AI Technologies, Practical Use Cases and the Road to Adoption

1. Computer vision refers to the ability of a machine to see images or a surrounding environment, automatically extract, analyze and comprehend information, and take the appropriate action. For example, a self-driving car knows what to do based on surrounding conditions, and a biometrics device can authenticate users by recognizing their fingerprint, retina, facial features, etc.
2. Natural language processing enables computers to understand and process human language which is just another form of unstructured data. More than a text-to-speech function, natural language processing recognizes and understands what you’re trying to say and extracts specific pieces of information from that message.
3. Virtual assistants use natural language processing to understand voice commands and respond by completing a task. This could involve everything from finding a location on a map and providing a weather forecast to playing music and turning off a device. In the workplace, a virtual assistant can schedule events, initiate a phone call, and send and receive text messages.
4. Robotic process automation is being used to streamline operations, reduce costs and free employees to work on higher-value tasks. Rules and policies are used to configure software to complete routine, repetitive tasks in a business process. It could be something as simple as an automated email reply, or a far more complex initiative such as deploying chatbots that interact with and provide answers to customers.
5. Machine learning is an advanced form of AI that uses sophisticated algorithms to learn from data, identify patterns and trends, make decisions and perform tasks without being programmed to do so. As machine learning software absorbs more data, it gains the ability to analyze larger, more complex data sets and deliver faster, more accurate results. Fraud detection in financial services, health monitoring and assessment in hospitals, and predicting equipment failure in the oil and gas industry are all made possible by machine learning.

McKinsey expects AI technologies to impact different industry sectors in different ways. For example, telecom companies have been quick to adopt AI, while healthcare is proceeding slowly. The more organizations pursue AI to drive innovation rather than reduce costs, the better the economic outcome. Not surprisingly, early adopters who apply AI across the enterprise rather than a handful of use cases will see the greatest benefit.

Organizations adopting AI first need to answer a few critical questions. Do we have the compute power, storage capacity, data, analytics and talent to support AI? What specific roles will AI play in our organization? What business value will AI deliver? How will that value be measured? Are we prepared to adapt workflows to take advantage of AI and embrace a more collaborative, agile culture?