Forrester: B2B marketers lead the AI revolution

Business to business (B2B) marketing teams are charging ahead with the adopting artificial intelligence (AI) technology and machine learning (ML) across a wide range of use cases. 

According to a study by Forrester titled The state of artificial intelligence and machine learning adoption In B2B marketing 2024, 25% of B2B marketing teams are using AI/ML in production, while 24% use it for advertising and media buying. Similarly, B2B marketing teams are using AI/ML for marketing analytics (24%) and personalisation (24%) too, while 23% of B2B marketing teams use it for predictive scoring. 

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Rounding up other uses of AI and ML are chatbots and virtual assistances (22%), marketing automation and tactic orchestration (21%), social media intelligence and management (21%), segmentation (21%) and intent monitoring (20%). 

Of these AI adopters, 23% are considered leading adopters, based on the breadth and depth of their adoption against other companies. Around 42% of leading adopters already have generative AI (gen AI) systems in production, compared to 11% of other respondents. 

These leading adopters also skew toward technology-adjacent industries such as high-tech manufacturing, business services including IT and tech services, and financial services and insurance. 

According to Forrester, these tech-savvy industries are best equipped to take advantage of AI technologies due to its data-rich nature. This is especially since these industries have experience using data to drive decision-making, and already have strong data governance processes. 

Potential challenges 

While leading adopters share an ability or willingness to invest in technology, many have encountered significant challenges with gen AI related to IP protection and data privacy. 

They also worry about a talent gap and data issues, said Forrester. 

When asked to indicate the most significant challenges for their marketing organisation in achieving goals in the next 12 months, 25% of leading adopters report that poor data accessibility is a challenge compared to just 19% for other respondents.

To mitigate the data challenges, leading adopters can embrace a data centre of excellence. Conversely, leading adopters are less concerned about aligning business goals and expectations, 17% as compared to 21% for other respondents.

In addition, leading adopters run into challenges finding the right talent to lead their AI efforts. Approximately 21% of leading adopters are concerned about recruiting the right talent versus just 16% of all other respondents.

Given the intense competition for AI talent, it might be wise for B2B marketers to look inward and fill these gaps by upskilling current employees, said Forrester.

Specifically related to gen AI, the leading adopters signal unique challenges and obstacles. At 34%, data privacy and security were the most commonly cited challenge for leading adopters as compared to 29% for non-leading adopters.

They also cited challenges integrating with existing infrastructure at 27% as compared to 20% and copyright concerns at 27% for leading adopters but just 18% for other respondents. 

Interestingly, according to a separate report by CleverTap, 81% of marketers have reported that their organisation is currently using GPT models such as OpenAI’s ChatGPT and Google’s AI Gemini, formerly Bard.

More specifically, 55% of marketers say their organisation’s adoption of GenAI can best be described as partially integrated across selected marketing functions. 71.4% of respondents also said that AI capabilities are most extensively utilised by content teams, underscoring the pivotal role AI plays in content generation.

While 82% of respondents have realised an increase in operational efficiency with AI by streamlining workflows and automating content generation, 54% of marketers have also used AI to achieve faster content generation and campaign rollout.

Other popular uses of AI by marketers include creating content that resonated with the emotions of the user (43%), automating the creation of segments based on the probability of conversation (39%) and running experiments at scale (39%).

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