Key Takeaways
- AI is moving beyond marketing execution into marketing decision-making.
- The marketer’s role is shifting from making routine decisions to designing and governing the decision process. It has become increasingly important to define where human judgment should intervene.
- Data quality is as important as model quality. AI can only make good decisions if it learns from the right data.
- Trust in AI varies with the consequences of the decision. The greater the business risk, the greater the need for human judgment.
- The appropriate balance between AI and human judgment depends on the business context, the marketing task, and the risks involved.
- Organizations will gain the greatest advantage when AI and marketers complement each other’s strengths rather than compete for the same decisions.
Marketing is one of the first business functions where AI is moving beyond execution into decision-making. Some marketers are using it to personalize email campaigns and customer journeys. Others rely on recommendation engines that learn a customer’s preferences and suggest products accordingly. AI is helping retailers recover abandoned shopping carts, predict what customers are likely to buy next, automate digital advertising, and improve sales forecasting. In luxury retail, customer data collected in physical stores is being combined with digital interactions to build richer customer profiles, while other companies are using AI to anticipate customer needs before they are explicitly expressed.
When Should Marketers Step In?
The more decisions AI makes, the more important it becomes to define the role of the marketer. Should AI simply recommend an action, or should it be allowed to act on its own? When should marketers intervene? Which decisions should remain entirely human? As AI adoption matures, these questions become central to the design of the marketing process itself rather than the technology behind it.
Consider an AI system that recommends the next product a customer is likely to buy. At one level, this appears to be a product recommendation problem. In reality, it is a chain of decisions. Which customer data should be used? How much historical behavior should be considered? Should recent purchases outweigh long-term preferences? When should the recommendation be ignored because a salesperson knows something the system does not? As AI assumes responsibility for more of these decisions, marketers are increasingly responsible for deciding how AI should decide.
What Senior Marketing Leaders Are Learning About AI Decision-Making
In a paper published in the journal Management Decision, Professor Simone Guercini of the University of Florence interviewed 22 senior marketing leaders from industries including retail, luxury, pharmaceuticals, financial services and technology to understand how AI is changing marketing decision-making.
One of the clearest findings is that AI does not replace managerial judgment; it changes where that judgment is applied. As organizations progress from marketing automation to machine learning, marketers spend less time making routine operational decisions and more time deciding how AI should be used. They determine what data the system should learn from, where AI should be applied, how its recommendations should be interpreted, and when business context requires human intervention. In other words, AI increasingly supports or generates marketing decisions, while marketers become responsible for governing the decision-making process itself.
The study also found that this shift is neither uniform nor complete. The appropriate balance between AI and human judgment depends on the business context, the quality of available data, and the nature of the marketing task. Organizations with large volumes of customer data can delegate more decisions to AI than those operating in complex B2B environments with relatively few customers or transactions. Similarly, AI performs well in areas such as personalization, recommendation engines and forecasting, but marketers continue to play a central role in validating inputs, interpreting outputs and exercising commercial judgment where experience and context matter.
How Much Should Marketers Trust AI?
One of the strongest themes to emerge from the interviews was not resistance to AI, but caution. None of the executives questioned the value of AI in improving productivity, personalization or prediction. Their concern was different: how much confidence should be placed in decisions that are increasingly generated by systems whose internal logic is often difficult to understand.
Several interviewees described AI as a “black box.” While machine learning systems can recognize patterns and produce recommendations that improve over time, marketers may not always understand how those recommendations were reached. That creates a practical management challenge. Commercial decisions carry financial, regulatory and reputational consequences, yet accountability continues to rest with people rather than algorithms. One executive explained that although AI-generated forecasts provided an excellent starting point, they would hesitate to commit to a major production decision solely because the system predicted an unexpected surge in demand. If the prediction proved wrong, the responsibility would remain theirs.
Trust was also closely linked to the quality of the data. Multiple interviewees emphasized that AI is only as reliable as the information it learns from. One recalled an example in which an AI system generated poor medical recommendations because it had been trained on an incomplete dataset. Others pointed to areas such as sentiment analysis, where current AI systems still struggle with short text, non-English languages and contextual interpretation. Rather than accepting AI outputs uncritically, the executives consistently described a process in which AI generates recommendations while marketers validate the data, interpret the results and apply business judgment before acting.
Designing the Decision Process
Organizations are in the process of redesigning decision-making processes to take advantage of the power of AI for improved personalization, faster execution and more accurate predictions.
Marketing leaders need to determine where AI should be given decision-making authority, where human judgment should remain central, and how the two should work together. That requires thoughtful decisions about data, governance, accountability and commercial judgment.
The organizations that benefit most from AI will be the ones that understand which decisions AI can make better than humans, which decisions require human experience and context, and how to combine the strengths of both into a coherent marketing process.
