By Frank Pagano, Shareholder, Advisor, Author
A Streetcar Named Desire
For many years, I thought of marketing as the science of human desire: understanding a need, translating it into a product, positioning it, narrating it, selling it. In the age of artificial intelligence, however, that grammar shows its limits. The company no longer speaks only with customers who buy, but also with citizens who produce data, workers who generate knowledge, suppliers who shape service quality, and communities affected by economic activity. For this reason, marketing—at least as we should understand it today—must go beyond communication, sales, and positioning toward a single group of users: fans, buyers, those who purchase. It must become a discipline for governing the relationship between economic value, social impacts, stakeholder listening, and the construction of trust.
In Total Marketing, which I wrote with Mara Cassinari, we described marketing as an evolution of Gartner’s Total Experience: beyond Customer Experience, Employee Experience, User Experience, and Multiexperience, toward an ecosystem in which consumers, employees, suppliers, partners, and communities are connected by data, technologies, and incentives. The aim is not only to make the commercial machine more efficient, but to increase social capital, reduce information asymmetries, improve decisions, and distribute value more equitably. In this perspective, AI becomes a cognitive infrastructure for seeing and acting within complexity that was previously invisible. Today we can observe more relationships, create more value, reduce waste, and make visible impacts and responsibilities that once remained at the margins. Yet opacity, conflicts, power asymmetries, and political choices remain. The question is therefore still open: do we want to use this new capacity for the common good?
Dataland
The first change concerns the subject of marketing. The classical consumer was defined by purchasing power, preferences, and brand loyalty. The citizen of the AI age is more complex: producer of data, bearer of rights, and node in a social network. Every interaction leaves a trace, and every trace can generate efficiency, innovation, and personalization, but also surveillance, manipulation, and exclusion. The question moves beyond “how can we know the customer better?” and becomes: under what conditions is it legitimate to know, interpret, and use what customers, employees, or citizens make available?
Marketing can offer a distinctive perspective here. It has always listened to the market, translated weak signals into proposals, built narratives, and measured reactions. In the age of AI, listening must mean designing relationship systems capable of treating data as participation rather than as a mine to be exploited. Every datum has an origin, a context, a risk, and a possible social return. At this juncture, marketing becomes a full managerial discipline.
Profit and/or Welfare?
The relationship between profit and welfare is central. Oliver Hart, with Luigi Zingales, argued that companies should look to shareholder welfare rather than only market value. When shareholders’ preferences include social, environmental, or civic goals, market value does not exhaust the company’s objective function. AI makes this discussion more urgent because it increases the capacity to measure, predict, and optimize. The question becomes simple and uncomfortable: what are we optimizing, and who decided that this is the right choice? When the objective function remains short-term margin, artificial intelligence becomes a machine of extraction: it improves targeting, reduces costs, automates work, increases conversion, and leaves imbalances intact—or even amplifies them. When, instead, the objective function includes trust, sustainability, quality of work, accessibility of services, and social impact, AI can become an accelerator of welfare. Welfare understood as the systemic quality of the value produced by the company, rather than as charity or reputational compensation.
Total Marketing proposes a different circuit, in which data, experiences, feedback, and responsibility reinforce one another. A medical device, an item of clothing, a banking service, or an energy network become meta-products: speaking systems accompanied by data, rights, certifications, traceability, and new forms of relationship. The customer becomes part of the information system that allows the proposition to improve.
Augmented Listening or Blind Optimization?
AI can expand companies’ ability to listen to opinions, preferences, and needs. It can read millions of interactions, identify patterns, predict friction points, personalize services, anticipate maintenance, reduce waste, suggest learning paths, improve diagnoses, and simplify access to financial or public services. In the language of Total Marketing, it can make operational the dream of one-to-one marketing: the right product, for the right person, at the right time. The same power can generate a symmetrical risk: reducing managerial judgment to optimization. The agentic enterprise, if badly governed, can become an automatic enterprise, where software agents make decisions on the basis of partial metrics, while managers merely validate outputs they only partly understand. Organizational culture is then replaced by dashboards, dialogue by scoring, discernment by algorithmic recommendations.
Here, AI governance becomes a managerial issue before it is a technical one. Governing AI means deciding which data to collect, why to collect it, who can access it, for what purposes, within what limits, with what possibilities for contestation, and with what return for those who generated it. It also means accepting that what is measurable may be irrelevant and what is efficient may be unjust. Human, cultural, and organizational criteria are the barrier that prevents computational power from becoming industrial myopia.
Data as Res Publica, then?
One of the strongest passages in Total Marketing concerns the nature of data. Data are company assets, individual property, individual rights, and traces of a relationship. They can be anonymized, aggregated, certified, monetized, shared, and protected; yet they retain a link with people, processes, territories, jobs, and rights. The question remains the same: who benefits from the value extracted from data?
The issue is not a utopia in which every datum is paid for directly. It concerns the opaque pact on which the digital economy has built part of its expansion: people surrender information, platforms and companies extract value, and society manages the externalities. Total Marketing suggests a different pact: data generated along the network must produce value for the network, through better services, fairer incentives, transparent supply chains, waste reduction, broader access to welfare, and new metrics of trust.
Are We Still Talking About Sustainability?
Sustainability, in this perspective, ceases to be a separate chapter in the report or a communication promise. It becomes the consequence of the company’s ability to know and govern its own ecosystem. An opaque supply chain leaves sustainability in the realm of declaration. A supply chain that is traced, measurable, and connected to incentives can transform sustainability into managerial practice. AI, together with technologies such as blockchain, digital twins, and digital identity systems, can transform sustainability from narrative into infrastructure.
Marketing as Welfare Design
If we take this trajectory seriously, marketing becomes a form of welfare design. Without replacing the State, it designs economic relationships that affect people’s well-being. A banking app that helps avoid financial mistakes, a healthcare system that uses real data to improve devices and care, an educational platform that personalizes learning, an energy network that reduces waste and costs, a food supply chain that rewards sustainable behaviors: all these are examples of marketing if we understand marketing as the governance of the relationship between needs, data, value, and trust. Traditional marketing asked: how do we convince people to choose us? Total Marketing asks: how do we build a system in which choosing us also improves the quality of the relationship among company, people, and community? In the first case, the customer is the target of a strategy. In the second, they are a co-producer of value, a subject of data and rights, and a participant in an ecosystem.
This vision keeps conflicts alive. Shared value arises from choices of governance, metrics, investments, limits, and negotiations. The same AI can be used to include or exclude, simplify or surveil, emancipate or create dependency. The question concerns the institutional and managerial design that orients it.
By What Criterion?
In the end, the most important issue is cultural before it is technical: who decides the criterion? AI can produce texts, images, forecasts, segmentations, recommendations, simulations, and diagnoses. Every output responds to a criterion, explicit or implicit. Management in the age of AI must become a culture of criteria: declaring what counts, why it counts, for whom it counts, within what limits, and with what responsibility. What are your values? This is the question for every marketing expert in the age of AI.
A Concrete Example of Total Marketing? Nestlé and the Milk Supply Chain
In Switzerland, Nestlé has worked on the milk supply chain with a precise objective: to reduce greenhouse gas emissions in dairy production by 20%, along with food-feed competition and competition for land use, over six years. The project involves farmers, producer organizations, public institutions, universities, and industrial partners. The company did not simply ask suppliers for a better price: it built farm-specific analyses, measured emissions, involved scientific partners, and linked results to economic incentives for farmers.
This is Total Marketing: shared data, common objectives, incentives, measurable sustainability, and brand reputation arising from real transformation of the supply chain. Milk is no longer only a raw material to be purchased at the lowest possible cost. It becomes the beginning of an ecosystem in which farmers, company, research, institutions, and consumers participate in building broader value. Here, sustainability is designed within the operating model. Technology helps, but it requires a long-term strategic decision that chooses the good of the system as the design criterion. The strength of the example lies here: Total Marketing can increase value for all actors involved when accurate, transparent, and shared data free resources, creativity, and responsibility throughout the supply chain.
Conclusion: The New Promise of Marketing
The original promise of marketing was to create value by better understanding people. In the age of AI, this promise can become more precise, more personalized, more effective, and capable of generating shared and tangible value. It can also become more dangerous if understanding turns into informational predation. For this reason, marketing must accept a broader responsibility. It must help define, measure, distribute, and make value comprehensible. It must connect profit and welfare, data and rights, personalization and freedom, automation and judgment, sustainability and performance. It must become the architecture of shared value in the age of AI.
