Operationalizing Machine Learning at Enterprise Scale

According to a McKinsey Global Survey, approximately 30% of executives reported active pilot projects, while 71% were expecting a significant increase in AI investment. However, the survey found that progress remained slow, most companies didn't have a clear strategy or infrastructure for sourcing data, and organizations were lacking the foundational building blocks to create value from AI at scale. Deploying AI in industrial operations is difficult for a variety of reasons – complex data management, challenging integration, enterprise security requirements, real-time analytics and capability to handle thousands of models in the production environment. However, a fundamental problem is finding skilled people to implement AI. To circumvent this issue, companies are relying on citizen data scientists – subject matter experts with domain expertise in operations – and providing them with advanced analytical tools.