AI & Machine Learning for Business
A non-technical introduction
The pace of AI innovation has been accelerating in recent years. While this has resulted in incredible new tools and technologies, it’s not entirely clear how we (i.e. professionals, entrepreneurs, and business operators) can use these innovations to drive value in business.
In this article, I provide a non-technical introduction to AI & Machine learning and share how they can fit into the way we do business. My goal here is to help those coming from non-AI/ML backgrounds make sense of these technologies by defining them in simple terms and sharing tips on how to start using them in practice.
What is AI?
When you think of AI, you might think of ChatGPT, AI-generated art, or maybe something like the Terminator. But let’s take a step back and ask the basic question, “What is AI?”
AI is short for artificial intelligence — which may not tell us much because one of these words is problematic.
The first word, artificial, is not the issue. It simply means something made by humans.
However, the second word, intelligence, is not well defined (even among AI researchers [1]). Nevertheless, a definition I like to use, and one that is most relevant in a business context, is intelligence = the ability to solve problems and make decisions.
Therefore, with this operational definition of intelligence, AI is simply a computer’s ability to solve problems and make decisions.
Intelligence in action
To get a better idea of what we mean by intelligence, let’s see it in action.
Suppose you are trying to decide whether to spend your Saturday by the pool or inside watching that new Netflix series. If you look out the window and see the scene in the image below, you may decide to stay in. That’s because the dark, cloudy sky is a good indicator that the weather won’t be great today.
As another example, consider the plot below, where sales data bounces up and down but then peaks in November. If asked, “What caused the peak?” we might reasonably say that it’s because of Black Friday, one of the biggest retail days of the entire year.
For a final example, let’s look at the text conversation below. If we are in the position of the blue texter, do we really believe the other person is fine? Based on their word choice, probably not.
This might lead us to try to resolve the tension by saying “I may have misunderstood the situation, can you help me understand better?” Or “I care about how you feel and want to make sure we both agree with the final decision.”
Each of the above scenarios had us use our intelligence in some way to solve a problem or make a decision. While each was very different from the others, there was one thing they all had in common — they required knowledge of how the world works.
In other words, we needed to know things like dark clouds precede bad weather, people shop a lot in November, and people don’t always admit it when upset. We know these things via our models of the world.
Models
Since the world is big and complicated, we have to make sense of it through models. A model is a simplification of a real-world thing that can fit in our heads.
One critical feature of models is that they allow us to make predictions. For example, when we saw the dark clouds, that information was processed by our mental model of how weather works and allowed us to predict that it will rain later.
Of course, models aren’t limited to the ones we hold up in our heads. We can also program models into computers (in fact, essentially all weather forecasts are generated using computer models).
2 Types of models
A natural question about models (mental or computer) is, where do they come from? For this, I like to split models into two categories: Principle-driven and Data-driven.
Principle-driven models are based on a set of rules. You might read these things in a textbook or learn from your grandma. For example, “If dark clouds, then rain later.”
Data-driven models, on the other hand, are based on past observations. This works by comparing how similar a new piece of information is to what you’ve seen in the past, e.g. “The last time the sky looked like this, it rained.”
Again, these models are not restricted to living in our heads. We can also program them into computers.
For Principle-driven models, we tell the computer exactly how to take inputs (e.g. dark clouds) and turn them into outputs (e.g. rain). However, for situations where we lack a set of rules, we can turn to techniques for generating Data-driven models — enter machine learning.
Machine Learning
While machine learning might have a mystique around it these days, it is a simple idea. Machine learning (ML) is a computer’s ability to learn by example [3].
The way it works is instead of explicitly telling a computer how to map inputs to outputs. The computer can learn this relationship by seeing many examples.
This is a powerful technique because it allows us to develop models even if we lack a theoretical understanding of the underlying thing, which is helpful in many contexts, such as sales, marketing, finance, weather, consumer behavior, and beyond.
Machine learning can be broken down into 2 steps. In the first step, we pass input-output pairs (i.e., predictors and targets) into an ML algorithm to obtain an ML model. Then, with a model in hand, we can pass new data into it to generate a prediction. This is illustrated in the image below.
How do we use it?
Up until this point, we’ve talked about 3 key terms. First, we discussed AI and defined it as a computer’s ability to solve problems and make decisions. Next, we introduced models, which are a critical part of intelligence and allow us to make predictions about the real world. Finally, we broke down machine learning, a way we can develop data-driven models of the world.
While these are powerful ideas, it’s not entirely clear how to use them to drive value in a business context. Here, I will share an illustrative example of what AI looks like in practice to (hopefully) spark ideas of how you can start using AI in your work.
Example: Credit Decisioning
One popular application of AI is using it to make credit decisions, i.e., approve or deny loans. Let’s see what that looks like.
Traditional way
The traditional way to do this is when someone submits a loan application to a bank (or some other financial service provider), an underwriter reviews the application and decides whether to grant or deny the loan.
However, now that we’ve learned about AI and machine learning, we might ask, “Can’t we replace the human underwriter with an AI underwriter?”
The answer to this question is “yes.. but it might be more complicated than you think.”
AI way
In practice, an “AI underwriter” would look more like the diagram below than the simple picture we saw above.
Notice that we don’t simply pass the loan application into an ML model and call it a day. Instead, the application passes through several business and IT processes before any machine learning (i.e. the credit risk model) is involved.
This is a critical aspect of what AI looks like in practice. Namely, it is often a web of processes and technologies all working together to solve a particular problem.
Put down that hammer
This reality of AI highlights one of the most underappreciated facts about it — it’s not easy. To make matters worse, many fall into a common trap that makes the process much harder than it needs to be — they start with the technology rather than the problem.
My old VP of Data Science would always say, “When you have a hammer, everything looks like a nail.” This captures a bias we (i.e. humans) have when it comes to building AI solutions. Namely, we are drawn toward a technology-first rather than a problem-first approach.
There are two main issues with a tech-first approach. First, there is a low probability of success (especially if you are not an expert). Second, there are way too many technologies to choose from.
A (much) better way is to start with the problem because in business, solving problems = generating value. And since we don’t get bonus points for using AI to solve our problems, we should seek the cheapest, quickest to implement, and most reliable solutions.
Start simple, fast, and easy
As we saw with the credit decision example, practical AI solutions can be more sophisticated than we might think. The (counterintuitive) secret to sophistication is that it is the result of many small and simple steps over a long period of time. In other words, the secret to sophistication is iteration.
Once you have identified the problem you want to solve, I recommend a simple, fast, and easy approach where each of these words is significant.
Simple — You want to start simply because sophistication is costly and fragile (there are many more ways it can fail than succeed). This means forgoing an AI solution if a simpler one is available.
Fast — Next, you want to prioritize fast solutions because (again) the secret to sophistication is iteration. If it takes you 6 months to build your first solution, you won’t be able to iterate fast enough to develop something significant (not to mention your solution might be obsolete at the current pace of innovation).
Easy — Finally, you want it to be easy. In other words, don’t make it hard for people to use the solution. This is for two main reasons. One, if no one uses it, you miss out on important feedback. And two, if it’s hard, that’s probably a sign that it doesn’t fit into your existing business process.
What’s next?
Although this was a high-level introduction, I hope it provided some clarity on AI and how to start using it in practice. It’s important to remember that AI (in practice) is often a collection of systems and technologies working in concert to solve a real-world problem. Two key elements in developing these solutions are a problem-first approach and iteration.
This was the first article in a larger series on using AI and ML in business. In future articles of this series, I will break down project management for data science and key considerations for ML model development.
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[1] arXiv:2303.12712 [cs.CL]
[2] AI-generated image from Canva
[3] Royal Society. (2017). (rep.). Machine learning: the power and promise of computers that learn by example. Retrieved January 20, 2024, from https://royalsociety.org/~/media/policy/projects/machine-learning/publications/machine-learning-report.pdf.
