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Overcome the Challenges of Operationalizing AI/ML
Data is an extremely valuable asset for every organization, but it is meaningless until it is used to make actionable decisions. Given the volume of data generated and collected today, using artificial intelligence with machine learning (AI/ML) is the most efficient way for organizations to sift through data and extract value. All industries and line-of-business functions find value by integrating AI/ML into data science efforts. Among participants in our Analytics and Data Benchmark Research, 97% of financial services organizations reported that AI/ML is important or very important, particularly for detecting and preventing fraud. Three-quarters (76%) of technology organizations also reported AI/ML is important or very important. AI/ML is often deployed to prevent cybersecurity disruptions. And more than one-half (57%) of healthcare and life sciences organizations rated AI/ML important or very important Healthcare and life science organizations use AI/ML models for numerous use cases including improving drug discoveries and creating personalized treatments to provide the best possible clinical outcomes for individuals.
However, creating, deploying and managing AI/ML models can be challenging for data and analytics executives. Our research shows that less than one in 10 organizations consider their organization’s AI/ML technology completely adequate and more than two-thirds consider it less than adequate. AI/ML presents several challenges. AI/ML requires a significant amount of data and therefore a scalable, high-performance infrastructure. It also requires a blend of data science skills with software development and operations knowledge. The tools and skills needed to code AI/ML models often do not help when dealing with the infrastructure for deploying and managing those models. Given these challenges, data and analytics executives should carefully weigh the following five considerations for AI initiatives.