id you know that 85% of all AI projects fail to reach the production or operation stage? Why is this the case?
It’s very common for businesses to come up with creative ideas to use AI to improve customer experience or simplify workflows. The barrier to success for these projects often resides in the time and resources it takes to get them into development and then into production. But, as we’ve seen with OpenAI’s new ChatGPT, AI can be as entertaining as it can be problematic.
With so many projects failing, or worse, being inaccurate, chances are that many of these companies are making the same mistakes. The following are some tips that will optimize your chances of success.
Start off on the right foot
The process of AI development suffers from poor planning, project management, and engineering problems. Most business leaders today learn about AI from the media, which often describes the value of AI as magic or as something that can be put into production with just a few sprinkles.
They believe implementing AI can help lower costs, improve margins and boost revenue. With competitors already in motion, it creates AI “FOMO” and executives are pushed to take action quickly despite not having a clear understanding of the overall impact, plan, cost and resources involved in creating a successful and accurate AI project.
With little understanding of the engineering environment, the first logical step should be hiring data scientists to map and plan the challenges that the team may face. However, these data scientists usually have no domain knowledge.