Digital pull

The first half of the 19th century, Victorian England. While the industrial revolution celebrated mechanical precision, Ada Lovelace - daughter of the poet Lord Byron - saw something that others overlooked.

Charles Babbage built the Analytical Engine, an early mechanical computer designed for complex calculations. Most people saw it as nothing more than a sophisticated calculating machine. Ada Lovelace, who worked with Babbage, wrote what we now call the first computer program. However, her real breakthrough was of a conceptual nature: she realized that the machine could process any symbols - not just numbers, but also letters, notes and patterns. "The machine could compose artistic and scientific pieces of music of any complexity and length," she wrote. What looked like a calculator could become a creative tool. Technology and culture were not separate - they would shape each other.

Two centuries later, we are repeating the same mistake that Ada's contemporaries made. Today, AI can translate 100 languages instantly, analyze cultural moods and adapt marketing messages across borders. According to a study by McKinsey in 2024, 65 % of companies now use generative AI, with its use in marketing more than doubling in the last year. According to estimates by Precedence Research, the global market for artificial intelligence in the marketing sector could grow from around USD 25.83 billion in 2025 to around USD 217.33 billion in 2034.

The visible successes are impressive. Nestlé has introduced AI-powered content with digital twins - virtual 3D replicas of products. This service reduces the time and cost of scaling digital twins by over 70 % and aims to convert 10,000 products within two years. Dove has published the "Real Beauty Prompt Playbook", which creates a framework to drive AI to more representative results and turn a potential bias problem into brand leadership.

From surface waves to digital suction

These are the dramatic waves on the surface - visible, measurable, celebrated. But MIT organizational scholars Wanda Orlikowski and Susan V. Scott have identified something equally powerful at work beneath these success stories: the digital pull. The pull is not reflected in dashboards or performance metrics, but in subtle changes in how organizations understand knowledge and define insights, relevance and value.

AI in marketing is therefore not a neutral optimization tool, but an ongoing process that - through everyday, often unnoticed actions - reconfigures markets, consumers and the boundaries of choice and creates a digital pull over time.

More "intelligence", less understanding?

Philip Kotler's Marketing 5.0 formulated "technology for humanity" and positioned AI as a supplement to, rather than a replacement for, human judgment. This concept assumes that points along the customer journey can be identified where humans and machines can be aligned to deliver greater value. What this vision did not foresee was that the efficiency of AI would supersede human judgment interpreting cultural meanings.

This is the invisible current that is reshaping companies' understanding of cultural knowledge itself and the role of people.

Efficiency on a large scale

In international marketing, AI is highly valued for its ability to run campaigns simultaneously in multiple markets. Research and practice highlight its role in optimizing resources, synchronizing global operations and supporting tasks such as ad placement, performance monitoring and large-scale A/B testing through advanced analytics. It also enables customer support across time zones and languages and improves localization through automated translations and virtual assistants.

These capabilities make AI particularly attractive in cross-border contexts. At the same time, they create the conditions under which the digital current is beginning to take shape. When efficiency pressures favor automated decisions and scalable patterns, human judgment runs the risk of being repositioned from sense-making to exception handling. In this configuration, human expertise is no longer primarily concerned with the interpretation of meaning, but only intervenes when algorithmic results fail, misfire or appear anomalous.

To understand how this narrowing takes place in practice, we need to look beneath the surface of AI-supported international marketing.

In the maelstrom: two invisible changes / dynamics

1. when culture becomes an optimization target

Culture functions as a living system of shared meanings - contextual, contested, constantly renegotiated. Cultural symbols carry weight because they are embedded in history, social relationships and collective experiences.

AI-driven marketing systems deal with culture differently. They identify patterns, correlations and statistical relationships. A color, phrase or visual motif becomes a variable whose value is determined by its measured impact on engagement metrics. The question is no longer "What does this symbol mean in this cultural context?", but "Which variant generates the strongest reaction?". The former requires interpretation, the latter only measurement.

Marketing campaigns that are optimized for engagement can unintentionally reinforce cultural stereotypes - because the optimization process favors simplistic, easily recognizable cultural cues over nuanced representations. Training data collected in specific cultural contexts encodes assumptions about preferences and behaviors that do not translate well to other markets. Feedback loops then stabilize what works best in the short term. What disappears in this process is not the culture itself, but the interpretative work through which cultural meaning is made visible, contested and renegotiated. Leading companies are tackling this problem head on. Dove's approach is instructive: instead of simply using AI and hoping for the best, they've created the Real Beauty Prompt Playbook - guidelines to help people using generative AI avoid stereotypical representations and promote more inclusive visual outcomes.

2. the question of authenticity

Marketing has always involved constructed narratives, but the relationship between message and messenger was fraught with implicit assumptions. When a brand told a story, human authorship was assumed - authors who made decisions, designers who used their judgment, strategists who discussed alternatives. Generative AI challenges this assumption. Content that appears culturally sophisticated can be algorithmically generated, adapted in real time and deployed without meaningful human control. For consumers, especially in markets where trust is built through personal relationships, this leads to real ambiguity. The mechanisms of trust building differ significantly depending on the culture. Individualistic cultures value performance-based trust - they evaluate AI systems for accuracy, efficiency and tangible benefits. They prioritize cognitive trust: the rational evaluation of competence and reliability. Collectivist cultures prioritize relationship-based trust - they consider social recognition, institutional support and community acceptance as primary factors. They strive for affective trust: emotional connections and perceived responsiveness, not just efficiency. The same AI-driven communication strategy can resonate very differently in different markets, leading to fragmentation where brands are actually aiming for consistency.

Brands that build lasting cross-cultural trust are transparent about their AI usage while emphasizing human control. They position AI as an extension of capabilities, not a substitute for human creativity and judgment. They build something like "cultural credibility signals" - markers that show that humans with cultural expertise are still involved in the decision-making process. Here too, human involvement is maintained, but primarily as a safety net and repair - not as an ongoing cultural sense-making process.

The hybrid path to the future

These dynamics do not emerge as problems. Marketing teams continue to achieve their KPIs. Campaigns continue to be launched. Performance dashboards show green scores. The undercurrent works precisely because it remains below the surface of normal organizational attention. The solution is not to abandon AI - that would be strategically wrong given its actual capabilities. The solution is hybrid intelligence: AI provides scalability, speed and pattern recognition. Human intelligence provides context, interpretation and meaning. Separately, they are insufficient. Together, they are transformative.

In practice, this means embedding cultural review protocols. Make room for human judgment around cultural appropriateness, invest in roles that translate data into cultural insights, document when human judgment overrides algorithmic recommendations, and develop metrics that capture not just engagement but resonance.

Beyond the suction effect

Philip Kotler envisioned Marketing 5.0 as "technology for humanity" - a future where AI augments human insights rather than replacing them. This future is still possible. But it requires what Ada Lovelace understood two centuries ago: The most powerful innovations occur when technical capabilities meet human intelligence. The pull is real. It is already changing how companies develop and apply cultural understanding. But it is not a force to be resisted - it is a dynamic to be dealt with intelligently. Technology will continue to evolve regardless. Whether it enhances or undermines human intelligence depends entirely on the decisions we make now. The good news is that we have examples of how this can work. Nestlé, Dove and others show what it can look like when organizations choose to actively participate in how AI reshapes cultural meanings - rather than leaving that dynamic entirely to technology.

The question is whether we will follow this example - or whether we will let ourselves be driven by the pull until we realize that we have lost something irreplaceable.

"When a brand told a story, people assumed human authorship, AI calls that into question."

The author

Dr. Magdalena Zabicka- Wlodarczyk is a researcher and practitioner with over 15 years of industry experience in a multi-national retail company, specializing in market research, cross-cultural collaboration and strategic decision making. She lectures on customer behavior in the context of new technologies, including artificial intelligence and cross-cultural marketing. She is currently a visiting researcher at the ZHAW. Her research focuses on human-centered approaches to AI, such as diagnosing employee attitudes towards AI and examining the role of leaders in its adoption. She is also involved in initiatives to promote AI literacy in education and training.