Marketing Managers

Many marketing teams already use AI for data-heavy tasks. For example, machine learning helps scan and analyze market research data, customer feedback, or sales trends far faster than a person could ^[1]. Generative AI tools can even create marketing materials – like advertising images, catchy copy or video voice‐overs – with no human artist required ^{[2] [1]}.

This lets even small companies produce professional ads without a big agency, and helps managers quickly test ideas. However, AI often produces templated results. Studies find that AI-made marketing content can be repetitive or “homogenized” compared to human work ^[2].

In practice, marketing managers still guide high-level tasks. They interpret the insights from AI tools and make final strategy decisions or creative choices. Complex or people-focused tasks – like negotiating copyright issues or planning live events – remain human-driven, since we haven’t seen reliable AI solutions there.

In short, AI is augmenting strong analytical and creative tasks (data crunching, ad creation, personalization) ^{[2] [1]}, but key skills like creativity, judgment and leadership are still done by people.

Adoption of AI in marketing is growing fast but uneven. On the plus side, many AI marketing tools are now commercially available. Marketers report using AI to improve customer insights, personalization, and campaign targeting ^[1].

In one survey, nearly all companies had started using AI in the past few years and saw it boost returns ^{[1] [2]}. Larger firms with lots of data and data scientists adopt AI quicker, since they can afford software and training ^[1]. For example, firms expect to increase AI use by over 50% in coming years to optimize ads and forecast sales (turning data into strategy) ^[1].