Artificial intelligence rewriting marketing and advertising
Iab Italia white paper photographs the transition from testing to industrial use of AI
Artificial intelligence has stopped knocking at the door. It has entered. And now it has sat at the table where campaigns, budgets, content, targets, creativity, reports, even the way a brand will speak tomorrow not only to people, but to machines, are decided. Iab Italia's latest white paper on Artificial Intelligence photographs a clear transition: the time of isolated trials, of small experiments, of 'let's see what happens', is coming to an end. For many operators, 2026 is the year of deployment.
Advertising has known automation for years: bidding, segmentation, attribution, performance measurement. What is new is something else. With generative AI, technology no longer stays behind the scenes. It approaches the desk of the marketer, the creative, the publisher. It writes, analyses, suggests, synthesises, predicts. It reduces the time between data and decision. And above all, it shifts the centre of gravity of marketing: from the old 4Ps - product, price, distribution, promotion - to a living ecosystem of signals, feedback, micro-clusters, customised content and learning systems.
The consumer, in this scheme, is no longer a target. He is a node. He produces data, influences other users, changes paths, enters and exits channels. The linear funnel is transformed into a loop. The brand no longer just launches campaigns: it designs adaptive environments. The product becomes an experience, the price can be dynamic, the point of sale is everywhere, promotion becomes confused with relationship and content.
Within this transformation, the Iab document tries to bring order. The areas are many: insight and trend research, content production, reporting, targeting, profiling, performance, creativity, hyper-personalisation. The red thread is one: AI serves to do it sooner, but it is not enough to do it sooner. It serves to understand better. Data collection becomes almost real-time. Data cleansing goes from days to hours. Trend analysis no longer just searches for keywords, but for meanings. Reports do not just describe tables: they try to tell why a phenomenon happens and what might happen next.
The promise is powerful. A brand can intercept a sudden drop in product sentiment. A retailer can reorganise its catalogue around a nascent trend. A B2B company can anticipate a downturn in demand. But the promise also brings with it the risk of shortcutting. Iab insists on one point: without method, AI produces noise faster. It needs clear objectives, validated sources, human supervision, defined roles, metrics.
