In 2024, AI washed over marketing organizations like a tsunami.
The opportunities of AI and the ensuing logistical, customer service and existential nightmares are falling squarely on the shoulders of marketers dealing with technology and data. Rather than revolutionizing their jobs, it’s simply upending them, creating stress and uncertainty.
Everywhere AI goes, it delivers good, bad and ugly results. Here’s how it’s been working so far in the three most common use cases:
Personalization and Customer Experience
The Good: AI analysis and generative AI content generation are working together to give people more relevant customer experiences. Brands can personalize emails, offers, website and mobile app experiences with copy and content based on past behavior, preferences and other data.
The Bad: AI hasn’t made it easier for advertisers to know enough about consumers to get personalization right. The process relies on accurate and complete data, still a work in progress for many companies.
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The Ugly: Less tested and potentially dangerous AI solutions like chatbots are being unleashed without enough oversight, creating potential for horrific customer interactions.
The Bottom Line: The dream of a truly personalized, relevant customer experience is still elusive. While AI helps create more specific messaging and content, it’s still early days, and it still relies on good data.
Creative Optimization and Content Generation
The Good: Generative AI is being used as a brainstorming companion, to create mood boards and topic ideas. A marketer strapped for time can have generative AI transform a blog post into a series of social media posts, translate a press release into 10 languages, and reformat creatives to fit requirements for every social media platform in seconds. That’s cool.
The Bad: Someone needs to decide what content still needs human oversight, editing and approval, which can be time-consuming and expensive, taking some of the coolness out of having hundreds of pieces of auto-generated anything.
The Ugly: Advertisers relying too heavily on generative AI risk tarnishing their brand with robotic copy, weird images or worse.
The Bottom Line: Saving time is awesome, but maintaining an authentic connection with customers needs to matter more than time savings or creating different versions of content.
Data Analysis, Targeting and Measurement
The Good: AI, like deep learning, is helping marketers analyze data much more effectively. New campaign measurement approaches help marketers focus on metrics that really matter, like incremental lift. AI is also helping marketers identify new customer segments and find new places to market.
The Bad: Again, data quality matters, and it’s not always great. Analysts using AI analysis need to be true data scientists for insights to be trusted.
The Ugly: AI targeting and analysis is opaque and hard to trust. Many marketers have to take a leap of faith to alter their strategy based on a result.
The Bottom Line: Marketers should work with teams of data professionals and ensure data being used is representative, clean and timely.
Working Through AI Fatigue
Marketers didn’t really save much time using AI in 2024. Instead, they generated more content, used more ways to target customers, and got more headaches. After the initial AI hype, marketers are solidly in the frustration phase, which will be a big theme in 2025.
Working through this frustration and fatigue will be a big competitive differentiator. Marketers willing to do the heavy lifting of improving processes, shoring up data and continuing to test AI will start to see the transformative gains many were hoping for in 2024.