How AI's Long Tail Will Benefit Small Businesses
In April of 2022, Andrew Ng, Founder and CEO of Landing AI, gave a Ted Talk titled How AI Could Empower Any Business, in which he describes what he has termed the “Long tail customization problem” of bringing AI into progressively more specialized applications.
Now that AI is in wide use within very large organizations and applications, the term “Long Tail” is being used to describe emerging and future markets for AI use. In this usage, the products or content are not the long tail, but rather the types of businesses that could potentially use AI, are themselves the Long Tail. Using AI for these emerging markets may be where Data-centric AI comes in.
A small convenience store, with limited shelf space, is not an example of long tail marketing, they only stock items that will have mass appeal, but that business segment itself, and their needs, represents the long tail of AI use.
Andrew Ng compares the rise of AI to the rise of literacy:
“When I think about the rise of AI, I’m reminded by the rise of literacy. A few hundred years ago, many people in society thought that maybe not everyone needed to be able to read and write. Fortunately, it was since figured out that we can build a much richer society if lots of people can read and write.”
Why the Emerging Frontier for AI will be Small Business.
In his TED talk, Andrew Ng gives an example of a pizza shop, and that a small amount of sales data may be used to generate helpful advice that could yield the business a few more thousands of dollars per year.
“So, in the AI world, we've figured out how to hire dozens or hundreds of machine learning engineers to build one giant monolithic system to serve millions, hundreds of millions, or even billions of users. But once we go into other industries, I see a lot of projects that are maybe worth $1 million to $5 million each, everything from a pizza chain wanting to do full demand forecasting, or a t-shirt manufacturer wanting to improve product placements, or how to do better quality control in automotive manufacturing.”
How will AI use Filter Down to Small Businesses in the Future?
In most cases the actual AI software will be pre-existing, and the challenge will be to provide it the relevant data for the domain of a business new to implementing AI.
“Most people get a data set from somewhere and then have a team write code and focus on improving the software, but this turns out to be difficult. But with the data-centric approach to AI, we’ve flipped this recipe on its head. We observed that for a lot of AI applications, the code is already a solved problem. There could be some open-source implementation of an AI model you can get from a vendor that works just fine. So instead, it's more fruitful to provide the tools for your teams to work on the data.”
“With data-centric AI, it’s about providing training to more people, but to subject matter experts rather than to machine learning engineers. That’s what my team is doing and what many others are trying to do in different application areas. I hope that these will provide a foundation with which we can give a lot more people access, and a lot more people the ability to build custom AI systems.”
How will small business owners end up inputting their data? Perhaps unintentionally.
I picture this happening as a series of specialized industry-specific software suites, with AI integrated into various functions.For example, the owner of a single location pizza place may never intentionally adopt AI, but at some point, AI may become so pervasive that something like the accounting software will eventually have functionality to identify sales trends, and convey how to act on them.
The future for AI in small business, may be based on software suites tailored to a business model. A single location dry cleaner may not make much use of technology, but there are many single location dry cleaners who might use the same tools if they were available, and maybe AI is not the selling point, but could come bundled into it.
The data centric approach is to make use of a pre-existing AI platform, which then needs to be trained on how a dry cleaning business works, in general, to be able to help the owner of the one location, specifically. I can't see the individual owner ever making that time commitment, but I can see a team of developers doing it for a program that would be used by many similar businesses.
Industry-specific, as opposed to client-specific, solutions have existed for some time. In this author’s experience as a software engineer, I once worked on a mobile app for customer self-service for Latin American mobile carriers, and in that case it was mostly a pre-existing application, somewhat customized for an individual carrier. Similarly, when I worked on a distance learning platform, it was basically a pre-existing solution being sold with minor customizations to different school systems.
Future adoption of AI for small business will most likely occur through business owners usingsoftware for their individual business, which was actually mostly pre-built with their entire industry in mind, and introduces AI trend detection as a value adding feature.
