In this article, I'm going to discuss the topic of AI adoption. I'm going to talk about a couple of ways that organizations and companies can adopt AI more easily.
More specifically, I'm going to talk about two areas of machine learning which I believe can be very easily integrated into existing systems and infrastructures, independent of the industry or the company, and can very quickly add value.
Risks Behind AI Adoption
One of the challenges behind AI adoption is that there are significant risks and this makes many companies hesitant to adopt AI.
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One of the major risks is obviously the risk of hiring. If you hire the wrong person or people, then you will have incurred a huge waste of time and money.
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Another issue is that a company might not be ready for the adoption of AI in terms of the culture. Unless the senior executives buy into AI, but also the middle layers, then it's very unlikely that AI can work well.
And this is one of the topics that I've discussed and talked about a lot in my work, also in my book The Decision Makers Handbook to Data Science. And unless a company has the right culture for AI, it's very likely going to fail AI adoption. So that being said, companies might be hesitant to adopt AI, and then this creates a culture of fear and the problem perpetuates itself.
Some other risks are obviously the risks of breaking things, right? So, if you're a company that has been going on for 20 or 30 years, maybe you're running some legacy systems and rebuilding these systems in a way that they can integrate with AI.
Whether we're talking about a product or developing another platform internally in one way or another, there might not be a huge incentive to make these changes because you're afraid you must break things.
And this is why, if an organization is very defensive around the adoption of AI, it's very important to start with some use cases which are easy to understand, they're easy to implement, and the management, upper management, and mid-level management can easily understand and they support.
That's why, unfortunately, many AI initiatives fail; whether we're talking about products or services because, sometimes, service providers or product developers pitch ideas. Companies that might seem a little bit too far out there.
Maybe the valuation is not clear, or maybe adopting these technologies would require a significant change in the culture or the infrastructure layer of a company.
Two Types of Technologies
There are two types of technologies that I believe can very easily add value without much effort. And these two technologies are:
Anomaly Detection
The term anomaly detection refers to a class of methods and algorithms in machine learning which are used to identify points that are, in a sense, unusual. These are points that do not seem to follow the regular patterns of behavior.
And the thing with anomaly detection is that it can be very useful in contexts like fraud but can also be used in other domains like manufacturing. You can use anomaly detection to detect whether some machine is not behaving as it should and then act against this.
Maybe this machine is about to break down, or similarly in retail, maybe you want to use anomaly detection to detect spikes in demand or to detect issues with the supply chain.
The benefits of anomaly detection are that it doesn't really require that much to integrate with existing systems. In many cases, you can just take data as they are and it's very easy to just feed them into algorithms.
And the thing is those anomaly detection algorithms, require fine-tuning, but they do not require training.
So, one of the main roadblocks to using AI, in many cases, is the absence of what we call labeled data. So, these are data points that have a target variable.
So, if we're talking, for example, about images, then labeled data is data that has been labeled as “this is an image of a human”, “this is an image of an animal”, et cetera.
So, anomaly detection doesn't require labeled data, which means that it is much easier and faster to roll out than supervised learning. Obviously, supervised learning is still king in terms of the applications of machine learning, but even if it's king, it's not always feasible to use it.
That's why anomaly detection can be so powerful if we're talking about early AI adoption
Unsupervised Learning
On a similar note, the second family of techniques and methods that I’d like to discuss is unsupervised learning. Anomaly detection can also be seen as a subset of unsupervised learning.
But unsupervised learning also includes the topics and methods like clustering. And clustering is one of the most famous cases and applications of data science.
So, for example, user segmentation is a very famous application, or pretty much any kind of segmentation.
And I believe that the combination of dashboards, which can help an organization extract useful insight, and the addition of some basic unsupervised learning and clustering on this dashboard can be a very good way to educate the management of the uses of AI and help them better understand how AI can add value in an organization.
Again, the benefit of using unsupervised learning is that you don't really require labeled data, you only need data that is structured in one way or another. But obviously, in some cases, you can also deal with structured data like text, as long as it's digitized.
But the important thing is, much like anomaly detection, you don't really need to spend time leveling cases.
So that's very powerful because you can get a useful system and, at the same, time you can get decision-makers within a company. They can get used to AI. And this is going to obviously help the gradual adoption and improve the understanding of what AI can do and how it can be used.
Therefore, if a company is quite risk averse in that sense, I think that using these techniques, these methods can be a very good way to start with AI adoption.
Conclusion - The Best Option for AI Adoption
There are many other ways to do AI adoption. And in this article, I'm just referring to two methods that can be used by risk-averse organizations. Obviously, if an organization wants to move faster, then the best option by far is to go for supervised learning.