5 Reasons Why AI Projects Fail

Companies have started AI projects based on open-source architectures, as the thought of a custom AI model is quite appealing. But unfortunately, a lot of these projects fail to deliver even after large investments. So, what are some reasons why AI projects fail?

AI projects fail because there isn’t enough high-quality data for the AI to train on, the AI uses inappropriate techniques for the project at hand, and AI can be challenging to debug. To ensure your AI project succeeds, ensure you have sufficient labeled data and scale the model appropriately.

The rest of this article will go over some technical reasons why AI projects fail. I’ll also share some steps you can take to prevent your project from failing.

1. Poor Data Quality

AI is data. Regardless of which AI base code you pick, you’ll largely be limited by how good your data is.

Data is the basis of all AI projects. It’s one of the main reasons 40% of companies fail even after significant investments in their AI models.

To be more specific, poorly annotated data is one of the worst things you can use for deep learning.

Let’s say your company wants to use computer vision to create a self-driving car. If you use a bunch of data with incomplete or incorrectly labeled annotations, the car may run over a pedestrian.

This can even happen with data labeled by humans, which is considered the best and most reliable. Maybe somebody who did the annotations was new at the job and used crosswalk labels on pedestrians.

Another example in the context of chatbots is unstructured texts and chat logs. There’s a plethora of untagged bodies of text on the internet, such as:

Chat logs
Emails
Forum, Reddit, and Quora posts
Transcripts of customer service calls
Social media posts

And most companies don’t even use labeled data, so their AI can’t learn accurately. And that poorly annotated data inevitably leads to poor results, as the AI doesn’t know how to correctly classify the information being fed.

Training AI models based on poor data quality not only provide inaccurate results but can also increase the cost of AI projects exponentially. If you’re interested in learning why AI projects incur high implementation costs, you might find our article “Reasons Why AI Is So Expensive” informative.

2. Insufficient Data

In addition to low-quality data, there often simply isn’t enough of it to go around. High-quality data is great, but deep learning algorithms require tremendous data before they can do anything reliably.

As Simon Tavasoli from Simpli Learn puts it, “Without data, there is very little that machines can learn.”

If you try supplementing high-quality data with unlabeled data, this can turn into a game of cat and mouse to determine which data is causing the issues.

Even the simplest AI models still need thousands of examples per category.

For instance, let’s say you’re trying to make an AI chatbot that can handle customers’ requests. You’ll need a few thousand labeled chat logs for each problem. And unless you’re Amazon or Walmart, you probably don’t have enough data.

It’s worth noting that how much data you need for your AI project depends on the complexity of the problem.

And most companies are overly ambitious, thinking AI is a magic tool that can learn everything from just a few samples.

Low-quality and insufficient data lead to two related issues: overfitting and underfitting.

Overfitting happens when AI is too complex and doesn’t have enough data to work with. This results in a model that can’t perform well when it encounters new data.

Conversely, underfitting is when the AI is either too simple or, in this context, doesn’t have enough variety in the input data.

3. Inappropriate AI Techniques

While data is the biggest hurdle in any AI project, unsuitable AI techniques and algorithms are also to blame.

Machine learning is already difficult to implement as-is, and using the wrong techniques only exacerbates the issue.

As I explained above, overfitting can be caused by a model too complex for its own good.

You probably wouldn’t use a multi-layered custom solution with 5 different API APIs to classify coffee cups based on their color. You’d just need a simple SVM-based model. And yet, companies think that throwing more money at the problem and adding more solutions will fix it.

Conversely, the problem can be that your project is using outdated or too simple algorithms to complete demanding tasks.

For instance, stock market prediction models need 3 layers of LSTM coupled with a regression layer to operate effectively.

And since there’s a massive shortage of knowledgeable ML engineers, this is a common reason AI projects fail.

4. AI Is Difficult to Debug

Artificial neural networks (ANNs/NNs) are popularly described as “black boxes.” We see the data we put in and the data we get out, but everything that happens in between is a mystery.

That’s because they’re incredibly complex and nearly impossible for humans to understand. The parameters are written for machines, not people, and they’re all tightly knit.

So, even the best AI experts in the world can’t often figure out why AI does something the way it does. Even when they know where to look to fix a bug, it still takes a lot of trial and error.

And a lot of companies either don’t have the patience or funding needed to fix these bugs. So, they just try to use the model as is until they realize that it’s making everything less inefficient. Which leads to them abandoning the project altogether.

5. Lack of Maintenance and Retraining

So, your company got everything set up, you used plenty of data and the proper techniques — everything is fine and dandy.

But six months down the line, problems start appearing. Maybe your fraud-detecting AI started giving false alarms for new clients. Or perhaps the email manager tool started labeling important emails as spam, leading to decreased sales.

Whatever the issue is, it was probably caused by neglect.

AI needs constant monitoring, new data, and minor tweaks and adjustments to continue working correctly.

If you use ChatGPT, you’ve likely noticed that OpenAI updates it every couple of weeks. That’s why instead of regressing, it keeps improving. Quite noticeably, too.

Not understanding that an AI project isn’t something you can set and forget is what causes many of them to fail.

What Steps Can Be Taken To Avoid AI Project Failure

Remember how I mentioned earlier that 40% of AI projects fail? That still means 3 out of 5 succeed in some capacity. So, let’s see what these companies are doing differently to make use of AI:

1. Use More High-Quality Data

Companies think that they should invest most of their money in AI to succeed with their project. The data is just an afterthought.

However, you should do the exact opposite. The AI model matters, but even the most sophisticated, tailor-made AI won’t be accurate with insufficient data.

So, invest in creating well-annotated datasets or look for existing ones that fit your use case. Alternatively, do some research to see if you can find quality datasets to purchase.

2. Use the Right AI Techniques

It’s vital to scale your model to your use case. A rule of thumb is to use the least complex model you can get away with. This leads to fewer complications and easier bug fixing, especially as you bring on more engineers.

For most uses, you don’t really need a complex neural network. All you might need is a minimalistic machine learning model based on simpler algorithms, such as:

Decision trees
Linear regression
Random forest
Naive Bayes
SVM
kNN

3. Monitor and Adjust the AI Model

Remember, no matter how good your AI model is right now, you’ll have to maintain it to continue using it. Your AI has to continuously evolve and learn from new data to adapt.

This is especially true if your company constantly changes things relevant to the AI’s use case.

For instance, let’s say you run a coffee store and use AI to recommend products to your customers. You probably add new coffee blends, remove old ones, and run out of stock all the time.

By continuously feeding new data and reannotating, I can guarantee that your AI will continue to be useful and even improve.

Final Thoughts

AI projects often fail because of high expectations and insufficient funding, especially regarding data.

You have to understand that even simple AI models need thousands of high-quality samples before they can do anything reliably.

The best way to ensure success is to scale your AI model appropriately, use high-quality data, and continuously update the AI.