As artificial intelligence (AI) becomes more sophisticated, many organisations recognise its power and implement it into their business. According to one survey, 84% of business leaders believe that AI will significantly impact their company, and 97% reported an increasing urgency to deploy AI-powered technologies. Successfully implementing AI projects, however, can be a serious challenge. Let’s take a look at the reasons why many AI projects fail -- and how to avoid it happening to you.
Optimising for the Wrong Business Problem
Some business problems can be solved with AI, others cannot. Often industry stakeholders request trained AI models that have been optimised for the wrong metrics or do not fit into the overall business workflow and context. This results in the data science team working hard for months to deliver a trained AI model that makes little impact on the business.
Lack of Suitable Data
Many AI projects fail because the organisation lacks the necessary data to adequately train an effective AI model. This is more common when the business is using AI for the first time or a new purpose. Whilst many businesses believe legacy datasets are enough to train AI algorithms, structuring data for analysis requires considerable context about why things happened as opposed to simply what happened.
Overconfidence in AI
Data scientists often enjoy pushing the boundaries of what’s possible with AI. In some cases, they are eager to try out newly developed models and frameworks when older, more established tools are a better fit for the problem at hand. Whilst it is important for an organisation to experiment with new technologies, solving real problems for its intended users should be prioritised.
Underinvestment in Infrastructure
Without investment in infrastructure, data engineers cannot build pipelines to clean data and deliver it to deployed AI models, leading to failed projects. Robust infrastructure ensures pipelines are monitored efficiently, with fresh data delivered consistently and new AI models deployed quickly and easily.
Immature Technology
Finally, in some cases, AI projects fail because the technology is applied to problems that are too difficult for AI to solve. Even the most advanced AI models cannot automate away a difficult task. Businesses need to recognise which problems are a good fit for AI and which are not, and this will help them avoid costly and embarrassing failures.
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