Leveraging AI in Marketing: A Guide for CMOs

Content marketing strategies that leverage AI can be incredibly lucrative for companies and can drive significant growth. But how can Chief Marketing Officers (CMOs) make the most of this technology? Marketers have limited capacity to process information, create strategies, create content on a large scale, and reach their performance potential. AI algorithms, however, have an almost infinite capacity to process data and provide predictions, recommendations and content in a more efficient, faster and cheaper way. It can be difficult to understand what AI is truly capable of, leading marketers to underestimate how it could revolutionize their businesses.

Many of the tasks that marketers typically perform are augmented by machine learning, deep learning and cognitive computing. To make the most of AI, marketing teams must be able to interpret the information that AI tools provide them and translate it into practical measures. To optimize revenue growth, CMOs must offer B2B customers greater personalization, more intelligent customer experiences similar to those of B2C, and take advantage of the increase in AI for staff. Marketing represents the voice of the customer, and executive leaders in all business functions must consider that voice in their business decisions.

Revenue operations teams that have successfully implemented AI must determine the business predictions for which the tool is designed and how they can take advantage of them. At a time when most CMOs stay with a company for only two and a half years, they need all the help (and optimism) possible. B2B CMOs must improve their skills by investing in making the marketing organization better aware of AI-driven use cases, beyond predictive and prescriptive AI solutions. AI will free up crucial time for your team to dedicate to offering the best marketing strategies.

Before betting on artificial intelligence, it's important to understand the foundations of successful predictive models. B2B marketing teams can take advantage of the data collected to identify patterns and trends with machine learning platforms. However, the most widely available applications of AI in marketing are for more tactical and operational use cases, such as scoring leads and propensity to buy. In reality, the enormous amounts of data, the exponential advances in computing power and the availability of AI technology from Facebook, Google, Microsoft, IBM and others have been combined with an avalanche of venture capital money that goes to everything related to AI to prepare the marketing industry for disruption.

In addition, they must consider how AI affects both sellers and consumers in a rapidly changing environment.