Link: https://research.ebsco.com/c/6sgjnr/viewer/pdf/d3cfsjpdyf

Citation: Haenlein, M. (2026). AI in marketing intelligence: To boldly go where no research has gone before. NIM Marketing Intelligence Review, 18(1), 10–17. https://doi.org/10.2478/nimmir-2026-0002

Artificial intelligence continues to be one of the biggest developments in data analytics, but its role is expanding far beyond simple automation. In the article AI in Marketing Intelligence: To Boldly Go Where No Research Has Gone Before, Michael Haenlein (2026) discusses how AI is transforming market research and marketing analytics by helping organizations collect, analyze, interpret, and communicate data more efficiently.

One of the key topics covered in the article is the emergence of AI-powered research systems. According to Haenlein (2026), AI can already assist with creating research questions, developing surveys, detecting fraud and bot activity, analyzing large datasets, identifying patterns, and generating reports. The article also discusses future developments such as autonomous research planning, synthetic consumer data, AI-generated focus groups, and personalized reporting tools. Rather than simply helping researchers complete individual tasks, AI is beginning to influence the entire research process from start to finish.

This trend has the potential to significantly impact the field of data analytics. Traditionally, analysts spend a large amount of time gathering data, cleaning datasets, organizing information, and preparing reports. AI can automate many of these routine activities, allowing analysts to spend more time interpreting results and making strategic recommendations. Organizations may also be able to make faster decisions because AI can process large amounts of information more quickly than humans. Additionally, AI can help identify patterns and insights that may otherwise go unnoticed.

At the same time, the article highlights several concerns that organizations must address. As AI becomes more involved in data collection and analysis, questions arise regarding data quality, bias, transparency, and accuracy. If organizations rely too heavily on AI-generated insights without proper oversight, they risk making decisions based on incomplete or flawed information. The author argues that human judgment will remain essential, especially when making important business decisions and evaluating the validity of research findings.

Overall, I view this trend as mostly positive. AI has the potential to make data analytics more efficient, accessible, and valuable for organizations by helping analysts process large amounts of information and identify insights more quickly. However, I also think AI is a double-edged sword. While it can improve productivity and decision-making, it comes with concerns related to bias, transparency, and the significant resources required to develop and operate these systems. As organizations continue adopting AI, it will be important to balance innovation with responsible use. I do not believe AI should replace human analysts. Instead, AI should be used as a tool that supports human expertise, critical thinking, and ethical decision-making. In my opinion, the organizations that find that balance will gain the greatest benefit from this technology.

Reference

Haenlein, M. (2026). AI in marketing intelligence: To boldly go where no research has gone before. NIM Marketing Intelligence Review, 18(1), 10–17. https://doi.org/10.2478/nimmir-2026-0002