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…

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