The 2022 State of AI in Marketing

Artificial Intelligence has its use cases in marketing, but many marketers remain uneducated or are just beginning to understand its impact.

The nascent stages of Artificial Intelligence (AI) in marketing remain apparent. Just ask marketers themselves.

According to findings from a Drift and Marketing AI Institute survey“lack of education and training” is the dominant barrier to adoption of AI in marketing out of 17 choices, with 63% of marketers selecting that barrier.

“This wasn’t surprising because it was 70% in 2021,” said Paul Roetzer, founder and CEO of Marketing AI Institute and author of “Marketing Artificial Intelligence: AI, Marketing and the Future of Business.” “So we know that lack of education and training is a major issue.”

The majority of marketers see AI as an abstract topic they don’t need to worry about now, according to Roetzer. “That lack of interest in the topic,” he added, “leads to the lack of awareness, understanding and urgency at the executive level.”

However, it’s not abstract or overwhelming, according to Roetzer. “When presented in an approachable way, with practical use cases and frameworks to get started, any marketer can learn and apply AI to save time and money, and drive growth with smarter technologies.”

Lack of awareness stayed in the No. 2 spot for marketers (52%) behind lack of education and training, but jumped six points from 46% in 2021.

Is the Skills Gap to Blame?

Aurelia Solomon, Drift’s senior director of product marketing, said that while a vast majority of marketers believe AI will transform their work, it wasn’t surprising to learn that 63% of marketers cite lack of education and training as the biggest barrier to adopting AI. This could be due to widespread reports of a technology skills gap, she added.

“I think this is reflective of a larger trend around growing skills gaps, especially for emerging technologies like AI,” she said. “Even though 67% of tech decision-makers recognize that the skills shortage is preventing their companies from keeping up with innovation, we found that just 11% of businesses have some kind of formal AI education and training program in place for their employees.”

Coursera’s latest Global Skills Report found US proficiency in technology and data science skills are declining and lag behind countries in Asia-Pacific, Europe and the Middle East. US learners did show higher proficiency in essential business skills, including marketing, leadership and management and strategy and operations.

CMOs and other C-Suite roles have an opportunity to work together to close the skills gap and develop training for a workforce that is itching to leverage AI, Solomon added.

“Marketers understand the power of AI, and they want to adopt it,” she said. “In order to do so, however, they need buy-in at the highest level. To make AI an integral part of the entire organization, leaders must work cross-functionally and focus on the value AI delivers by function and role.”

Related Article: If You Want to Succeed With Artificial Intelligence in Marketing, Invest in People

Marketers See Use Cases but Are at Beginning

Despite the lack of awareness and education, marketers do see use cases for AI. Marketers are using AI to improve marketing outcomes in three key areas: personalization, revenue acceleration and getting actionable insights from data.

“Each of these is extremely important for marketers,” Solomon said, “but it all starts with personalization as buyers today expect a customized digital experience tailored to their needs. With AI, interactions between buyers and sellers can maintain 1:1 hyper-personalization at scale.”

However, the vast majority of marketers are just getting started with AI, Roetzer noted.

When asked, “Which stage of AI transformation best describes your marketing team?,” here’s how marketers responded:

  • 67%: Understanding: Learning how AI works and exploring use cases and technologies.

  • 36%: Piloting: Prioritizing, and starting to run, a limited number of quick-win pilot projects with narrowly defined use cases.

  • 15%: Scaling: Achieving wide-scale adoption of AI while consistently increasing efficiency and performance.

Further, 45% consider themselves as “beginner” when asked, “How would you classify your understanding of AI terminology and capabilities?” This was the number one response — followed by intermediate (43%) and advanced (12%).

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