Artificial intelligence is all the rage now. Consumers are just fascinated with all of the AI-enabled tech that they’ve continuously been getting for the past couple of years. However, it’s a bit different in the business world. Many companies struggle to see the value in AI development. Many don’t even realize that they may need AI.
In a lot of these cases, the issue is rather simple. People don’t understand what AI is, what AI is capable of, and how it works. In this article, we will cover some artificial intelligence facts that are relevant to anyone exploring artificial intelligence and its potential. This article incorporates general information about the current state of AI, but it also channels our experience in implementing AI projects for enterprises.
1. AI Is Not Just a Buzzword
The business impact of AI is real. It’s propagating through industries right now, and it’s probably already finding a way into yours. According to McKinsey, 70% of companies will use some form of artificial intelligence by 2030. It will result in an additional $13 trillion of economic activity on a global scale
Companies are rushing to be the first ones to realize that potential. And it pays off immensely. Deloitte says that 82% of companies that adopt AI today already see a positive ROI trend, with 17% as the median return.
The potential for these returns is horizontal, across many industries, from retail and manufacturing to energy and healthcare. So if you’re still considering whether it’s worth to explore an artificial intelligence project, remember that your competitors might already be doing it.
2. AI Will Not Kill Us…Most Likely
The most basic association that people have with AI is either the T-1000 from Terminator 2 or HAL 9000 from the Space Odyssey. It doesn’t help that one of the most famous entrepreneurs in the world is pretty pessimistic about artificial intelligence. Modern cultural references, like Ex Machina, aren’t being helpful at improving the general perception of AI.
Many people, including business leaders, don’t understand the technologies behind what industry calls “AI.” There’s no AI that even remotely resembles a true AI, which is called AGI or artificial general intelligence.
Instead, modern AI is a computer system that can perform certain tasks usually attributed to human intelligence. It is much more rudimentary and designed to work in a single domain, with proficiency at one single task.
AI is harmless in its current form, and by the time we get to something threatening, there should be systems in place to control it. The scientific world is well aware of the potential dangers of AI. That’s why schools like Stanford are already researching the future of AI. Their 100 Year Study of Artificial Intelligence, which releases periodic reports, have concluded recently that AI would not be embodied by a machine, driven by a single goal of killing people.
3. AI: The Schrodinger’s Job Killer
Will AI kill jobs? Are we all just going to bow to our silicone overlords? The truth is, we don’t know for sure. Yes, AI might eliminate jobs due to automation. But at the same time, it will create jobs elsewhere. It all depends on the industry and the level of its automation. But there are industries somewhere in the middle, like the postal service. Email is here. Postal service is here. There’s an equilibrium in many other industries to be yet discovered.
4. AI Is Not Omnipotent
This fact correlates with the previous one. The public perception of AI is very skewed. Given the amount of hype around the technologies that make up AI, many people are starting to think that AI can do anything. This resulted in what is called “AI Solutionism,” an idea that AI can solve any problem. This kind of thinking also translates into the business world, and many business leaders get disappointed after learning that AI can’t do “this” or “that.”
There are many limitations on modern AI, like limited datasets, interpretability of outputs, and even the availability of data. And this is partially why some of the early adopters seeing better ROI are industries where these problems are less severe.
5. Your AI Is as Good as Your Data
We wanted to focus on one of the limitations of AI because it’s a critical one. As a business, you can’t expect ROI from artificial intelligence, when you don’t have the “fuel” for it. Just like you wouldn’t expect a car to run without any gas. AI, just like humans, learns from experience. This experience in case of AI is the historical data that you’re supplying it with. AI-enabled systems use machine learning, which is a process by which the software learns through data. Many use these terms interchangeably.
You can’t expect good results from a system that’s been taught on bad data. That could be anything from bad quality of data to lack of data. To quote Doug Bewsher, CEO of Leadspace, “Data is AI’s Achilles’ heel.”
So if AI is in your purview for the next couple of years, you’d better start thinking about improving your data quality and data management practices (architecture, metadata, privacy, and other elements).
6. Good Time to Start with AI Was Yesterday
An artificial intelligence project requires a lot of investment from your team. There are many stages involved. In a very rudimentary form, these stages may include:
- Building a business case
- Figuring out where and how your data is stored
- Creating a dataset (cleaning, preparing data, etc.)
- Figuring out the best machine learning tools for the project
- Training a machine learning model
- Operationalizing the model
And this is just for the project itself. There’s a ton of preparation. You might not even have the right people. Many of these steps are technical. There’s a lot of coding, integrations, and project management involved. It is going to take a lot of time and effort. And the chances of the project failing are pretty high. Start now or risk being left behind by your competitors.
7. Deep Learning Is Not a Magic Pill
Deep learning (DL) possesses a certain mystique. Many business leaders hear it being thrown around and think that it’s some sort of a magic pill for business problems. But this statement couldn’t be further from the truth.
First, deep learning is machine learning. It’s a subset of machine learning techniques. It sounds cooler; we’ll give DL that. But just like with any other machine learning approach, it has a ton of limitations. One of them is the fact that deep learning is a black box. It provides you with output, but you won’t be able to easily explain how it got to it. So if you work in a bank that’s building risk models, deep learning is a very bad choice, as it can’t be that easily interpreted for the regulators.
8. AI Has Its Quirks
Artificial intelligence, at the end of the day, is a piece of software. It’s not going to be 100% accurate with what it’s doing. Many AI systems will provide you with a probability. It’s up to you to figure out a threshold that triggers an action. On top of that, artificial intelligence can be fooled.
AI can already behave like humans, outside of its standard purview. Researchers at OpenAI essentially programmed their AI to be curious, and it played video games the whole time. Just like humans do with their free time on a weekend. Now that’s some human-level intelligence. 🙂
9. AI Can Be Biased
We talked about data before. Bias is another problem with data. Data is generated by humans. And we’re biased. Intentionally or unintentionally. Let’s take a real-life example. Tokyo Medical University recently lost its accreditation because it unfairly treated women who were applying for a degree there. They were disqualifying women because the consensus was that men would be better doctors, as they won’t take maternity leave and will be less likely to switch or quit to raise a child.
Now, imagine we tried to train an AI that predicts the best potential students for our university. If we took the data from Tokyo Medical University, the algorithm trained on it would pick up that men get accepted more, as it would just see it as an important factor for people being accepted. It would treat male gender as a positive factor and disqualify women. All because somebody was biased somewhere down the line.
There are plenty of examples like that. Even the biggest players, like Amazon, screw up with AI bias all the time. That’s why you need to scan for these things, both before and after the system is operationalized to deliver insights, predictions, etc.
10. AI Is Already Everywhere
This probably sounds like a cliche. But AI is already everywhere, and you may not even realize it. Netflix recommendations. Spotify Discover. Alexa, Siri, or Bixbi. Amazon’s shipping. Uber or Lyft? Doesn’t matter. They all use AI. Your insurance company is probably calculating your premium with AI. Your bank is probably calculating your risk of defaulting on a loan too. If they don’t, they soon will be.
Refer to the first subtitle in this article. AI is not a buzzword. And your business will need it to compete in the nearest future.
11. AI Doesn’t Need Real Data
Yes. We talked a lot about data in the post. But guess what? Sometimes you don’t even need data. You can make it up. Sort of. It has to resemble real data as if it was generated naturally. It’s called synthetic data. And companies are using it in industries where data is lacking or is expensive to acquire.
12. Open Source AI Tools Aren’t Free
While going open source on your AI toolkit might seem like a great idea, it’s not always that way. Depending on the size and the complexity of your AI project, this decision might be fatal. Open source lacks, among other things, one very crucial component. There’s no enterprise support. Once something goes wrong, you’re on your own.
That’s why you find a partner (drop us a quick note) and an enterprise platform for AI. Companies like HortonWorks were built around this premise. They take open source products and provide enterprise support for people using them.
13. AI Doesn’t Have to Be a Black Box
We touched upon the topic of black box AI. This is a system that delivers output, but it’s hard to explain why specifically that output was generated. Why might this be important?
If you’re faced with a business problem, it’s very useful to know why data behaves in a certain way. For example, if AI provides you with a credit risk score for loan applications, you might want to know what data influenced the prediction of the score.
Sometimes, you are required to explain your output like in the case with regulations that we mentioned before. Federal Reserve Board, as such a regulator, requires banking institutions to explain their models.
Fortunately, there are many ways of making it happen for non-deep learning models through additional model/data manipulations. There are AI tools that already support interpretability features by default, making the process less technical and less time-consuming.
14. AI Is Not That Expensive
Yes, an AI-enabled system for enterprise tasks can be an expensive undertaking. In some cases, AI vendors require a whole suite of technologies to operate that only make sense for large enterprises. For example, SAS and their machine learning ecosystem might cost tens of thousands of dollars per month to operate.
But that doesn’t mean that small businesses can’t find AI tools that fit their budget and needs. Let’s start with the fact that there are plenty of open-source tools out there. Yes, they require skills, but if you’re in a tech-related business, it’s likely that there’s someone on your team willing and able to deploy these tools.
If you want to start with AI on a budget, you’d need to scale down your understanding of AI. For example, a chatbot is also a form of AI. They’re incredibly affordable in deployment. This kind of leads us to the next AI fact.
15. Your AI Is as Good as You Are with AI
Sorry for the mouthful, but we wanted to keep it simple. If your team doesn’t understand how AI works, your AI project is likely going to be a failure. Many business leaders already understand this. That’s why, for example, Nokia’s Chairman made it a part of the company’s culture to learn about machine learning, the cornerstone of AI.
If you’re reading this article, then you’re on the right track. But you also need to deliver the message about the potential benefits of AI across your team and the organization.
If you’re looking for an experienced AI partner – reach out to Iflexion. We understand what it takes to operationalize an AI system and how an organization’s culture needs to change to accommodate such a huge technological shift.
Are you working on an AI project for your business? What’s the most exciting part of it? Are you facing challenges? Share your thoughts and ideas in the comments below!