specially tailored to the unique challenges and opportunities of that sector. It's not attempting to be a Jack of All Trades, but rather aims to be a master of one. This focus allows for the development of solutions that are not only effective but also efficient, maximizing the potential of AI within a given realm.
Two types of companies are offering vAI. First, we have “traditional” vertical SaaS (vSaaS) companies like Mindbody, Procore, and Turns incorporating AI into their platforms.
Mindbody (vSaaS for fitness and wellness) has introduced Mindbody [ai], an assistant to the front desk to respond to missed calls, answer questions, and help clients make bookings.
Procore (vSaaS for construction) offers AI-powered analysis of construction financials.
Turns (vSaaS for laundry & dry cleaning) is helping owners use AI to augment their marketing to spur customer growth.