The Top 5 Reasons Why Most AI Projects Fail

Due to the pandemic, most businesses are increasing their investments in AI. Organizations have accelerated their AI efforts to ensure their business is not majorly affected by the current pandemic.

Though the implementation is a positive development in terms of AI adoption, organizations need to be aware of the challenges in adopting AI. Building an AI system is not a simple task. It comes with challenges at every stage.

‍Related Reading: A Step By Step Guide To AI Model Development

Even though you build an AI project, there are high chances of it failing upon deployment, which can be attributed to numerous reasons. This blog post will cover the top five reasons on why AI projects fail and mention the solutions for a successful AI project implementation.

1. Improper Strategic Approach

There are two facets to a strategic approach. The first is being over-ambitious, and the second is the lack of a business approach.

When it comes to adopting an AI project, most organizations tend to start with a large-scale problem. One of the main reasons is the false belief people have about AI. 

Currently, AI is overhyped but under-delivered. Most people believe AI to be that advanced piece of technology that is nothing short of magic. Though AI is potent enough to be such a technology, it is still at a very nascent stage. 

Furthermore, adopting AI in an organization is a considerable investment of time, money, resources, and people. Since companies make that huge investment, they also expect higher returns.

But as mentioned before, AI is still too narrow to drive such returns in one go. Does that mean you cannot get a positive ROI? Not at all.

AI adoption is a step-by-step process. Every AI project you build is a step forward to making AI the core of your business. So start with smaller projects like gauging demand for your products, predicting credit score, personalizing marketing, etc. As you build more projects, your AI will better understand your needs (with all the data), and you will start seeing much better ROI.

Moving on to the second facet of the problem - When companies decide to build an AI project, they usually see the problem statement from a technical perspective. This approach prevents them from measuring their true business success.

Companies have to start seeing a problem from a business perspective first. Ask yourself the following questions:

• What business problem are you trying to solve?
• What are the metrics that define success?

Once you have answered these questions, move on to decide what technology you would use to solve the problem. Remember, AI is an ocean that covers multiple technologies like machine learning, neural networks, deep learning, computer vision, and so much more.

Understand which technology would be most suitable for the problem at hand and then start building an AI solution.

2. Lack of Good Talent 

Most people forget that AI is a tool created by humans. Of course, data is the crucial ingredient, but humans are the ones who use it to develop AI. And currently, there is a shortage of talented professionals who can build effective AI systems.

In its Emerging Jobs 2020 report, LinkedIn ranked AI specialists in the first position. However, the supply does not seem to match the demand yet.

The shortage dips further when you consider quality and experience as well. Mastering AI or becoming an expert in AI takes years. Before becoming an AI expert, one needs to master the various underlying skills like statistics, mathematics, and programming. Also, AI practitioners have to constantly keep updating themselves as AI is a continuously evolving field.

According to Gartner, 56% of the organizations surveyed reported a lack of skills as the main reason for failing to develop successful AI projects. 

Organizations can solve this problem in two ways. 

First, they need to identify talent within their workforce and start upskilling them. They can gradually extend this process to the rest of the organization.

Second, organizations need to partner with universities to bridge the gap between academia and the industry. With a clear picture of the skills needed and the right resources, universities can train students with the skills required in the industry.

While pedagogical changes will boost AI upskilling, it is still a long term approach. What about now? There is a third approach which is slowly gaining traction, which is, dedicated AI companies that have the right talent, building AI models and offering AI-as-a-Service. 

3. Data Quality and Quantity

Having addressed the issue of the people who make AI, let’s now talk about the ingredient that makes this technology possible - data.

AI, as a concept, was first introduced in the decade of 1950. But at that time, the researchers did not have enough data to bring the technology to reality. However, in the last decade, the situation has drastically changed.

With technology and gadgets being a close companion of humans, most gadgets and software have garnered the ability to collect zillions of data. And with this rising data collection, AI started gaining traction.

But then a new problem arose - the quality of the data.

Data is one of the two most crucial requirements to create an effective AI system. Though companies had started to collect tons of data, issues like unwanted data, unstructured data persisted.

Data usually gets collected in multiple forms - structured, unstructured or semi structured. It is usually unorganized and contains various parameters that may or may not be essential for your AI project specifically.

For example, if you are building a recommendation system, you would want to avoid collecting unnecessary data like mail id, customer picture, phone number, etc. This data would not help you solve the problem of understanding your customer preferences. Worse, you might face the issue of overfitting where there is a ton of unnecessary data.

In the above example, if you had data like web browsing history, previous purchases, interests, location, then your AI system would give you much better results.

To solve the data issue, consider involving all the stakeholders before starting an AI project - the business heads, data analysts, data scientists, ML engineers, IT analysts, and DevOps engineers. You can then have a clear picture of what data is required to build the AI model, what quantity, and what form. Once you have an understanding of this, you can clean and transform your data as required.

Also, while you prepare the data, make sure you keep aside a part of it as testing data to ensure the AI you build works as you intend it to work.

4. Lack of AI Awareness in Employees

Most people believe artificial intelligence would replace them in their jobs. However, this is certainly not the case. 

As companies adopt AI, they will also have to concurrently educate their workforce on how AI is an “augmentor”. This education which is rightly termed as “data literacy” is crucial if you want your organization to have an enterprise-wide AI adoption.

Data literacy needs to be prioritized for two reasons

1. To ensure that your workforce(especially non-tech) are aware of what AI does and the capacity in which it helps them
2. To ensure that upon successful education, they do not blindly rely on AI for the decisions it makes

There have been scenarios where even though companies have deployed AI in their day-to-day operations, the workforce has rejected it. This indicates that employees have trust issues with the technology.

Alternatively, you do not want your workforce to blindly accept all the decisions made by your AI. You need to ensure the decisions are justified and make sense.

Due to these reasons, as and how your organization starts adopting AI, you will also have to start educating your workforce on the technology. Promote AI as a technology that takes up tasks and not jobs. Let your workforce understand that the sole purpose of AI is to free up human time so that they can focus on complex problems. It is pertinent that people understand AI as not just artificial intelligence but augmented intelligence.

5. Post-Deployment Governance and Monitoring

Consider you bought a car. It has all the necessary features that you wanted. It drives smoothly, helps you get to work in a matter of minutes, and even customizes the ambiance as per your preferences. But does that mean it does not need any attention or maintenance from your end? Absolutely not.

Similarly, for simplicity, building and deploying an AI is like having a car. You will also have to maintain the AI after deployment. However, maintaining AI is a far bigger undertaking than maintaining a vehicle.

AI systems make myriad decisions based on the data that are fed to them. If people cannot understand how an AI arrives at a particular decision, then that AI system can be labeled as a “black box AI.”

Ensuring your AI does not turn into a black box is crucial, especially when it makes decisions like processing loans, suggesting medical treatments, accepting applications for universities, and so on. Many governments are realizing the black box issue and are considering regulating the technologies. Even if they are not bound legally, it becomes an ethical responsibility of the developers to ensure AI is fair and just.

An additional challenge here is the dynamic nature of data and the business scenario. It is unlikely for data to remain static throughout the lifetime of an AI project. As the data changes, the AI also needs to be recalibrated to ensure it does not drift from its performance.

This process of recalibrating AI systems is mostly similar to building an all-new model. And like any AI project, it takes time and resources. For this purpose, most companies try to stretch their models for a long time without “maintaining” them and accommodating the business changes in the model. But you cannot time when the model will start drifting and lead to unnecessary implications.

To address these problems, organizations will have to constantly monitor their AI systems. The AI needs to be regularly updated with the changing data and business scenarios. To make this process a little less difficult, you can use an AI observability tool that helps you monitor your models and report unnecessary drifts.

AI Adoption Is A Journey

AI is a powerful technology that is changing the way we do business today. However, like every good thing, it needs time and effort to uncover and function to the best of its abilities.

As AI enablers, we recommend organizations adopt AI in a step-by-step fashion. The returns on AI investments are not linear - it compounds as you start utilizing it in your organization. Once you have specific AI use cases, you can extend the AI system to an enterprise-wide level adoption.

Interested in discussing your next AI project? Get in touch with our AI experts.