ChatGPT is an AI-powered chatbot platform that enables human users to converse with machines. It utilizes natural language processing and machine learning algorithms, transforming how people interact with AI technology. ChatGPT offers significant advantages over previous similar tools, and its potential for application in various fields has generated attention and anticipation. However, some experts are wary of ChatGPT, citing ethical implications. Therefore, this paper shows that ChatGPT has significant potential to transform marketing and shape its future if certain ethical considerations are taken into account. First, we argue that ChatGPT-based tools can help marketers create content faster and potentially with quality similar to human content creators. It can also assist marketers in conducting more efficient research and understanding customers better, automating customer service, and improving efficiency. Then we discuss ethical implications and potential risks for marketers, consumers, and other stakeholders, that are essential for ChatGPT-based marketing; doing so can help revolutionize marketing while avoiding potential harm to stakeholders.

Artificial intelligence (AI) marketing systems increasingly plan campaigns, generate messages, assess performance, and revise outputs with limited human input. In such multi-agent systems (MAS), commercial optimization pressure may produce ethical compliance drift: gradual movement from acceptable persuasion toward detector-defined manipulation-risk signals. We examine this problem in a controlled OpenClaw simulation of a three-role AI marketing team. The study varies optimization pressure across baseline, moderate-pressure, and high-pressure operating regimes while holding the model backend, prompt pool, agent roles, and monitoring process constant. Optimization pressure significantly affected detector-defined manipulation-risk scores, and the observed pattern was non-monotonic. The moderate-pressure condition produced the highest observed mean, but moderate and high pressure were not statistically distinguishable in the pairwise comparison. These results reflect detector-based risk indicators in simulated marketing-agent outputs, not direct evidence of consumer harm, deception, or real-world behavioral manipulation. The findings support pressure-aware auditing as a standards-informed monitoring practice consistent with IEEE value-sensitive design principles, while also identifying the need for human annotation, hybrid detectors, and real-user validation before stronger governance claims are made.