Top Considerations Before Implementing AI

Business leaders and AI practitioners must ask the right questions before embarking on an AI project.

Artificial Intelligence (AI) is poised to transform many industries in the coming years, including augmenting human intelligence, powering automation, enabling optimization, offering decision support, paving the way for hyper-personalization and making possible natural interfaces to many business applications. Companies are actively exploring, experimenting and deploying AI-infused solutions in their business processes. Chatbots in customer support scenarios, doctors' assistants in hospitals, legal research assistants in the legal domain, marketing manager assistants in marketing, and face detection applications in the security domain are some early use cases of AI in enterprise.

Many things must come together to build and manage AI-infused applications. Data scientists who build machine learning models need infrastructure, training data, model lifecycle management tools and frameworks, libraries, and visualizations. Similarly, an IT administrator who manages the AI-infused applications in production needs tools to ensure that models are accurate, robust, fair, transparent, explainable, continuously and consistently learning, and auditable. This requires new tools, platforms, training and even new job roles. AI-infused applications should be consumable in the cloud (public or private) or within your existing datacenter or in a hybrid landscape. All this can be overwhelming for companies trying to deploy AI-infused applications.