Artificial intelligence (AI), machine learning (ML) and other emerging technologies have potential to solve complex problems for organizations. Yet despite increased adoption over the past two years, only a small percentage of companies feel they are gaining significant value from their AI initiatives. Where are their efforts going wrong? Simple missteps can derail any AI initiative, but there are ways to avoid these missteps and achieve success.
Following are four mistakes that can lead to a failed AI implementation and what you should do to avoid or resolve these issues for a successful AI rollout.
When determining where to apply AI to solve problems, look at the situation through the right lens and engage both sides of your organization in design thinking sessions, as neither business nor IT have all the answers. Business leaders know which levers can be pulled to achieve a competitive advantage, while technology leaders know how to use technology to achieve those objectives. Design thinking can help create a complete picture of the problem, requirements and desired outcome, and can prioritize which changes will have the biggest operational and financial impact.