Introduction
Artificial intelligence in marketing is one of the defining topics of contemporary marketing history. It affects almost every area of modern marketing: market research, customer segmentation, search engine optimization, content creation, social media, advertising, personalization, customer service, marketing automation, lead scoring, pricing, competitor monitoring, image generation, video production and strategic planning. Since the public release of ChatGPT on 30 November 2022, artificial intelligence has moved from the background of enterprise software into the everyday working environment of marketers, agencies, founders and communication teams (OpenAI, 2022).
Yet artificial intelligence in marketing did not begin with ChatGPT. ChatGPT made AI visible, conversational and accessible, but the historical roots are much older. The development reaches back to the formal birth of artificial intelligence as a research field in the 1950s, the rise of expert systems, database marketing, customer relationship management, recommendation engines, programmatic advertising, machine learning, marketing automation and finally generative AI. What changed after 2022 was not simply the existence of AI, but the interface. Marketers no longer needed to interact only with dashboards, rules, code or specialist tools. They could speak to a system in natural language and ask it to draft, summarize, compare, translate, ideate, structure, classify and optimize.
From a marketing-historical perspective, this development is highly significant. Philip Kotler’s understanding of marketing as the creation, communication and delivery of value provides a useful theoretical foundation (Kotler and Keller, 2016). AI affects all three dimensions. It helps marketers identify customer needs, design value propositions, generate communication and deliver personalized experiences. Hartmut Berghoff’s historical view of marketing as a modern social technique is equally relevant because AI shows how marketing has always been connected to technologies of observation, persuasion, measurement and behavioral influence (Berghoff, 2007; Berghoff, Scranton and Spiekermann, 2012). The research traditions represented by CHARM and the Journal of Historical Research in Marketing also matter here because they place marketing practices within long-term technological, institutional and cultural developments rather than treating them as isolated management tools (CHARM, 2026; Emerald Publishing, 2026).
The Historical Roots of Artificial Intelligence
The term “artificial intelligence” is usually traced to the Dartmouth Summer Research Project. In the 1955 proposal, John McCarthy, Marvin Minsky, Nathaniel Rochester and Claude Shannon proposed a two-month research project at Dartmouth College in the summer of 1956. The proposal expressed the famous assumption that every aspect of learning or intelligence could, in principle, be described so precisely that a machine could simulate it (McCarthy et al., 1955).
At first, this research field seemed far removed from marketing. In the 1950s and 1960s, marketing was shaped more visibly by consumer research, mass media, brand management, retail expansion, motivation research and the growing academic discipline of marketing management. However, the Dartmouth proposal introduced a long-term idea that would later become central to marketing: human reasoning, classification, language and decision-making could be partly formalized and supported by machines.
Early AI was mainly symbolic and rule-based. Researchers tried to represent knowledge through explicit rules. For marketing, this was a distant but important foundation. Later expert systems, CRM rules, campaign triggers, scoring models and automated recommendations would all inherit this basic ambition: to translate knowledge about customers and markets into repeatable machine-supported decisions.
From Market Research to Data Intelligence
Marketing was data-driven long before the term “AI marketing” became popular. Companies used surveys, panels, test markets, sales reports, household data, coupons, loyalty cards and direct-response advertising to understand customers and measure demand. The historical difference between traditional market research and modern AI is not the desire to know the customer. It is the scale, speed and automation of that knowledge.
Berghoff, Scranton and Spiekermann show that modern marketing and market research developed together as firms learned to observe consumers more systematically (Berghoff, Scranton and Spiekermann, 2012). AI continues this historical trend. Where earlier market research often depended on samples, interviews and periodic studies, AI can process search behavior, transaction records, CRM data, reviews, social media reactions, customer service transcripts and competitor movements in near real time.
This changes the relationship between intuition and analysis. Marketing has always combined creative judgment with empirical control. AI shifts the balance because it can detect patterns too large or subtle for human teams to process manually. At the same time, AI does not remove the need for interpretation. A model can identify correlations, but it does not automatically understand cultural meaning, brand history, ethical implications or long-term positioning.
Expert Systems, CRM and the Prehistory of AI Marketing
Before generative AI became visible, many AI-like systems were already embedded in marketing. In the 1980s and 1990s, companies experimented with expert systems, database marketing and decision-support tools. Later, CRM platforms, marketing automation and analytics systems became standard in professional marketing departments. These tools were not always called artificial intelligence, but they performed tasks that are now associated with AI: segmentation, scoring, prediction, personalization and automated communication.
Lead scoring is a good example. A potential customer receives points for behaviors such as visiting a website, downloading a white paper, opening an email, attending a webinar or filling out a form. Earlier systems often used fixed rules. Modern AI systems can learn from historical conversion patterns and update scoring logic dynamically. This turns sales readiness from a static assumption into a probabilistic model.
Recommendation engines were another important prehistory of AI in marketing. Amazon, Netflix, YouTube and Spotify demonstrated that recommendations could become part of the product experience itself. A recommendation is not merely a service feature. It is also merchandising, personalization, retention and persuasion. AI therefore moved marketing from general messaging toward individualized interfaces.
AI in Marketing Science
Academic marketing research has examined AI with growing intensity since the 2010s. Davenport et al. argued that AI would substantially change both marketing strategies and customer behavior, proposing a multidimensional framework based on intelligence levels, task types and whether AI is embedded in robots (Davenport et al., 2020).
Huang and Rust developed a strategic framework for artificial intelligence in marketing that distinguishes mechanical AI, thinking AI and feeling AI. Mechanical AI automates repetitive marketing tasks, thinking AI processes data to support decisions, and feeling AI analyzes interactions, emotions and customer relationships (Huang and Rust, 2021). Their earlier work on AI in service also identified mechanical, analytical, intuitive and empathetic intelligence as different kinds of service-related capabilities (Huang and Rust, 2018).
This academic distinction is useful because it prevents a narrow view of AI as merely text generation. In marketing, mechanical AI may automate bidding, reporting or campaign rules. Thinking AI may support segmentation, forecasting, product recommendations or customer lifetime value models. Feeling AI may support chatbot interactions, sentiment analysis, social listening or service recovery. Together, these functions show that AI is not one tool but a family of technologies reshaping the entire marketing process.
ChatGPT and the Breakthrough of Conversational AI
The public release of ChatGPT in November 2022 marked a turning point because it made advanced language AI directly usable through conversation. OpenAI described ChatGPT as a model that interacts in a conversational way, can answer follow-up questions, admit mistakes, challenge incorrect premises and reject inappropriate requests (OpenAI, 2022).
For marketing, this was transformative. ChatGPT made artificial intelligence usable not only for data scientists but for copywriters, SEO managers, social media teams, product marketers, consultants and entrepreneurs. A marketer could ask for headline variations, campaign concepts, audience personas, article outlines, email sequences, competitive positioning or translation drafts. Natural language became the interface to marketing productivity.
Historically, this resembles earlier shifts such as the printing press, typewriter, desktop publishing, search engines and social media platforms. Each changed not only the speed of communication but the structure of professional work. ChatGPT changed marketing because language itself became a tool for controlling software. The prompt became a new form of brief.
However, ChatGPT should not be understood simply as an automated copywriter. In professional marketing, it can function as a strategist’s assistant, research organizer, editorial sparring partner, translation tool, content variation engine, workflow component and analytical interpreter. Its value depends on context, quality control and human direction.
Generative AI and the Expansion of Marketing Production
Generative AI creates new content rather than only classifying or predicting existing data. It can generate text, images, code, audio, video concepts, synthetic voice, design variants and campaign ideas. This has direct relevance for marketing because marketing is a content-intensive discipline.
Content marketing, advertising, SEO, social media, product descriptions, landing pages, email nurturing, video scripts, white papers, sales enablement and internal presentations all require continuous production. Generative AI accelerates this production dramatically. What once required many hours of drafting can now be prototyped in minutes.
The deeper change is not only speed but variation. A marketing team can create several headline directions, rewrite copy for different personas, adapt content to multiple regions, translate campaign material and generate image concepts for testing. Marketing becomes more experimental and iterative.
At the same time, generative AI creates a quality problem. If many companies use similar tools with similar prompts, outputs may become generic. The competitive advantage will not come from using AI alone. It will come from brand knowledge, strong editorial direction, proprietary data, original research, cultural insight and human judgment.
AI in Content Creation and SEO
Content creation is one of the most visible applications of AI in marketing. AI tools can help develop article structures, meta descriptions, FAQ sections, social posts, email sequences, product copy, video scripts and translation drafts. For SEO, AI can support keyword clustering, search intent analysis, topic mapping, internal linking suggestions and content refresh planning.
However, AI-generated SEO content is not automatically valuable. Search visibility depends on relevance, expertise, originality, trustworthiness and usefulness. Especially for historical, scientific or technical topics, AI must be combined with careful source work. A long article is not the same as a good article.
For a website such as marketing.museum, AI should therefore be used as a research and structuring assistant rather than as a substitute for historical judgment. The publicly visible orientation of marketing.museum already emphasizes marketing history, early trade practices, branding and communication. A strong article on AI in marketing should therefore not repeat general historical origin narratives, but show how AI continues the long history of marketing as observation, persuasion and market organization.
AI and Personalization
Personalization is one of the most important areas of AI marketing. The idea is not new. Direct mail, catalogues, loyalty cards and CRM systems already tried to address customers more individually. AI increases the precision and speed of personalization by processing many signals at once: purchase history, browsing behavior, search terms, location, interaction patterns, service history and predicted preferences.
In practice, AI can personalize product recommendations, website content, email timing, advertising creatives, pricing, chatbot responses and customer journeys. Done well, this increases relevance and reduces friction. Done poorly, it can feel invasive, manipulative or inaccurate.
This is why personalization is also an ethical issue. The more precisely marketing adapts to an individual, the more important transparency and trust become. Customers may appreciate relevance, but they may reject the feeling of being monitored. AI therefore intensifies an old marketing tension: the desire to know the customer versus the customer’s desire to retain autonomy.
AI and Marketing Automation
Marketing automation connects data, triggers, content and channels. AI adds prediction and adaptive decision-making. Email campaigns can adjust based on user behavior. Lead scoring can become more dynamic. Advertising systems can optimize bids and creative combinations. Chatbots can answer common questions. Social listening tools can identify emerging issues.
Workflow automation platforms now allow marketers to connect AI with CRM systems, spreadsheets, analytics platforms, content tools and communication channels. A competitor update can be summarized automatically. A customer review can be classified by sentiment. A webinar registration can trigger a personalized follow-up. A lead can be enriched, scored and routed to sales.
Historically, this is part of the rationalization of marketing work. Marketing has moved from manual campaigns toward modular, measurable and automated systems. AI accelerates this transition. The role of marketers shifts from repetitive execution toward process design, quality control, brand governance and strategic interpretation.
AI in Market Research and Competitive Intelligence
AI changes market research because it can process large volumes of unstructured data. Reviews, customer support logs, social media comments, competitor websites, product descriptions, job postings, press releases and search trends can be analyzed for patterns. Natural language processing helps identify recurring topics, complaints, desires and emerging market signals.
This is historically important because market research has always been a central part of modern marketing. Earlier firms struggled to collect enough information about customers. Today, the challenge is often too much information. AI becomes a filtering technology. It helps marketers distinguish signals from noise.
Competitive intelligence also becomes more continuous. Companies can monitor competitor landing pages, pricing, ad messages, product launches, keyword movements and customer sentiment. Instead of conducting occasional market studies, marketers can build ongoing observation systems. This makes strategy more responsive, but it also increases the pressure to react quickly.
AI, Creativity and Human Judgment
One of the most important questions is whether AI makes marketing more creative or more generic. The answer depends on how it is used. AI can produce ideas, variations and unexpected combinations. It can help overcome creative blocks and accelerate first drafts. It can also produce average, derivative and brandless content if used without direction.
Creativity in marketing is not only the production of novelty. It requires cultural relevance, brand coherence, audience understanding and strategic purpose. AI can assist these processes, but it does not automatically possess brand responsibility. It does not know what a company should stand for unless humans define it.
The most effective use of AI in creative marketing is therefore not replacement but augmentation. AI can generate options; humans select, refine and give meaning. AI can accelerate production; humans protect identity. AI can suggest patterns; humans decide what matters.
Ethics, Bias, Copyright and Trust
AI in marketing raises major ethical questions. Hermann systematically analyzes ethical concerns in AI marketing from a multi-stakeholder perspective and emphasizes that AI reshapes business strategy, activities, interactions and relationships while also creating ethical controversies (Hermann, 2022).
One issue is factual reliability. Large language models can produce plausible but false information. In marketing, this can lead to incorrect claims, invented sources, misleading product descriptions or reputational damage. Historical and scientific content requires especially careful verification.
Another issue is bias. AI systems can reproduce stereotypes or unfair patterns from training data. This may affect targeting, personalization, image generation and customer service. A campaign optimized only for efficiency may unintentionally exclude or misrepresent certain groups.
Copyright and intellectual property are also major concerns. Generative AI can produce text, images, music and video concepts, but companies must ensure that outputs are legally safe and brand-appropriate. Marketing teams need clear policies for disclosure, review and acceptable use.
Trust becomes the central resource. AI can increase efficiency, but careless use can damage credibility. The question is not only whether AI can produce content, but whether the content is accurate, responsible and aligned with the brand.
AI and the Future of Marketing Work
AI changes marketing jobs. Routine tasks such as simple copy variations, summarization, reporting, translation drafts, image variants and standard responses are increasingly automatable. At the same time, new responsibilities emerge: prompt design, AI workflow management, data governance, model selection, fact-checking, ethical review and creative direction.
This does not mean that marketers disappear. It means that the skill profile changes. Marketers will need stronger strategic thinking, better editorial judgment, deeper data literacy and a clearer understanding of automation. The value of human work moves from manual production toward orchestration.
Historically, this pattern is familiar. The printing press, photography, radio, television, desktop publishing, the internet, search engines and social media all changed marketing work. AI is different because it affects language, analysis and production simultaneously. It is therefore likely to reshape marketing roles more broadly than many earlier tools.
AI as a New Social Technique of Marketing
Berghoff’s concept of marketing as a modern social technique is particularly useful for understanding AI (Berghoff, 2007). Marketing has always been concerned with shaping perception, preference and behavior. AI makes these processes faster, more personalized and more scalable.
This does not automatically mean manipulation, but it increases responsibility. If AI systems decide which message a customer sees, which offer is shown, which price is suggested, which lead is prioritized or which complaint is escalated, then AI participates in shaping social and economic reality.
The historical development of marketing has always involved tools of observation and influence: market research, advertising psychology, segmentation, database marketing, loyalty programs, search advertising and social media targeting. AI is the next stage in this development. It turns marketing into algorithmic relationship management.
The Next Phase: AI Agents and Autonomous Campaign Systems
The next stage of AI in marketing will likely involve AI agents. Unlike simple tools that perform one task, agents can plan and execute multi-step workflows. They may research a market, summarize competitors, draft campaign assets, configure ads, monitor results and recommend optimizations.
Multimodal AI will also become more important. Systems will process and generate text, image, audio, video and structured data together. This means a campaign brief may lead to landing page copy, ad images, video scripts, email sequences, social posts and analytics dashboards generated in one connected workflow.
The key question will not be whether companies use AI. It will be how well they integrate AI into strategy, brand, data and governance. When AI becomes common, differentiation will come from better questions, better proprietary knowledge, better creative judgment and better ethical standards.
Conclusion
Artificial intelligence in marketing is not a short-term trend. It is part of a long historical development from market research and database marketing to automation, personalization and generative communication. The Dartmouth proposal of 1955 introduced the research ambition of artificial intelligence. CRM, recommendation systems and programmatic advertising later embedded AI-like logic into marketing practice. ChatGPT made conversational AI broadly visible and usable.
AI now changes how marketers understand customers, create content, manage campaigns, automate workflows, observe competitors and personalize experiences. It makes marketing faster, more measurable and more adaptive. It also creates risks around accuracy, bias, copyright, privacy and trust.
The central historical lesson is clear: AI does not replace marketing. It transforms the tools, rhythms and responsibilities of marketing. Good marketing still requires human judgment, strategic clarity, cultural understanding, brand identity and ethical responsibility. AI can amplify these qualities, but it cannot substitute for them. For marketing history, artificial intelligence is therefore not merely a technological chapter. It is a new stage in the long evolution of marketing as a system for understanding, influencing and organizing markets.
References
Berghoff, H. (2007): Marketinggeschichte: Die Genese einer modernen Sozialtechnik. Frankfurt am Main: Campus.
Berghoff, H., Scranton, P. and Spiekermann, U. (eds.) (2012): The Rise of Marketing and Market Research. New York: Palgrave Macmillan.
CHARM (2026): Conference on Historical Analysis and Research in Marketing. CHARM Association.
Davenport, T., Guha, A., Grewal, D. and Bressgott, T. (2020): ‘How artificial intelligence will change the future of marketing’, Journal of the Academy of Marketing Science, 48(1), pp. 24–42.
Emerald Publishing (2026): Journal of Historical Research in Marketing. Bingley: Emerald Publishing.
Hermann, E. (2022): ‘Leveraging Artificial Intelligence in Marketing for Social Good—An Ethical Perspective’, Journal of Business Ethics, 179(1), pp. 43–61.
Huang, M.-H. and Rust, R. T. (2018): ‘Artificial Intelligence in Service’, Journal of Service Research, 21(2), pp. 155–172.
Huang, M.-H. and Rust, R. T. (2021): ‘A strategic framework for artificial intelligence in marketing’, Journal of the Academy of Marketing Science, 49(1), pp. 30–50.
Kotler, P. and Keller, K. L. (2016): Marketing Management. 15th edn. Harlow: Pearson.
McCarthy, J., Minsky, M. L., Rochester, N. and Shannon, C. E. (1955): A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence. Hanover: Dartmouth College.
OpenAI (2022): Introducing ChatGPT. San Francisco: OpenAI.
