Three tips for crafting an AI strategy

Artificial intelligence (AI) is expected to provide enterprises with the knowledge they need to create new revenues, streamline business processes and deliver superior customer experiences. While there is a great deal of debate over where to begin and which use case is more critical to profitability, operational issues are often handled at the end of the planning process. Machine learning (ML) models need to work efficiently to generate meaningful insights and the only way to make sure this happens is to tackle production issues from the beginning. Algorithms are required to process large volumes of data efficiently to generate timely insights. But often models fail to execute as intended in production, because of data bottlenecks and architectural complexities that were not foreseen in the early planning stages.