By 2020, Forbes estimates that 85 percent of customer interactions will be managed without a human.1 While many of us use AI technology, such as Alexa and Siri, as part of our daily lives, we may not be aware of its greater uses. In fact, with machine learning applied, AI can help teach computers, target ads and personalize content for consumers to ensure better and more informed business decisions. This paper will clarify some key definitions around artificial intelligence and machine learning. It will also simplify some common techniques in machine learning, such as supervised learning, natural language processing, and classification, and identify the types of business questions these techniques can answer.
While understanding a small number of customers may not pose a challenge, keeping pace as organizations grow and expand their customer base can be difficult. Data analytics can help reveal trends and metrics that would otherwise be lost among the masses of information. Organizations are now starting to leverage descriptive, diagnostic, predictive, and prescriptive analytics to address the growing needs and demands of their customer base. The promise of artificial intelligence is exciting but before jumping in organizations need the right data literacy, infrastructure, and expertise. This paper will also cover key competencies organizations need to get started with AI and how to progress from data collection, exploration, and analytics to artificial intelligence. Finally, this paper will help define meaningful and high-value use cases with a structured framework to gather and align business, technology, and data requirements for a successful artificial intelligence implementation.
