If someone asked me a year ago what are the biggest issues that companies struggle with when implementing AI, I would probably have highlighted data quality and availability. And while a growing focus on data and progressing digitization has helped organizations to start addressing these challenges, Gartner still project that around 85% of all machine learning (ML) projects will fail.
In this context, I see a bigger challenge confronting C-level executives responsible for driving innovation and efficiency. AI has become a “hype topic”; and while everyone talks about it, not everyone fully understands it. Put simply, to grasp the full potential of AI – and understand its inevitable limitations – you need to pilot multiple projects and fail enough times to draw meaningful conclusions.
But let’s face it: in harsh economic conditions, few companies have the resources required for such experimentation. A phrase I overheard at a recent AI event, “We have more pilots than an airline, yet we are not an aviation company”, underscores that many companies are motivated to use AI, but fail to narrow down its use cases and achieve real impact and return on investment (ROI).
So how can this be addressed?
How to implement AI and achieve return on investment?