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What are the reasons for failure of artificial intelligence projects

The enthusiasm to jump on the AI train is understandable and encouraged, but the success of AI depends on the complete universe of data being captured and analysed through a large-scale database with continuous analysis of the convergence between predictive and real time data. Premature failure of AI projects is a common concern, and one that is legitimate; recent numbers show 85% of all AI projects fail, sometimes before they have even been initiated, Why? Well, oftentimes, these big projects are misaligned with business priorities. Especially today, a lot of businesses are invested in keeping their traditional processes going, so embarking on such an altering project is not likely to make the top priorities list. 

AI is expensive. Spending on AI projects and solutions will hit the $58 billion mark within the next few years. Many companies tend to take a leap into a sea of information on the matter and end up with one takeaway: AI helps cut cost. While this is true, the results take time, effort and skills. From partnering with the right solution providers, to hiring the perfect candidates to administer AI projects. A lot of companies will initiate and then withdraw when the costs are fully evaluated in comparison to the results. This is why strategising AI projects is essential to success, but still does not guarantee it. 

This relates to the way AI is described as risky business. It is considered an expensive tool that is hard to measure and maintain. However, developing strategy and approach can set companies on the right path. It must always start with a problem the business is facing, and a question; Can AI solve it? A strategy can then be designed and set, with proper and regular measuring of RoI. 

The way an AI solution will work for a business is through data, and the quality of data it is fed with. This poses another threat to AI projects, and is considered a major contributing factor in their failure. AI requires a lot of data in order to deliver, and the more the better. If a company is small, with not much data to go from, then expectations must be scaled to that level of data availability. The data must also be relevant to the problem the AI solution is designed to solve, and oftentimes such intricacies aren’t even considered. 

AI is risky, but the rewards can bring cost-cutting and long-term success to business. It is most crucial to consider data quality and availability when AI adoption is on the table. Strategy, success and failure measurement criteria are also determining steps in the adoption process. Meanwhile, globally, the direction business is going towards is one that should solve the issue of low skill levels and expertise in the field.

Key Takeaways

  • Spending on AI projects and solutions will hit the $58 billion mark within the next few years.
  • AI adoption must be a step-by-step process, starting with why the business needs it, and how it can help.
  • AI is risky, but the rewards can bring cost-cutting and long-term success to business.

 

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