4 Reasons AI Projects Fail, or Never Begin

85% of AI projects end in failure, why?

No. 1: Skills of Staff

Demand for AI engineers is growing exponentially more rapidly than the labor market can provide for, with a current demand of 2.3 million, and a supply of less than 300,000. By 2025, the demand for talent is predicted to reach a staggering 20 million, resulting in an enormous labor shortage. It is no wonder Gartner reports “skills of staff” as the No. 1 barrier to AI development.

It is clear that AI is the future of business, yet the knowledge and technical skills needed to create these innovative systems are concentrated. Of the 300,000 engineers with AI capabilities worldwide, it is estimated that only 5,000 individuals have the technical skills to develop brand new, cutting edge AI systems from scratch. Tech giants like Microsoft, Amazon, Uber, and Facebook pay these senior AI engineers up to $1 million per year, more than half of that compensation in stock. So, if you aren’t a tech giant, it can be nearly impossible to compete.

No. 2: Understanding AI Benefits and Use Cases

Let’s say you have access to senior AI talent, the next problem is understanding what the specific benefits and use cases are that exist for AI in your business. When researching AI, many articles are so hyper specific and technical that it can be difficult for executives to imagine the areas that AI applies in their business.

Even when an application for AI is determined, actual ROI can be difficult to determine. Often times millions are invested before finding out whether a project will be successful. These huge investments are simply too risky for many companies with limited budgets.

No. 3: Data Scope and Quality & No. 4: Security & Privacy Concerns

73% of CEOs don’t know how to unlock the value of their data. Lack of quality data can often be a barrier to AI adoption. Some companies are data poor, while other companies can not fully leverage all of their data to use because of security or privacy concerns. Organizing and analyzing data can seem like a daunting and expensive task, but it doesn’t have to be.

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