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.
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.
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:
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.
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.
