Over the past year, generative AI’s capacity for content creation has drawn most of the noisiest headlines: how AI models can instantaneously create Pixar-like shorts starring your pet chihuahua, or drum up the Drake-Kendrick Lamar diss track you never knew you wanted. What gets less attention, but is equally (if not more) valuable, is the business-use side of GenAI content creation: marketing.
Marketing is particularly well suited for adopting generative AI because it is an iterative, creative, and dynamic practice that relies on the types of media — texts, images, video — that have driven LLM development. (This is one of the reasons why many of the first B2B GenAI use cases were for marketing!) Plus, successful marketing plans and assets don’t necessarily have a single “right” solution. This makes marketing different from, say, fintech, where users expect a single, correct answer to queries and there’s a higher threshold for accuracy. And unlike sales, marketing is not dependent on painstakingly building a person-to-person relationship with the customer (although maybe this will become easier in a world of AI-powered sales development representatives).
Concurrently, the way companies view marketing is changing. As people spend their time in increasingly fragmented locations, customers become harder and harder to reach. Marketers are looking for ways to scalably create personalized campaigns and messages to meet people where they are. However, marketing teams are frequently siloed and work across disparate tools that frequently don’t work together. GenAI-based software can and already is helping to bridge this gap.
We’re already beginning to see the financial impact of this shift. According to a recent McKinsey report, GenAI in marketing and sales could reap an estimated $3.3 trillion in annual global productivity, and global payments company Klarna recently said it saves $10 million in costs every year by using GenAI
