What is AI as a service?
While ‘as a service' products including software and infrastructure are common within the technology industry, Artificial Intelligence as a service, or AIaaS, as a concept, is still relatively new.
AI as a Service is a term used to describe a third party that provides advanced AI functionalities to companies upon a one-time payment or subscription fee. It’s not an understatement to say that it’s a game-changer for many small-to-medium businesses.
Up until recently, many companies were priced out of using Artificial Intelligence for their business, as it would have required in-house development of systems with human-like qualities, such as reasoning, thinking, and learning. With AIaaS, it has now become more attainable than ever before, allowing companies to leverage Artificial Intelligence for things such as customer service, data analysis, and automating production.
A word of caution, however, before we proceed - AI is more and more commonly used as a marketing tactic by service operators, and there is a great deal of ambiguity on different definitions.
The benefits of using AIaaS platforms
No need for sophisticated tech skills
AIaaS is accessible to even those who do not have an AI-skilled programmer on board - just add a layer of no-code infrastructure to the game. Companies that truly provide AIaaS often do not require any coding or tech skills at any point in the setup process. In a piece for Forbes, Daniel Newman of Broadsuite Media Group, aptly notices that “in a time when there’s a shortage of AI experts and ever-increasing competition in the marketplace, that’s a huge workaround”.
On this note, it’s important to underline that while some AIaaS solutions do not require any coding skills – the level of implementation complexity varies a great deal when we enter the legacy software world.
Advanced infrastructure - and fast
Before AIaaS, strong and fast GPUs were required to run successful AI and machine learning models. Most SMEs don't have the resources and time to develop software in-house.
In the world of AI, there are a few rules of thumb - one of them is that your model will only perform well in doing a task if the data it's been fed is of good quality. AIaaS being customizable, will offer the opportunity to build a specific task-oriented model on top of the abundance of data most organizations sit on top of already.
“The worldwide AIaaS market will be valued at just a shade under $11 billion by the end of 2023, with the 2017-2023 CAGR hovering around the 49% mark.” - Hussain Fakhruddin, the CEO of Teksmobile
Not only does AIaaS give you access to AI while cutting back on non-value-added labor, but there is also a significant amount of transparency to it. AIaaS allows you to pay per usage - machine learning requires a great deal of computing power, but most pricing models focus on usage.
Also, some platforms allow the user to have a bit more control over AI automation.
The way to go about it - adding human in the loop as an option. HITL is a continuous feedback loop where the process owners give AI feedback in edge cases. The feature aims to achieve what neither a human being nor a machine can achieve on their own.
Let's be frank: most as-a-service platforms are not as user-friendly as they'd like to make it sound. Although many of the AI options are open-sourced, meaning that they can be downloaded, modified, and used freely, they can be challenging to install and develop. AIaaS, on the other hand, in most cases is completely ready for use. Process owners can utilize AI software without any formal training.
End-to-end ML services include both pre-built models and custom-created models - as well as drag-and-drop interfaces for reduced complexity. The cool thing about this? Getting your ML project started within hours without engineers.
Ever heard of an organization that as they grow receives fewer emails? Yeah, neither have we.
AIaaS is built for scaling. If you've trained your model to classify your info@ inbox based on email urgency or sentiment, and funneling the right emails to the right person, you're already ahead of the game.
AIaaS is perfect for performing tasks that require some level of cognitive judgment, but where the task itself is not value-adding.
Types of AI as a Service platforms and the problems they solve
If you're here, chances are you're probably looking for a specific tool already, so let’s shed some light on the most common types on the market.
Nowadays, regardless of whether you search the web for anything from government websites to clothing stores, you likely come across bots – particularly, their most common type, i.e., chatbots.
They use natural language processing (NPL) algorithms to emulate natural conversations between humans. These types of bots are used primarily for customer service and provide relevant answers to customers’ most recurring questions. As they respond on a 24/7 basis, they save time and resources, allowing employees to focus their time on more complex tasks. In fact, one of Europe’s fastest-growing parcel companies, InPost, has recently reported that they automate as many as 92% of the millions of customer conversations they handle each year through leveraging a chatbot.
An API (Application Programming Interface) is the software “middle-man” which allows two applications to communicate with one another. An example of this would be a third-party airline booking website such as Expedia, CheapOair, or kayak, which all pull information off of a collection of airline databases to present all their deals in one place, in a readable manner. Common uses for APIs also include:
- Natural Language Processing (e.g. sentiment or urgency analysis)
- Computer vision
- Conversational AI
Machine Learning (ML) is used by companies to analyze and find patterns within their data. As a result, it makes predictions that they weren’t specifically taught to do, learning as the process evolves. This method of data analysis is meant to be run with little to no human intervention. In AIaaS, companies can manage Machine Learning without having any particular technical expertise - there are tons of solutions ranging from pre-trained models to building one to perform a custom task (just don't forget the rule of thumb!).
Data labeling is essentially annotating your large quantities of data so that it can be organized efficiently. It has a wide range of use cases – i.e., assuring data quality, categorizing it by size, and further training your AI, to name a few. In the case of the latter, human-in-the-loop (which we mentioned earlier in this post) is used to label data so that it can be easily evaluated by AI in the future.
Data classification is when data gets tagged under one or more categories. The classifications usually include content-based, context-based, and user-based. With the use of Artificial Intelligence, data can be classified on a larger scale, provided that a data classification outline and criteria are clearly defined. Here’s a great visual example of how data classification can be used by businesses:
Examples of AIaaS vendors
When it's time to decide, it's all about what the major pain points and bottlenecks in your processes are.
As mentioned at the beginning of this piece, big players such as Google, Microsoft, and Amazon use AI to enhance their products by adding AI modules. Still, they can hardly be described as AIaaS, as their products are often:
- pre-trained models that focus on catch-all problems
- require a significant amount of technical knowledge for setup
- complex, expensive and non-transferrable
Here's where challengers come in. You're not going to need IBM Watson to bring clarity into your customer support tickets, or organize your inbox unless you're... well, IBM.
For this reason, AIaaS is a term best suited to describe smaller players, who have narrowed down their offering and focus on offering a service that truly provides out-of-the-box AI technology benefits. Below, we mention a few examples of such companies.
Odus is a chatbot service that provides automated messages and calls with a human-like AI. The chats are fully automated and can work entirely on their own. Odus has a user-friendly, no-code approach, making it simple for even those without tech skills to manage.
- Provides call and written communication automation
- Fast integration with a variety of other tools
- Offers complex scenarios with entities
A solution like Levity is, in essence, what AIaaS should be about – to get started, you define your workflow, upload sample data, train the custom AI, and let it learn and refine itself. Initially, the tool will ask you for input when it’s uncertain of how to tackle an issue, but it’s designed to relieve you of mundane work over time.
While some basic AI knowledge will be useful, Levity doesn’t require you to be an expert in the field. In fact, the tool aims “to deliver an intuitive and guided experience in all aspects – including what you need to know about AI.
- Fully customizable document, image and text classification
- Integrates with hundreds of tools
- Human-in-the-loop feature for handling edge cases
Viz.ai is a medical imaging company based in San Francisco, California, that specializes in applying Artificial Intelligence in healthcare. The company uses advanced deep learning to quickly relay information about stroke patients to specialists who can treat them.
They have also introduced a module that helps control the influx of patients during COVID-19, allowing medical professionals to rank the level of severity for each patient. As a result, their solution enables a better flow of patients and provides a safer workplace for staff.
- Improving patient management
- Safer environment for medical staff and patients during COVID-19
- Reducing time to treatment and time to diagnosis
Before going all-in on implementing a solution, ask yourself the following questions:
- Can I write clear rules for my process? If the answer is yes, AI might not be a good fit for your automation plan.
- Does the potential vendor provide the option to test the product (and API)? You'll want to test the product with your own data, but any AIaaS platform should be able to answer your data security questions in a transparent manner.
- Does the product have a secure API? Again, before deciding on a vendor, it is a good idea to check any data compliance rules you may have internally - as well as the vendor's SOC 2 credentials.
AIaaS is the result of the combination of the ‘as a service’ model and the advancements in Artificial Intelligence usage in recent years - it makes this technology accessible for anyone.
While, in the past, AI was already leveraged in automation solutions from major companies like IBM, Microsoft, Google, and Amazon, for many businesses, they had too steep of a learning curve and were too complex to provide tangible results. It is therefore no understatement, that the rise of AIaaS truly opens up a world of opportunities for SMEs.
Wondering what is the benefit of using AIaaS in an industry like yours - or want to do a little sanity check? Reach out – we’ll happily discuss your business use cases and see how our solution can help!